diff --git a/config/config.yaml.tftpl b/config/config.yaml.tftpl
index 4b4d9914..b9d087b9 100644
--- a/config/config.yaml.tftpl
+++ b/config/config.yaml.tftpl
@@ -195,7 +195,7 @@ vertex_ai:
           subnetwork: "default"
           # If you want to use the vpc network defined above, set the following flag to true
           use_private_service_access: false
-          state: '${pipeline_configuration.feature-creation-auto-audience-segmentation.execution.schedule.state}'
+          state: ${pipeline_configuration.feature-creation-auto-audience-segmentation.execution.schedule.state}
         # The `pipeline_parameters` defines the parameters that are going to be used to compile the pipeline.
         # Those values may difer depending on the pipeline type and the pipeline steps being used.
         # Make sure you review the python function the defines the pipeline.
@@ -277,7 +277,7 @@ vertex_ai:
           subnetwork: "default"
           # If you want to use the vpc network defined above, set the following flag to true
           use_private_service_access: false
-          state: '${pipeline_configuration.feature-creation-audience-segmentation.execution.schedule.state}'
+          state: ${pipeline_configuration.feature-creation-audience-segmentation.execution.schedule.state}
         # The `pipeline_parameters` defines the parameters that are going to be used to compile the pipeline.
         # Those values may difer depending on the pipeline type and the pipeline steps being used.
         # Make sure you review the python function the defines the pipeline.
@@ -340,7 +340,7 @@ vertex_ai:
           subnetwork: "default"
           # If you want to use the vpc network defined above, set the following flag to true
           use_private_service_access: false
-          state: '${pipeline_configuration.feature-creation-purchase-propensity.execution.schedule.state}'
+          state: ${pipeline_configuration.feature-creation-purchase-propensity.execution.schedule.state}
         pipeline_parameters:
           project_id: "${project_id}"
           location: "${location}"
@@ -401,7 +401,7 @@ vertex_ai:
           subnetwork: "default"
           # If you want to use the vpc network defined above, set the following flag to true
           use_private_service_access: false
-          state: '${pipeline_configuration.feature-creation-churn-propensity.execution.schedule.state}'
+          state: ${pipeline_configuration.feature-creation-churn-propensity.execution.schedule.state}
         pipeline_parameters:
           project_id: "${project_id}"
           location: "${location}"
@@ -456,7 +456,7 @@ vertex_ai:
           subnetwork: "default"
           # If you want to use the vpc network defined above, set the following flag to true
           use_private_service_access: false
-          state: '${pipeline_configuration.feature-creation-customer-ltv.execution.schedule.state}'
+          state: ${pipeline_configuration.feature-creation-customer-ltv.execution.schedule.state}
         pipeline_parameters:
           project_id: "${project_id}"
           location: "${location}"
@@ -517,7 +517,7 @@ vertex_ai:
           subnetwork: "default"
           # If you want to use the vpc network defined above, set the following flag to true
           use_private_service_access: false
-          state: '${pipeline_configuration.feature-creation-aggregated-value-based-bidding.execution.schedule.state}'
+          state: ${pipeline_configuration.feature-creation-aggregated-value-based-bidding.execution.schedule.state}
         pipeline_parameters:
           project_id: "${project_id}"
           location: "${location}"
@@ -531,6 +531,67 @@ vertex_ai:
         pipeline_parameters_substitutions:
           aggregated_value_based_bidding_training_preparation_procedure_name: "${project_id}.aggregated_vbb.invoke_aggregated_value_based_bidding_training_preparation"
           aggregated_value_based_bidding_explanation_preparation_procedure_name: "${project_id}.aggregated_vbb.invoke_aggregated_value_based_bidding_explanation_preparation"
+
+    # This pipeline contains the configuration parameters for the feature creation pipeline for the lead score propensity model.
+    # To deploy this pipeline to your Google Cloud project:
+    ## 1. Define the pipeline parameters below, following YAML format
+    ## 2. Define the queries and procedures SQL parameters in this file under the `bigquery` section, following YAML format
+    ## 3. Create the queries and procedures SQL files under sql/ folder
+    ## 4. Create the terraform resources uin terraform/feature-store/bigquery-procedures.tf
+    ## 5. Create the terraform resources to compile and schedule the pipeline in terraform/pipelines/pipelines.tf
+    ## 6. Define python function that perform `compilation` and `upload to GCS bucket` are defined in `python/pipelines/compiler.py` and `python/pipelines/uploader.py`.
+    ## 7. Define python function that perform `schedule` of the pipeline is defined in `python/pipelines/scheduler.py`.
+    ## 8. Create the pipeline python function in python/pipelines/feature_engineering_pipelines.py
+    ## 9. Run terraform apply
+    feature-creation-lead-score-propensity: 
+      execution:
+        name: "feature-creation-lead-score-propensity"
+        job_id_prefix: "feature-creation-lead-score-propensity-"
+        experiment_name: "feature-creation-lead-score-propensity"
+        # `type` must be "custom", when we're building Python and/or SQL based pipelines for feature engineering purposes.
+        type: "custom"
+        schedule:
+          cron: "TZ=${time_zone} 0 1 * * *"
+          # Define the maximum number of concurrent pipeline runs.
+          # The default value is 1.
+          max_concurrent_run_count: 1
+          start_time: null
+          end_time: null
+          # The `subnetwork` defines the subnetwork in which the pipeline will be executed.
+          # The default value is "default".
+          # Follow the guide: https://cloud.google.com/vertex-ai/docs/general/vpc-peering
+          subnetwork: "default"
+          # If you want to use the vpc network defined above, set the following flag to true
+          use_private_service_access: false
+          state: ${pipeline_configuration.feature-creation-lead-score-propensity.execution.schedule.state}
+        pipeline_parameters:
+          project_id: "${project_id}"
+          location: "${location}"
+          # The query_lead_score_propensity_label defines the procedure that will be used to invoke the creation of the lead score propensity label feature table.
+          query_lead_score_propensity_label: "
+            CALL `{lead_score_propensity_label_procedure_name}`();"
+          # The query_user_dimensions defines the procedure that will be used to invoke the creation of the user dimensions feature table.
+          query_user_dimensions: "
+            CALL `{user_dimensions_procedure_name}`();"
+          # The query_user_rolling_window_metrics defines the procedure that will be used to invoke the creation of the user rolling window metrics feature table.
+          query_user_rolling_window_metrics: "
+            CALL `{user_rolling_window_metrics_procedure_name}`();"
+          # The query_lead_score_propensity_inference_preparation define the procedure that will be used to invoke the creation of the lead score propensity inference preparation table.
+          query_lead_score_propensity_inference_preparation: "
+            CALL `{lead_score_propensity_inference_preparation_procedure_name}`();"
+          # The query_lead_score_propensity_training_preparation define the procedure that will be used to invoke the creation of the lead score propensity training preparation table.
+          query_lead_score_propensity_training_preparation: "
+            CALL `{lead_score_propensity_training_preparation_procedure_name}`();"
+          timeout: 3600.0
+        pipeline_parameters_substitutions: # Substitutions are applied to the parameters before compilation
+          lead_score_propensity_label_procedure_name: "${project_id}.feature_store.invoke_lead_score_propensity_label"
+          user_dimensions_procedure_name: "${project_id}.feature_store.invoke_user_dimensions"
+          user_rolling_window_metrics_procedure_name: "${project_id}.feature_store.invoke_user_rolling_window_lead_metrics"
+          user_scoped_metrics_procedure_name: "${project_id}.feature_store.invoke_user_scoped_metrics"
+          user_session_event_aggregated_metrics_procedure_name: "${project_id}.feature_store.invoke_user_session_event_aggregated_metrics"
+          date_timezone: "UTC" # used when input_date is None and need to get current date.
+          lead_score_propensity_inference_preparation_procedure_name: "${project_id}.lead_score_propensity.invoke_lead_score_propensity_inference_preparation"
+          lead_score_propensity_training_preparation_procedure_name: "${project_id}.lead_score_propensity.invoke_lead_score_propensity_training_preparation"
     
     # This pipeline contains the configuration parameters for the value based bidding training and inference pipelines.
     # To deploy this pipeline to your Google Cloud project:
@@ -563,7 +624,7 @@ vertex_ai:
           subnetwork: "default"
           # If you want to use the vpc network defined above, set the following flag to true
           use_private_service_access: false
-          state: '${pipeline_configuration.value_based_bidding.training.schedule.state}'
+          state: ${pipeline_configuration.value_based_bidding.training.schedule.state}
         # These are pipeline parameters that will be passed to the pipeline to be recompiled
         pipeline_parameters:
           project: "${project_id}"
@@ -601,10 +662,15 @@ vertex_ai:
           stats_and_example_gen_dataflow_max_num_workers: 10
           evaluation_dataflow_starting_num_workers: 5
           evaluation_dataflow_max_num_workers: 10
-          distill_batch_predict_max_replica_count: 10
-          distill_batch_predict_starting_replica_count: 10
-          evaluation_batch_predict_max_replica_count: 10
-          evaluation_batch_predict_starting_replica_count: 10
+          distill_batch_predict_machine_type: "n1-highmem-8"
+          distill_batch_predict_max_replica_count: 5
+          distill_batch_predict_starting_replica_count: 5
+          evaluation_batch_predict_max_replica_count: 5
+          evaluation_batch_predict_starting_replica_count: 5
+          evaluation_batch_explain_max_replica_count: 5
+          evaluation_batch_explain_starting_replica_count: 5
+          stage_1_num_parallel_trials: 5
+          stage_2_num_parallel_trials: 5
           evaluation_dataflow_disk_size_gb: 30
           stats_and_example_gen_dataflow_disk_size_gb: 30
           transform_dataflow_disk_size_gb: 30
@@ -644,7 +710,7 @@ vertex_ai:
           subnetwork: "default"
           # If you want to use the vpc network defined above, set the following flag to true
           use_private_service_access: false
-          state: '${pipeline_configuration.value_based_bidding.explanation.schedule.state}'
+          state: ${pipeline_configuration.value_based_bidding.explanation.schedule.state}
         pipeline_parameters:
           project: "${project_id}"
           location: "${cloud_region}"
@@ -689,7 +755,7 @@ vertex_ai:
           subnetwork: "default"
           # If you want to use the vpc network defined above, set the following flag to true
           use_private_service_access: false
-          state: '${pipeline_configuration.purchase_propensity.training.schedule.state}'
+          state: ${pipeline_configuration.purchase_propensity.training.schedule.state}
         # These are pipeline parameters that will be passed to the pipeline to be recompiled
         pipeline_parameters:
           project: "${project_id}"
@@ -735,10 +801,15 @@ vertex_ai:
           stats_and_example_gen_dataflow_max_num_workers: 10
           evaluation_dataflow_starting_num_workers: 5
           evaluation_dataflow_max_num_workers: 10
-          distill_batch_predict_max_replica_count: 10
-          distill_batch_predict_starting_replica_count: 10
-          evaluation_batch_predict_max_replica_count: 10
-          evaluation_batch_predict_starting_replica_count: 10
+          distill_batch_predict_machine_type: "n1-highmem-8"
+          distill_batch_predict_max_replica_count: 5
+          distill_batch_predict_starting_replica_count: 5
+          evaluation_batch_predict_max_replica_count: 5
+          evaluation_batch_predict_starting_replica_count: 5
+          evaluation_batch_explain_max_replica_count: 5
+          evaluation_batch_explain_starting_replica_count: 5
+          stage_1_num_parallel_trials: 5
+          stage_2_num_parallel_trials: 5
           evaluation_dataflow_disk_size_gb: 30
           stats_and_example_gen_dataflow_disk_size_gb: 30
           transform_dataflow_disk_size_gb: 30
@@ -790,7 +861,7 @@ vertex_ai:
           subnetwork: "default"
           # If you want to use the vpc network defined above, set the following flag to true
           use_private_service_access: false
-          state: '${pipeline_configuration.purchase_propensity.prediction.schedule.state}'
+          state: ${pipeline_configuration.purchase_propensity.prediction.schedule.state}
         pipeline_parameters:
           project_id: "${project_id}"
           location: "${cloud_region}"
@@ -850,10 +921,10 @@ vertex_ai:
           # Follow the guide: https://cloud.google.com/vertex-ai/docs/general/vpc-peering
           subnetwork: "default"
           # If you want to use the vpc network defined above, set the following flag to true
-          use_private_service_access: false   
+          use_private_service_access: false
           # The `state` defines the state of the pipeline.
           # In case you don't want to schedule the pipeline, set the state to `PAUSED`.
-          state: '${pipeline_configuration.churn_propensity.training.schedule.state}'
+          state: ${pipeline_configuration.churn_propensity.training.schedule.state}
         # These are pipeline parameters that will be passed to the pipeline to be recompiled
         pipeline_parameters:
           project: "${project_id}"
@@ -899,10 +970,17 @@ vertex_ai:
           stats_and_example_gen_dataflow_max_num_workers: 10
           evaluation_dataflow_starting_num_workers: 5
           evaluation_dataflow_max_num_workers: 10
-          distill_batch_predict_max_replica_count: 10
-          distill_batch_predict_starting_replica_count: 10
-          evaluation_batch_predict_max_replica_count: 10
-          evaluation_batch_predict_starting_replica_count: 10
+          distill_batch_predict_machine_type: "n1-highmem-8"
+          distill_batch_predict_max_replica_count: 5
+          distill_batch_predict_starting_replica_count: 5
+          evaluation_batch_predict_max_replica_count: 5
+          evaluation_batch_predict_starting_replica_count: 5
+          evaluation_batch_explain_max_replica_count: 5
+          evaluation_batch_explain_starting_replica_count: 5
+          evaluation_batch_explain_max_replica_count: 5
+          evaluation_batch_explain_starting_replica_count: 5
+          stage_1_num_parallel_trials: 5
+          stage_2_num_parallel_trials: 5
           evaluation_dataflow_disk_size_gb: 30
           stats_and_example_gen_dataflow_disk_size_gb: 30
           transform_dataflow_disk_size_gb: 30
@@ -956,7 +1034,7 @@ vertex_ai:
           use_private_service_access: false
           # The `state` defines the state of the pipeline.
           # In case you don't want to schedule the pipeline, set the state to `PAUSED`.
-          state: PAUSED # possible states ACTIVE or PAUSED
+          state: ${pipeline_configuration.churn_propensity.prediction.schedule.state}
         pipeline_parameters:
           project_id: "${project_id}"
           location: "${cloud_region}"
@@ -1019,7 +1097,7 @@ vertex_ai:
           use_private_service_access: false
           # The `state` defines the state of the pipeline.
           # In case you don't want to schedule the pipeline, set the state to `PAUSED`.
-          state: PAUSED # possible states ACTIVE or PAUSED
+          state: ${pipeline_configuration.segmentation.training.schedule.state}
         # These are pipeline parameters that will be passed to the pipeline to be compiled
         # For Demographics Audience Segmentation model, we use the BQML KMeans clustering algorithm.
         # Check the official documentation for better understanding the algorithm 
@@ -1070,7 +1148,7 @@ vertex_ai:
           use_private_service_access: false
           # The `state` defines the state of the pipeline.
           # In case you don't want to schedule the pipeline, set the state to `PAUSED`.
-          state: PAUSED # possible states ACTIVE or PAUSED
+          state: ${pipeline_configuration.segmentation.prediction.schedule.state}
         pipeline_parameters:
           project_id: "${project_id}"
           location: "${location}"
@@ -1124,7 +1202,7 @@ vertex_ai:
           use_private_service_access: false
           # The `state` defines the state of the pipeline.
           # In case you don't want to schedule the pipeline, set the state to `PAUSED`.
-          state: PAUSED # possible states ACTIVE or PAUSED
+          state: ${pipeline_configuration.auto_segmentation.training.schedule.state}
         # These are pipeline parameters that will be passed to the pipeline to be compiled
         # For Interest based Auto Audience Segmentation model, we use the BQML KMeans clustering algorithm.
         # Check the official documentation for better understanding the algorithm 
@@ -1174,7 +1252,7 @@ vertex_ai:
           use_private_service_access: false
           # The `state` defines the state of the pipeline.
           # In case you don't want to schedule the pipeline, set the state to `PAUSED`.
-          state: PAUSED # possible states ACTIVE or PAUSED
+          state: ${pipeline_configuration.auto_segmentation.prediction.schedule.state}
         pipeline_parameters:
           project_id: "${project_id}"
           location: "${location}"
@@ -1231,7 +1309,7 @@ vertex_ai:
           use_private_service_access: false
           # The `state` defines the state of the pipeline.
           # In case you don't want to schedule the pipeline, set the state to `PAUSED`.
-          state: PAUSED # possible states ACTIVE or PAUSED
+          state: ${pipeline_configuration.propensity_clv.training.schedule.state}
           # These are pipeline parameters that will be passed to the pipeline to be recompiled
         pipeline_parameters:
           project: "${project_id}"
@@ -1276,10 +1354,15 @@ vertex_ai:
           stats_and_example_gen_dataflow_max_num_workers: 10
           evaluation_dataflow_starting_num_workers: 5
           evaluation_dataflow_max_num_workers: 10
-          distill_batch_predict_max_replica_count: 10
-          distill_batch_predict_starting_replica_count: 10
-          evaluation_batch_predict_max_replica_count: 10
-          evaluation_batch_predict_starting_replica_count: 10
+          distill_batch_predict_machine_type: "n1-highmem-8"
+          distill_batch_predict_max_replica_count: 5
+          distill_batch_predict_starting_replica_count: 5
+          evaluation_batch_predict_max_replica_count: 5
+          evaluation_batch_predict_starting_replica_count: 5
+          evaluation_batch_explain_max_replica_count: 5
+          evaluation_batch_explain_starting_replica_count: 5
+          stage_1_num_parallel_trials: 5
+          stage_2_num_parallel_trials: 5
           evaluation_dataflow_disk_size_gb: 30
           stats_and_example_gen_dataflow_disk_size_gb: 30
           transform_dataflow_disk_size_gb: 30
@@ -1350,7 +1433,7 @@ vertex_ai:
           use_private_service_access: false
           # The `state` defines the state of the pipeline.
           # In case you don't want to schedule the pipeline, set the state to `PAUSED`.
-          state: PAUSED # possible states ACTIVE or PAUSED
+          state: ${pipeline_configuration.clv.training.schedule.state}
         # These are pipeline parameters that will be passed to the pipeline to be recompiled
         pipeline_parameters:
           project: "${project_id}"
@@ -1392,10 +1475,15 @@ vertex_ai:
           stats_and_example_gen_dataflow_max_num_workers: 10
           evaluation_dataflow_starting_num_workers: 5
           evaluation_dataflow_max_num_workers: 10
-          distill_batch_predict_max_replica_count: 10
-          distill_batch_predict_starting_replica_count: 10
-          evaluation_batch_predict_max_replica_count: 10
-          evaluation_batch_predict_starting_replica_count: 10
+          distill_batch_predict_machine_type: "n1-highmem-8"
+          distill_batch_predict_max_replica_count: 5
+          distill_batch_predict_starting_replica_count: 5
+          evaluation_batch_predict_max_replica_count: 5
+          evaluation_batch_predict_starting_replica_count: 5
+          evaluation_batch_explain_max_replica_count: 5
+          evaluation_batch_explain_starting_replica_count: 5
+          stage_1_num_parallel_trials: 5
+          stage_2_num_parallel_trials: 5
           evaluation_dataflow_disk_size_gb: 30
           stats_and_example_gen_dataflow_disk_size_gb: 30
           transform_dataflow_disk_size_gb: 30
@@ -1446,7 +1534,7 @@ vertex_ai:
           use_private_service_access: false
           # The `state` defines the state of the pipeline.
           # In case you don't want to schedule the pipeline, set the state to `PAUSED`.
-          state: PAUSED # possible states ACTIVE or PAUSED
+          state: ${pipeline_configuration.clv.prediction.schedule.state}
         # These are the pipeline parameters to be used in this convoluted prediction pipeline that takes predictions from LTV model and purchase propensity model.
         pipeline_parameters:
           project_id: "${project_id}"
@@ -1491,6 +1579,173 @@ vertex_ai:
           pubsub_activation_type: "cltv-180-30" # cltv-180-180 | cltv-180-90 | cltv-180-30
         pipeline_parameters_substitutions: null
     
+    # This pipeline contains the configuration parameters for the propensity training and inference pipelines for the lead score propensity model.
+    # To deploy this pipeline to your Google Cloud project:
+    ## 1. Define the pipeline parameters below, following YAML format
+    ## 2. Define the bigquery or Vertex AI KFP components to be used by your pipeline in `python/pipelines` section, if applicable.
+    ## 3. Define or reuse the pipeline definition method to be used to compile the pipeline into a YAML file in `python/pipelines` section.
+    ## 5. Create the terraform resources to compile, upload and schedule the pipeline in `terraform/pipelines/pipelines.tf`
+    ## 6. Define python function that perform `compilation` and `upload to GCS bucket` are defined in `python/pipelines/compiler.py` and `python/pipelines/uploader.py`.
+    ## 7. Define python function that perform `schedule` of the pipeline is defined in `python/pipelines/scheduler.py`.
+    ## 9. Run terraform apply
+    ## Note: For `type` = "tabular workflows", the pre-compiled YAML file `automl_tabular_pl_v4.yaml` is recompiled by parsing the `pipeline_parameters` below as default values
+    ## to the the new YAML file. The recompiled YAML will be uploaded and scheduled in Vertex AI Pipelines.
+    lead_score_propensity:
+      training:
+        name: "lead-score-propensity-training-pl"
+        job_id_prefix: "lead-score-propensity-training-pl-"
+        experiment_name: "lead-score-propensity-training"
+        # `type` can be "custom" or "tabular-workflows". 
+        #  For using Vertex AI Tabular Workflow use the later, for all other modeling approaches use "custom" (i.e. BQML, Scikit-learn).
+        type: "tabular-workflows"
+        schedule:
+          cron: "TZ=${time_zone} 0 8 * * SAT"
+          max_concurrent_run_count: 1
+          start_time: null
+          end_time: null
+          # The `subnetwork` defines the subnetwork in which the pipeline will be executed.
+          # The default value is "default".
+          # Follow the guide: https://cloud.google.com/vertex-ai/docs/general/vpc-peering
+          subnetwork: "default"
+          # If you want to use the vpc network defined above, set the following flag to true
+          use_private_service_access: false
+          state: ${pipeline_configuration.lead_score_propensity.training.schedule.state}
+        # These are pipeline parameters that will be passed to the pipeline to be recompiled
+        pipeline_parameters:
+          project: "${project_id}"
+          location: "${cloud_region}"
+          root_dir: "gs://${project_id}-pipelines/lead-score-propensity-training"
+          transformations: "gs://${project_id}-pipelines/lead-score-propensity-training/transformations_config_{timestamp}.json"
+          # These are specific data types transformations that will be applied to the dataset.
+          custom_transformations: "pipelines/transformations-lead-score-propensity.json"
+          train_budget_milli_node_hours: 100 # 1000 = 1 hour 
+          # Set these to apply feature selection tuning.
+          max_selected_features: 20
+          apply_feature_selection_tuning: true
+          run_evaluation: true
+          run_distillation: false
+          # The Lead Score Propensity model name
+          model_display_name: "lead-score-propensity-model" 
+          # The Lead Score Propensity model description
+          model_description: "Lead Score Propensity Classification AutoML Model"
+          # Use `prediction_type` to "regression" for training models that predict a numerical value.  For classification models, use "classification" and you will
+          # also get the probability likelihood for that class.
+          prediction_type: "classification"
+          # The optimization objectives may change depending on the `prediction_type`.
+          # For binary classification, use "maximize-au-roc", "minimize-log-loss", "maximize-au-prc", "maximize-precision-at-recall" or "maximize-recall-at-precision". 
+          # For multi class classification, use "minimize-log-loss". 
+          # For regression, use "minimize-rmse", "minimize-mae", or "minimize-rmsle".
+          optimization_objective: "maximize-au-roc" # maximize-precision-at-recall, maximize-au-prc, maximize-au-roc, minimize-log-loss, maximize-recall-at-precision
+          #Don't use when parameter `optimization_objective` is not `maximize-precision-at-recall` or `maximize-recall-at-precision`
+          #optimization_objective_recall_value: 0.72
+          #optimization_objective_precision_value: 0.72
+          target_column: "will_login"
+          predefined_split_key: "data_split"
+          data_source_csv_filenames: null
+          training_fraction: null
+          validation_fraction: null
+          test_fraction: null
+          # This is the training dataset provided during the training routine.
+          # The schema in this table or view must match the schema in the json files.
+          # Take into consideration the `excluded_features` list below. They won't be used for training.
+          data_source_bigquery_table_path: "bq://${project_id}.lead_score_propensity.v_lead_score_propensity_training_5_1_last_window"
+          data_source_bigquery_table_schema: "../sql/schema/table/lead_score_propensity_training_preparation.json"
+          dataflow_service_account: "df-worker@${project_id}.iam.gserviceaccount.com"
+          transform_dataflow_max_num_workers: 10
+          stats_and_example_gen_dataflow_max_num_workers: 10
+          evaluation_dataflow_starting_num_workers: 5
+          evaluation_dataflow_max_num_workers: 10
+          distill_batch_predict_machine_type: "n1-highmem-8"
+          distill_batch_predict_max_replica_count: 5
+          distill_batch_predict_starting_replica_count: 5
+          evaluation_batch_predict_max_replica_count: 5
+          evaluation_batch_predict_starting_replica_count: 5
+          evaluation_batch_explain_max_replica_count: 5
+          evaluation_batch_explain_starting_replica_count: 5
+          stage_1_num_parallel_trials: 5
+          stage_2_num_parallel_trials: 5
+          evaluation_dataflow_disk_size_gb: 30
+          stats_and_example_gen_dataflow_disk_size_gb: 30
+          transform_dataflow_disk_size_gb: 30
+          timestamp_split_key: null
+          stratified_split_key: null
+          weight_column: null
+          additional_experiments: null
+          export_additional_model_without_custom_ops: false
+          # Override the study spec parameters in case you want to restrict hyperparameter search space. Including `model_type`.
+          # In this case, for Value Based Bidding, we're looking for a perfect fit using a tree based model.
+          # Don't use when parameter `apply_feature_selection_tuning` is `true`
+          #study_spec_parameters_override:
+          #  - parameter_id: "model_type"
+          #    categorical_value_spec:
+          #      values: 
+          #        - nn
+          #        - boosted_trees
+          #  - parameter_id: "feature_selection_rate"
+          #    double_value_spec:
+          #      min_value: 0.5
+          #      max_value: 1.0
+          #    scale_type: UNIT_LINEAR_SCALE
+        # Features to be excluded from the training dataset.
+        exclude_features:
+          - processed_timestamp
+          - data_split
+          #- feature_date
+          - user_pseudo_id
+          - user_id
+          - device_web_browser_version
+          - device_os_version
+          - will_login
+        pipeline_parameters_substitutions: null 
+      prediction:
+        name: "lead-score-propensity-prediction-pl"
+        job_id_prefix: "lead-score-propensity-prediction-pl-"
+        experiment_name: "lead-score-propensity-prediction"
+        # `type` can be "custom" or "tabular-workflows". 
+        #  For using Vertex AI Tabular Workflow use the later, for all other modeling approaches use "custom" (i.e. BQML, Scikit-learn).
+        type: "custom"
+        schedule:
+          cron: "TZ=${time_zone} 0 5 * * *"
+          max_concurrent_run_count: 1
+          start_time: null
+          end_time: null
+          # The `subnetwork` defines the subnetwork in which the pipeline will be executed.
+          # The default value is "default".
+          # Follow the guide: https://cloud.google.com/vertex-ai/docs/general/vpc-peering
+          subnetwork: "default"
+          # If you want to use the vpc network defined above, set the following flag to true
+          use_private_service_access: false
+          state: ${pipeline_configuration.lead_score_propensity.prediction.schedule.state}
+        pipeline_parameters:
+          project_id: "${project_id}"
+          location: "${cloud_region}"
+          job_name_prefix: "lead-score-propensity-prediction-pl-"
+          # The Lead Score Propensity model name to be used for prediction
+          model_display_name: "lead-score-propensity-model" 
+          model_metric_name: "logLoss"
+          # The `model_metric_threshold` parameter defines what is the maximum acceptable value for the `model_metric_name` so that the model can be selected.
+          # If the actual models metrics values are higher than this limit, no models will be selected and the pipeline is going to fail.
+          model_metric_threshold: 0.9
+          number_of_models_considered: 1
+          # This is the prediction dataset table or view.
+          bigquery_source: "${project_id}.lead_score_propensity.v_lead_score_propensity_inference_5_1"
+          bigquery_destination_prefix: "${project_id}.lead_score_propensity"
+          bq_unique_key: "user_pseudo_id"
+          machine_type: "n1-standard-4"
+          max_replica_count: 10
+          batch_size: 64
+          accelerator_count: 0
+          accelerator_type: "ACCELERATOR_TYPE_UNSPECIFIED" # ONE OF ACCELERATOR_TYPE_UNSPECIFIED, NVIDIA_TESLA_K80, NVIDIA_TESLA_P100, NVIDIA_TESLA_V100, NVIDIA_TESLA_P4, NVIDIA_TESLA_T4
+          generate_explanation: false
+          # This is the probability value that will tell the condition to slit into the two classes.
+          # For probabilities higher than `threashold`, set postive label to 1, otherwise 0.
+          threashold: 0.5
+          positive_label: "1"
+          # THese are parameters to trigger the Activation Application Dataflow.
+          pubsub_activation_topic: "activation-trigger"
+          pubsub_activation_type: "lead-score-propensity-5-1"  # lead-score-propensity-5-1
+        pipeline_parameters_substitutions: null
+
     # This pipeline contains the configuration parameters for the feature creation pipeline for the audience segmentation model.
     # This block defines the pipeline parameters that are going to be used for three tasks: compilation, upload and scheduling.
     # To deploy this pipeline to your Google Cloud project:
@@ -1533,7 +1788,7 @@ vertex_ai:
           use_private_service_access: false
           # The `state` defines the state of the pipeline.
           # In case you don't want to schedule the pipeline, set the state to `PAUSED`.
-          state: PAUSED # possible states ACTIVE or PAUSED
+          state: ACTIVE # possible states ACTIVE or PAUSED
         # The `pipeline_parameters` defines the parameters that are going to be used to compile the pipeline.
         # Those values may difer depending on the pipeline type and the pipeline steps being used.
         # Make sure you review the python function the defines the pipeline.
@@ -1597,7 +1852,7 @@ vertex_ai:
           use_private_service_access: false
           # The `state` defines the state of the pipeline.
           # In case you don't want to schedule the pipeline, set the state to `PAUSED`.
-          state: PAUSED # possible states ACTIVE or PAUSED
+          state: ACTIVE # possible states ACTIVE or PAUSED
         # The `pipeline_parameters` defines the parameters that are going to be used to compile the pipeline.
         # Those values may difer depending on the pipeline type and the pipeline steps being used.
         # Make sure you review the python function the defines the pipeline.
@@ -1726,6 +1981,18 @@ bigquery:
       description: "Dataset with gemini_insights results from multiple use cases"
       friendly_name: "Gemini Insights Dataset"
       max_time_travel_hours: 168
+    # Dataset for the lead score propensity use case.
+    lead_score_propensity:
+      name: "lead_score_propensity"
+      location: "${location}"
+      project_id: "${project_id}"
+      collation: "und:ci"
+      is_case_insensitive: TRUE
+      description: "Lead Score Propensity Use Case dataset for Marketing behavioural modeling"
+      friendly_name: "Lead Score Propensity Dataset"
+      max_time_travel_hours: 168
+      default_partition_expiration_days: 365
+      default_table_expiration_days: 365
   table:
     # Table containing the feature engineered dataset that will be used for the Audience Segmentation prediction pipeline.
     audience_segmentation_inference_preparation:
@@ -1762,6 +2029,13 @@ bigquery:
       table_name: "churn_propensity_inference_preparation"
       location: "${location}"
       table_description: "Purchase Propensity Inference Preparation table to be used for Model Prediction"
+    # Table containing the feature engineered dataset that will be used for the Lead Score Propensity prediction pipeline.
+    lead_score_propensity_inference_preparation:
+      project_id: "${project_id}"
+      dataset: "lead_score_propensity"
+      table_name: "lead_score_propensity_inference_preparation"
+      location: "${location}"
+      table_description: "Lead Score Propensity Inference Preparation table to be used for Model Prediction"
     # Table containing the feature engineered labels that will be used for the Purchase Propensity training pipeline.
     purchase_propensity_label:
       project_id: "${project_id}"
@@ -1769,13 +2043,20 @@ bigquery:
       table_name: "purchase_propensity_label"
       location: "${location}"
       table_description: "Purchase Propensity Label table to be used for Model Prediction"
-    # Table containing the feature engineered labels that will be used for the Purchase Propensity training pipeline.
+    # Table containing the feature engineered labels that will be used for the Churn Propensity training pipeline.
     churn_propensity_label:
       project_id: "${project_id}"
       dataset: "feature_store"
       table_name: "churn_propensity_label"
       location: "${location}"
       table_description: "Churn Propensity Label table to be used for Model Prediction"
+    # Table containing the feature engineered labels that will be used for the Purchase Propensity training pipeline.
+    lead_score_propensity_label:
+      project_id: "${project_id}"
+      dataset: "feature_store"
+      table_name: "lead_score_propensity_label"
+      location: "${location}"
+      table_description: "Lead Score Propensity Label table to be used for Model Prediction"
     # Table containing the feature engineered dimensions that will be used for the Purchase Propensity training and inference pipeline.
     user_dimensions:
       project_id: "${project_id}"
@@ -1811,6 +2092,13 @@ bigquery:
       table_name: "user_rolling_window_metrics"
       location: "${location}"
       table_description: "User Rolling Window Metrics table as part of the Feature Store for Purchase Propensity use case"
+    # Table containing the featured engineered rolling window metrics that will be used for the Purchase Propensity training and inference pipeline.
+    user_rolling_window_lead_metrics:
+      project_id: "${project_id}"
+      dataset: "feature_store"
+      table_name: "user_rolling_window_lead_metrics"
+      location: "${location}"
+      table_description: "User Rolling Window Lead Metrics table as part of the Feature Store for Lead Score Propensity use case"
     # Table containing the feature engineered all users metrics that will be used for the Customer Lifetime Value training and inference pipeline.
     user_scoped_lifetime_metrics:
       project_id: "${project_id}"
@@ -1860,6 +2148,9 @@ bigquery:
     churn_propensity_query_template:
       none: none
     # This is a query template to be used by the Activation application, so there is no configuration to be applied.
+    lead_score_propensity_query_template:
+      none: none
+    # This is a query template to be used by the Activation application, so there is no configuration to be applied.
     cltv_query_template:
       none: none
     create_gemini_model:
@@ -1875,6 +2166,25 @@ bigquery:
       project_id: "${project_id}"
       dataset: "aggregated_vbb"
       stored_procedure: "aggregated_value_based_bidding_training_preparation"
+    # This is a stored procedure that CALLs the Lead Score Propensity Training Preparation stored procedure.
+    invoke_lead_score_propensity_training_preparation:
+      project_id: "${project_id}"
+      dataset: "lead_score_propensity"
+      stored_procedure: "lead_score_propensity_training_preparation"
+      # The `interval_max_date` parameter defines hwo many days we leave out of the training dataset after the latest date in the dataset.
+      # This is usually the same value as the look forward window.
+      interval_max_date: 1
+      # The `interval_min_date` parameter defines how many days we leave out of the training dataset before the first date in the dataset.
+      # This is usually the same value as the lookback window.
+      interval_min_date: 5
+      # `training_split_end_number` must be smaller then `validation_split_end_number`.
+      # This is a number out of 10 deciles, how many rows will belong to the `data_split` = TRAIN (Between 1 and `training_split_end_number`)
+      train_split_end_number: 5
+      # This is a number out of 10 deciles, how many rows will belong to the `data_split` = VALIDATE (Between `training_split_end_number` and `validation_split_end_number`)
+      # The rest of the rows will belong to the `data_split` = TEST (Between `validation_split_end_number` and 10)
+      validation_split_end_number: 8
+      mds_project_id: "${mds_project_id}"
+      mds_dataset: "${mds_dataset}"
     # This is a stored procedure that CALLs the Purchase Propensity Training Preparation stored procedure.
     invoke_purchase_propensity_training_preparation:
       project_id: "${project_id}"
@@ -1894,7 +2204,7 @@ bigquery:
       validation_split_end_number: 8
       mds_project_id: "${mds_project_id}"
       mds_dataset: "${mds_dataset}"
-    # This is a stored procedure that CALLs the Purchase Propensity Training Preparation stored procedure.
+    # This is a stored procedure that CALLs the Churn Propensity Training Preparation stored procedure.
     invoke_churn_propensity_training_preparation:
       project_id: "${project_id}"
       dataset: "churn_propensity"
@@ -1962,6 +2272,11 @@ bigquery:
       stored_procedure: "auto_audience_segmentation_training_preparation"
       # The `lookback_days` parameter is the number of days to look back for training data.
       lookback_days: 15
+    # This is a stored procedure that CALLs the Lead Score Propensity Inference Inference Preparation stored procedure.
+    invoke_lead_score_propensity_inference_preparation:
+      project_id: "${project_id}"
+      dataset: "lead_score_propensity"
+      stored_procedure: "lead_score_propensity_inference_preparation"
     # This is a stored procedure that CALLs the Purchase Propensity Inference Inference Preparation stored procedure.
     invoke_purchase_propensity_inference_preparation:
       project_id: "${project_id}"
@@ -2085,6 +2400,22 @@ bigquery:
       # The `interval_end_date` parameter defines how many days we leave out of the backfill before the last dates of events.
       # This is usually the same value as the look forward window.
       interval_end_date: 180
+    # This is a stored procedure that CALLs the Lead Store Propensity Label Backfill stored procedure.
+    invoke_backfill_lead_score_propensity_label:
+      mds_project_id: "${mds_project_id}"
+      mds_dataset: "${mds_dataset}"
+      project_id: "${project_id}"
+      dataset: "feature_store"
+      insert_table: "lead_score_propensity_label"
+      # The `interval_max_date` parameter defines hwo many days we leave out of the training dataset after the latest date in the dataset.
+      # This is usually the same value as the look forward window.
+      interval_max_date: 1
+      # The `interval_min_date` parameter defines how many days we leave out of the training dataset before the first date in the dataset.
+      # This is usually the same value as the lookback window.
+      interval_min_date: 5
+      # The `interval_end_date` parameter defines how many days we leave out of the backfill before the last dates of events.
+      # This is usually the same value as the look forward window.
+      interval_end_date: 5
     # This is a stored procedure that CALLs the Purchase Propensity Label Backfill stored procedure.
     invoke_backfill_purchase_propensity_label:
       mds_project_id: "${mds_project_id}"
@@ -2154,6 +2485,22 @@ bigquery:
       # The `interval_end_date` parameter defines how many days we leave out of the backfill before the last dates of events.
       # This is usually the same value as the look forward window.
       interval_end_date: 30
+    # This is a stored procedure that CALLs the User Rolling Window Lead Metrics Backfill stored procedure.
+    invoke_backfill_user_rolling_window_lead_metrics:
+      mds_project_id: "${mds_project_id}"
+      mds_dataset: "${mds_dataset}" 
+      project_id: "${project_id}"
+      dataset: "feature_store"
+      insert_table: "user_rolling_window_lead_metrics"
+      # The `interval_max_date` parameter defines hwo many days we leave out of the training dataset after the latest date in the dataset.
+      # This is usually the same value as the look forward window.
+      interval_max_date: 1
+      # The `interval_min_date` parameter defines how many days we leave out of the training dataset before the first date in the dataset.
+      # This is usually the same value as the lookback window.
+      interval_min_date: 5
+      # The `interval_end_date` parameter defines how many days we leave out of the backfill before the last dates of events.
+      # This is usually the same value as the look forward window.
+      interval_end_date: 5
     # This is a stored procedure that CALLs the User Scoped Metrics Backfill stored procedure.
     invoke_backfill_user_scoped_metrics:
       mds_project_id: "${mds_project_id}"
@@ -2219,6 +2566,16 @@ bigquery:
       # The `interval_end_date` parameter defines how many days we leave out of the backfill before the last dates of events.
       # This is usually the same value as the look forward window.
       interval_input_date: 180
+    # This is a stored procedure that CALLs the Lead Score Propensity Label stored procedure.
+    invoke_lead_score_propensity_label:
+      mds_project_id: "${mds_project_id}"
+      mds_dataset: "${mds_dataset}"
+      project_id: "${project_id}"
+      dataset: "feature_store"
+      stored_procedure: "lead_score_propensity_label"
+      # The `interval_end_date` parameter defines how many days we leave out of the backfill before the last dates of events.
+      # This is usually the same value as the look forward window.
+      interval_input_date: 1
     # This is a stored procedure that CALLs the Purchase Propensity Label stored procedure.
     invoke_purchase_propensity_label:
       mds_project_id: "${mds_project_id}"
@@ -2311,6 +2668,14 @@ bigquery:
       # The `interval_end_date` parameter defines how many days we leave out of the backfill before the last dates of events.
       # This is usually the same value as the look forward window.
       interval_end_date: 180
+    # This is a stored procedure that CALLs the User Rolling Window Metrics stored procedure.
+    invoke_user_rolling_window_lead_metrics:
+      project_id: "${project_id}"
+      dataset: "feature_store"
+      stored_procedure: "user_rolling_window_lead_metrics"
+      # The `interval_end_date` parameter defines how many days we leave out of the backfill before the last dates of events.
+      # This is usually the same value as the look forward window.
+      interval_end_date: 15
     # This is a stored procedure that CALLs the User Scoped Metrics stored procedure.
     invoke_user_scoped_metrics:
       project_id: "${project_id}"
@@ -2322,10 +2687,11 @@ bigquery:
   # This section sets the parameters for the features, training and inference procedures that insert data into tables and views to be used for
   # training and prediction.
   # There is no strict recommendation on the right parameters that will maximize the models performance, however here are some back of the envelope numbers.
-  #      Purchase Propensity model: 1 month-2 years for dates interval. From Xk - 10M users.
-  #      Customer LTV model: 6 months-2 years for dates interval. From Xk - 10M users.
-  #      Audience Segmentation / Auto Audience Segmentation models: 1 month-1 year for dates interval. From XXX - 10M users.
-  #      Aggregated VBB model: 1000 days - 2000 days 
+  #      Lead Score Propensity model: 2 weeks-1 year for dates interval. From Xk - 1M users.
+  #      Purchase Propensity model: 1 month-2 years for dates interval. From Xk - 1M users.
+  #      Customer LTV model: 6 months-2 years for dates interval. From Xk - 1M users.
+  #      Audience Segmentation / Auto Audience Segmentation models: 1 month-1 year for dates interval. From XXX - 1M users.
+  #      Aggregated VBB model: 100 days - 2000 days 
   #         Note: For Aggregated VBB, it's common to duplicate rows to that training dataset size reaches at least 1k rows for AutoML to train a model.
   #               If that is your case, this is not a problem since typically duplicated rows has a similar effect as of training the model for more epochs.
   procedure:
@@ -2413,6 +2779,17 @@ bigquery:
       expiration_duration_hours: 168
       custom_start_date: "'2023-01-01'"
       custom_end_date: "NULL"
+    # This is the stored procedure that calculates the label column for the Lead Score Propensity use case.
+    # The label represents wether a user will make a lead score over a period of time.
+    # Typically, looking at a period of 1 day in the future.
+    # The granularity level is per user per day.
+    lead_score_propensity_label:
+      project_id: "${project_id}"
+      dataset: "feature_store"
+      name: "lead_score_propensity_label"
+      insert_table: "lead_score_propensity_label"
+      mds_project_id: "${mds_project_id}"
+      mds_dataset: "${mds_dataset}"
     # This is the stored procedure that calculates the label column for the Purchase Propensity use case.
     # The label represents wether a user will make a purchase over a period of time.
     # Typically, looking at a period of 15 to 30 days in the future.
@@ -2425,7 +2802,7 @@ bigquery:
       mds_project_id: "${mds_project_id}"
       mds_dataset: "${mds_dataset}"
     # This is the stored procedure that calculates the label column for the Churn Propensity use case.
-    # The label represents wether a user will make a purchase over a period of time.
+    # The label represents wether a user will churn over a period of time.
     # Typically, looking at a period of 30 days in the future.
     # The granularity level is per user per day.
     churn_propensity_label:
@@ -2444,6 +2821,22 @@ bigquery:
     # The procedure will split the data into three splits (TRAIN, VALIDATE, TEST) and will take care of avoiding splits contamination.
     # There is a minimum number of examples rows of 1000 and the maximum is as much as it fits in memory, overall consensus is that for ML models
     # you will provide at maximum a couple of millions of rows.
+    lead_score_propensity_training_preparation:
+      project_id: "${project_id}"
+      dataset: "lead_score_propensity"
+      name: "lead_score_propensity_training_preparation"
+      insert_table: "lead_score_propensity_training_full_dataset"
+      feature_store_project_id: "${project_id}"
+      feature_store_dataset: "feature_store"
+      mds_project_id: "${mds_project_id}"
+      mds_dataset: "${mds_dataset}"
+      expiration_duration_hours: 168
+      custom_start_date: "'2024-01-01'"
+      custom_end_date: "NULL"
+    # This is the stored procedure that collects the features and prepare the examples rows to train a model.
+    # The procedure will split the data into three splits (TRAIN, VALIDATE, TEST) and will take care of avoiding splits contamination.
+    # There is a minimum number of examples rows of 1000 and the maximum is as much as it fits in memory, overall consensus is that for ML models
+    # you will provide at maximum a couple of millions of rows.
     purchase_propensity_training_preparation:
       project_id: "${project_id}"
       dataset: "purchase_propensity"
@@ -2454,7 +2847,7 @@ bigquery:
       mds_project_id: "${mds_project_id}"
       mds_dataset: "${mds_dataset}"
       expiration_duration_hours: 168
-      custom_start_date: "'2023-01-01'"
+      custom_start_date: "'2024-01-01'"
       custom_end_date: "NULL"
     # This is the stored procedure that collects the features and prepare the examples rows to train a model.
     # The procedure will split the data into three splits (TRAIN, VALIDATE, TEST) and will take care of avoiding splits contamination.
@@ -2524,6 +2917,16 @@ bigquery:
       insert_table: "user_rolling_window_metrics"
       mds_project_id: "${mds_project_id}"
       mds_dataset: "${mds_dataset}"
+    # This is the stored procedure that UPSERTs new look back rolling windows metrics rows daily.
+    # The granularity level is per user per day.
+    # These metrics are used for the Lead Score Propensity use case.
+    user_rolling_window_lead_metrics:
+      project_id: "${project_id}"
+      dataset: "feature_store"
+      name: "user_rolling_window_lead_metrics"
+      insert_table: "user_rolling_window_lead_metrics"
+      mds_project_id: "${mds_project_id}"
+      mds_dataset: "${mds_dataset}"
     # This is the stored procedure that UPSERTs new aggregated users metrics rows daily.
     # The granularity level is per day, whereas the calculations take into consideration all users.
     # These metrics are used for the Customer Lifetime Value use case.
@@ -2575,6 +2978,16 @@ bigquery:
       mds_project_id: "${mds_project_id}"
       mds_dataset: "${mds_dataset}"
     # This is the stored procedure that collects the features and prepare the examples rows for daily prediction.
+    lead_score_propensity_inference_preparation:
+      project_id: "${project_id}"
+      mds_dataset: "${mds_dataset}"
+      dataset: "lead_score_propensity"
+      name: "lead_score_propensity_inference_preparation"
+      feature_store_project_id: "${project_id}" 
+      feature_store_dataset: "feature_store"
+      insert_table: "lead_score_propensity_inference_preparation"
+      expiration_duration_hours: 168
+    # This is the stored procedure that collects the features and prepare the examples rows for daily prediction.
     purchase_propensity_inference_preparation:
       project_id: "${project_id}"
       mds_dataset: "${mds_dataset}"
diff --git a/infrastructure/terraform/.terraform.lock.hcl b/infrastructure/terraform/.terraform.lock.hcl
index 04cda0d6..fb574eb5 100644
--- a/infrastructure/terraform/.terraform.lock.hcl
+++ b/infrastructure/terraform/.terraform.lock.hcl
@@ -167,6 +167,7 @@ provider "registry.terraform.io/hashicorp/template" {
 provider "registry.terraform.io/hashicorp/time" {
   version = "0.12.1"
   hashes = [
+    "h1:6BhxSYBJdBBKyuqatOGkuPKVenfx6UmLdiI13Pb3his=",
     "h1:j+ED7j0ZFJ4EDx7sdna76wsiIf397toylDN0dFi6v0U=",
     "zh:090023137df8effe8804e81c65f636dadf8f9d35b79c3afff282d39367ba44b2",
     "zh:26f1e458358ba55f6558613f1427dcfa6ae2be5119b722d0b3adb27cd001efea",
diff --git a/infrastructure/terraform/README.md b/infrastructure/terraform/README.md
index dc2836b0..fcbb9246 100644
--- a/infrastructure/terraform/README.md
+++ b/infrastructure/terraform/README.md
@@ -106,6 +106,7 @@ Also, this method allows you to extend this solution and develop it to satisfy y
     Terraform stores state about managed infrastructure to map real-world resources to the configuration, keep track of metadata, and improve performance. Terraform stores this state in a local file by default, but you can also use a Terraform remote backend to store state remotely. [Remote state](https://developer.hashicorp.com/terraform/cdktf/concepts/remote-backends) makes it easier for teams to work together because all members have access to the latest state data in the remote store.
 
     ```bash
+    SOURCE_ROOT="${HOME}/${REPO}"
     cd ${SOURCE_ROOT}
     scripts/generate-tf-backend.sh
     ```
diff --git a/infrastructure/terraform/modules/activation/export-procedures.tf b/infrastructure/terraform/modules/activation/export-procedures.tf
index e7281356..86d942be 100644
--- a/infrastructure/terraform/modules/activation/export-procedures.tf
+++ b/infrastructure/terraform/modules/activation/export-procedures.tf
@@ -119,10 +119,33 @@ resource "google_bigquery_routine" "export_churn_propensity_procedure" {
   routine_type    = "PROCEDURE"
   language        = "SQL"
   definition_body = data.template_file.churn_propensity_csv_export_query.rendered
-  description     = "Export purchase propensity predictions as CSV for GA4 User Data Import"
+  description     = "Export churn propensity predictions as CSV for GA4 User Data Import"
   arguments {
     name      = "prediction_table_name"
     mode      = "IN"
     data_type = jsonencode({ "typeKind" : "STRING" })
   }
 }
+
+data "template_file" "lead_score_propensity_csv_export_query" {
+  template = file("${local.source_root_dir}/templates/activation_user_import/lead_score_propensity_csv_export.sqlx")
+  vars = {
+    ga4_stream_id = var.ga4_stream_id
+    export_bucket = module.pipeline_bucket.name
+  }
+}
+
+resource "google_bigquery_routine" "export_lead_score_propensity_procedure" {
+  project         = null_resource.check_bigquery_api.id != "" ? module.project_services.project_id : var.project_id
+  dataset_id      = module.bigquery.bigquery_dataset.dataset_id
+  routine_id      = "export_lead_score_propensity_predictions"
+  routine_type    = "PROCEDURE"
+  language        = "SQL"
+  definition_body = data.template_file.lead_score_propensity_csv_export_query.rendered
+  description     = "Export lead score propensity predictions as CSV for GA4 User Data Import"
+  arguments {
+    name      = "prediction_table_name"
+    mode      = "IN"
+    data_type = jsonencode({ "typeKind" : "STRING" })
+  }
+}
\ No newline at end of file
diff --git a/infrastructure/terraform/modules/activation/main.tf b/infrastructure/terraform/modules/activation/main.tf
index 1bf35a9d..ef58ce59 100644
--- a/infrastructure/terraform/modules/activation/main.tf
+++ b/infrastructure/terraform/modules/activation/main.tf
@@ -24,6 +24,8 @@ locals {
   cltv_query_template_file                       = "cltv_query_template.sqlx"
   purchase_propensity_query_template_file        = "purchase_propensity_query_template.sqlx"
   purchase_propensity_vbb_query_template_file    = "purchase_propensity_vbb_query_template.sqlx"
+  lead_score_propensity_query_template_file      = "lead_score_propensity_query_template.sqlx"
+  lead_score_propensity_vbb_query_template_file  = "lead_score_propensity_vbb_query_template.sqlx"
   churn_propensity_query_template_file           = "churn_propensity_query_template.sqlx"
   activation_container_image_id                  = "activation-pipeline"
   docker_repo_prefix                             = "${var.location}-docker.pkg.dev/${var.project_id}"
@@ -750,7 +752,7 @@ data "template_file" "churn_propensity_query_template_file" {
   }
 }
 
-# This resource creates a bucket object using as content the purchase_propensity_query_template_file file.
+# This resource creates a bucket object using as content the churn_propensity_query_template_file file.
 resource "google_storage_bucket_object" "churn_propensity_query_template_file" {
   name    = "${local.configuration_folder}/${local.churn_propensity_query_template_file}"
   content = data.template_file.churn_propensity_query_template_file.rendered
@@ -791,6 +793,40 @@ resource "google_storage_bucket_object" "purchase_propensity_vbb_query_template_
   bucket  = module.pipeline_bucket.name
 }
 
+data "template_file" "lead_score_propensity_query_template_file" {
+  template = file("${local.template_dir}/activation_query/${local.lead_score_propensity_query_template_file}")
+
+  vars = {
+    mds_project_id     = var.mds_project_id
+    mds_dataset_suffix = var.mds_dataset_suffix
+  }
+}
+
+# This resource creates a bucket object using as content the lead_score_propensity_query_template_file file.
+resource "google_storage_bucket_object" "lead_score_propensity_query_template_file" {
+  name    = "${local.configuration_folder}/${local.lead_score_propensity_query_template_file}"
+  content = data.template_file.lead_score_propensity_query_template_file.rendered
+  bucket  = module.pipeline_bucket.name
+}
+
+# This resource creates a bucket object using as content the lead_score_propensity_vbb_query_template_file file.
+data "template_file" "lead_score_propensity_vbb_query_template_file" {
+  template = file("${local.template_dir}/activation_query/${local.lead_score_propensity_vbb_query_template_file}")
+
+  vars = {
+    mds_project_id        = var.mds_project_id
+    mds_dataset_suffix    = var.mds_dataset_suffix
+    activation_project_id = var.project_id
+    dataset               = module.bigquery.bigquery_dataset.dataset_id
+  }
+}
+
+resource "google_storage_bucket_object" "lead_score_propensity_vbb_query_template_file" {
+  name    = "${local.configuration_folder}/${local.lead_score_propensity_vbb_query_template_file}"
+  content = data.template_file.lead_score_propensity_vbb_query_template_file.rendered
+  bucket  = module.pipeline_bucket.name
+}
+
 # This data resources creates a data resource that renders a template file and stores the rendered content in a variable.
 data "template_file" "activation_type_configuration" {
   template = file("${local.template_dir}/activation_type_configuration_template.tpl")
@@ -802,6 +838,8 @@ data "template_file" "activation_type_configuration" {
     purchase_propensity_query_template_gcs_path        = "gs://${module.pipeline_bucket.name}/${google_storage_bucket_object.purchase_propensity_query_template_file.output_name}"
     purchase_propensity_vbb_query_template_gcs_path    = "gs://${module.pipeline_bucket.name}/${google_storage_bucket_object.purchase_propensity_vbb_query_template_file.output_name}"
     churn_propensity_query_template_gcs_path           = "gs://${module.pipeline_bucket.name}/${google_storage_bucket_object.churn_propensity_query_template_file.output_name}"
+    lead_score_propensity_query_template_gcs_path        = "gs://${module.pipeline_bucket.name}/${google_storage_bucket_object.lead_score_propensity_query_template_file.output_name}"
+    lead_score_propensity_vbb_query_template_gcs_path    = "gs://${module.pipeline_bucket.name}/${google_storage_bucket_object.lead_score_propensity_vbb_query_template_file.output_name}"
   }
 }
 
diff --git a/infrastructure/terraform/modules/feature-store/bigquery-datasets.tf b/infrastructure/terraform/modules/feature-store/bigquery-datasets.tf
index d7b08539..52816e44 100644
--- a/infrastructure/terraform/modules/feature-store/bigquery-datasets.tf
+++ b/infrastructure/terraform/modules/feature-store/bigquery-datasets.tf
@@ -90,6 +90,32 @@ resource "google_bigquery_dataset" "churn_propensity" {
   }
 }
 
+# This resource creates a BigQuery dataset called `lead_score_propensity`.
+resource "google_bigquery_dataset" "lead_score_propensity" {
+  dataset_id    = local.config_bigquery.dataset.lead_score_propensity.name
+  friendly_name = local.config_bigquery.dataset.lead_score_propensity.friendly_name
+  project       = null_resource.check_bigquery_api.id != "" ? local.lead_score_propensity_project_id : local.feature_store_project_id
+  description   = local.config_bigquery.dataset.lead_score_propensity.description
+  location      = local.config_bigquery.dataset.lead_score_propensity.location
+  # The max_time_travel_hours attribute specifies the maximum number of hours that data in the dataset can be accessed using time travel queries. 
+  # In this case, the maximum time travel hours is set to the value of the local file config.yaml section bigquery.dataset.feature_store.max_time_travel_hours configuration.
+  max_time_travel_hours = local.config_bigquery.dataset.lead_score_propensity.max_time_travel_hours
+  # The delete_contents_on_destroy attribute specifies whether the contents of the dataset should be deleted when the dataset is destroyed. 
+  # In this case, the delete_contents_on_destroy attribute is set to false, which means that the contents of the dataset will not be deleted when the dataset is destroyed.
+  delete_contents_on_destroy = false
+
+  labels = {
+    version = "prod"
+  }
+
+  # The lifecycle block allows you to configure the lifecycle of the dataset. 
+  # In this case, the ignore_changes attribute is set to all, which means that 
+  # Terraform will ignore any changes to the dataset and will not attempt to update the dataset.
+  lifecycle {
+    ignore_changes = all
+  }
+}
+
 # This resource creates a BigQuery dataset called `customer_lifetime_value`.
 resource "google_bigquery_dataset" "customer_lifetime_value" {
   dataset_id    = local.config_bigquery.dataset.customer_lifetime_value.name
@@ -300,7 +326,8 @@ module "gemini_insights" {
   location     = local.config_bigquery.dataset.gemini_insights.location
   # The delete_contents_on_destroy attribute specifies whether the contents of the dataset should be deleted when the dataset is destroyed. 
   # In this case, the delete_contents_on_destroy attribute is set to false, which means that the contents of the dataset will not be deleted when the dataset is destroyed.
-  delete_contents_on_destroy = true
+  delete_contents_on_destroy = false
+  deletion_protection = true
 
   dataset_labels = {
     version    = "prod",
@@ -314,7 +341,7 @@ module "gemini_insights" {
       # The max_time_travel_hours attribute specifies the maximum number of hours that data in the dataset can be accessed using time travel queries.
       # In this case, the maximum time travel hours is set to the value of the local file config.yaml section bigquery.dataset.gemini_insights.max_time_travel_hours configuration.
       max_time_travel_hours = local.config_bigquery.dataset.gemini_insights.max_time_travel_hours
-      deletion_protection   = false
+      deletion_protection   = true
       time_partitioning     = null,
       range_partitioning    = null,
       expiration_time       = null,
diff --git a/infrastructure/terraform/modules/feature-store/bigquery-procedures.tf b/infrastructure/terraform/modules/feature-store/bigquery-procedures.tf
index 1424656c..628a6848 100644
--- a/infrastructure/terraform/modules/feature-store/bigquery-procedures.tf
+++ b/infrastructure/terraform/modules/feature-store/bigquery-procedures.tf
@@ -350,6 +350,32 @@ resource "google_bigquery_routine" "churn_propensity_inference_preparation" {
   }
 }
 
+# This resource reads the contents of a local SQL file named lead_score_propensity_inference_preparation.sql and 
+# stores it in a variable named lead_score_propensity_inference_preparation_file.content. 
+# The SQL file is expected to contain the definition of a BigQuery procedure named lead_score_propensity_inference_preparation.
+data "local_file" "lead_score_propensity_inference_preparation_file" {
+  filename = "${local.sql_dir}/procedure/lead_score_propensity_inference_preparation.sql"
+}
+
+# The lead_score_propensity_inference_preparation procedure is designed to prepare features for the Lead Score Propensity model.
+# ##
+# The procedure is typically invoked before prediction the Lead Score Propensity model to ensure that the features data 
+# is in the correct format and contains the necessary features for prediction.
+resource "google_bigquery_routine" "lead_score_propensity_inference_preparation" {
+  project         = null_resource.check_bigquery_api.id != "" ? local.lead_score_propensity_project_id : local.feature_store_project_id
+  dataset_id      = google_bigquery_dataset.lead_score_propensity.dataset_id
+  routine_id      = "lead_score_propensity_inference_preparation"
+  routine_type    = "PROCEDURE"
+  language        = "SQL"
+  definition_body = data.local_file.lead_score_propensity_inference_preparation_file.content
+  description     = "Procedure that prepares features for Lead Score Propensity model inference. User-per-day granularity level features. Run this procedure every time before Lead Score Propensity model predict."
+  arguments {
+    name      = "inference_date"
+    mode      = "INOUT"
+    data_type = jsonencode({ "typeKind" : "DATE" })
+  }
+}
+
 # This resource reads the contents of a local SQL file named purchase_propensity_label.sql and 
 # stores it in a variable named purchase_propensity_label_file.content. 
 # The SQL file is expected to contain the definition of a BigQuery procedure named purchase_propensity_label.
@@ -422,6 +448,42 @@ resource "google_bigquery_routine" "churn_propensity_label" {
   }
 }
 
+# This resource reads the contents of a local SQL file named lead_score_propensity_label.sql and 
+# stores it in a variable named lead_score_propensity_label_file.content. 
+# The SQL file is expected to contain the definition of a BigQuery procedure named lead_score_propensity_label.
+data "local_file" "lead_score_propensity_label_file" {
+  filename = "${local.sql_dir}/procedure/lead_score_propensity_label.sql"
+}
+
+# The lead_score_propensity_label procedure is designed to prepare label for the Lead Score Propensity model.
+# ##
+# The procedure is typically invoked before training the Lead Score Propensity model to ensure that the labeled data 
+# is in the correct format and ready for training.
+resource "google_bigquery_routine" "lead_score_propensity_label" {
+  project         = null_resource.check_bigquery_api.id != "" ? local.feature_store_project_id : local.feature_store_project_id
+  dataset_id      = google_bigquery_dataset.feature_store.dataset_id
+  routine_id      = "lead_score_propensity_label"
+  routine_type    = "PROCEDURE"
+  language        = "SQL"
+  definition_body = data.local_file.lead_score_propensity_label_file.content
+  description     = "User-per-day granularity level labels. Run this procedure daily."
+  arguments {
+    name      = "input_date"
+    mode      = "INOUT"
+    data_type = jsonencode({ "typeKind" : "DATE" })
+  }
+  arguments {
+    name      = "end_date"
+    mode      = "INOUT"
+    data_type = jsonencode({ "typeKind" : "DATE" })
+  }
+  arguments {
+    name      = "rows_added"
+    mode      = "OUT"
+    data_type = jsonencode({ "typeKind" : "INT64" })
+  }
+}
+
 # This resource reads the contents of a local SQL file named purchase_propensity_training_preparation.sql and 
 # stores it in a variable named purchase_propensity_training_preparation_file.content. 
 # The SQL file is expected to contain the definition of a BigQuery procedure named purchase_propensity_training_preparation.
@@ -463,6 +525,46 @@ resource "google_bigquery_routine" "purchase_propensity_training_preparation" {
   }
 }
 
+# This resource reads the contents of a local SQL file named lead_score_propensity_training_preparation.sql and 
+# stores it in a variable named lead_score_propensity_training_preparation_file.content. 
+# The SQL file is expected to contain the definition of a BigQuery procedure named lead_score_propensity_training_preparation.
+data "local_file" "lead_score_propensity_training_preparation_file" {
+  filename = "${local.sql_dir}/procedure/lead_score_propensity_training_preparation.sql"
+}
+
+# The lead_score_propensity_training_preparation procedure is designed to prepare features for the Lead Score Propensity model.
+# ##
+# The procedure is typically invoked before training the Lead Score Propensity model to ensure that the features data 
+# is in the correct format and contains the necessary features for training.
+resource "google_bigquery_routine" "lead_score_propensity_training_preparation" {
+  project         = null_resource.check_bigquery_api.id != "" ? local.lead_score_propensity_project_id : local.feature_store_project_id
+  dataset_id      = google_bigquery_dataset.lead_score_propensity.dataset_id
+  routine_id      = "lead_score_propensity_training_preparation"
+  routine_type    = "PROCEDURE"
+  language        = "SQL"
+  definition_body = data.local_file.lead_score_propensity_training_preparation_file.content
+  description     = "Procedure that prepares features for Lead Score Propensity model training. User-per-day granularity level features. Run this procedure every time before Lead Score Propensity model train."
+  arguments {
+    name      = "start_date"
+    mode      = "INOUT"
+    data_type = jsonencode({ "typeKind" : "DATE" })
+  }
+  arguments {
+    name      = "end_date"
+    mode      = "INOUT"
+    data_type = jsonencode({ "typeKind" : "DATE" })
+  }
+  arguments {
+    name      = "train_split_end_number"
+    mode      = "INOUT"
+    data_type = jsonencode({ "typeKind" : "INT64" })
+  }
+  arguments {
+    name      = "validation_split_end_number"
+    mode      = "INOUT"
+    data_type = jsonencode({ "typeKind" : "INT64" })
+  }
+}
 
 # This resource reads the contents of a local SQL file named churn_propensity_training_preparation.sql and 
 # stores it in a variable named churn_propensity_training_preparation_file.content. 
@@ -685,6 +787,42 @@ resource "google_bigquery_routine" "user_rolling_window_metrics" {
   }
 }
 
+# This resource reads the contents of a local SQL file named user_rolling_window_lead_metrics.sql and 
+# stores it in a variable named user_rolling_window_lead_metrics_file.content. 
+# The SQL file is expected to contain the definition of a BigQuery procedure named user_rolling_window_lead_metrics.
+data "local_file" "user_rolling_window_lead_metrics_file" {
+  filename = "${local.sql_dir}/procedure/user_rolling_window_lead_metrics.sql"
+}
+
+# The user_rolling_window_lead_metrics procedure is designed to prepare the features for the Purchase Propensity model.
+# ##
+# The procedure is typically invoked before training the Purchase Propensity model to ensure that the features data 
+# is in the correct format and ready for training.
+resource "google_bigquery_routine" "user_rolling_window_lead_metrics" {
+  project         = null_resource.check_bigquery_api.id != "" ? local.feature_store_project_id : local.feature_store_project_id
+  dataset_id      = google_bigquery_dataset.feature_store.dataset_id
+  routine_id      = "user_rolling_window_lead_metrics"
+  routine_type    = "PROCEDURE"
+  language        = "SQL"
+  definition_body = data.local_file.user_rolling_window_lead_metrics_file.content
+  description     = "User-per-day granularity level metrics. Run this procedure daily. Metrics calculated using a rolling window operation."
+  arguments {
+    name      = "input_date"
+    mode      = "INOUT"
+    data_type = jsonencode({ "typeKind" : "DATE" })
+  }
+  arguments {
+    name      = "end_date"
+    mode      = "INOUT"
+    data_type = jsonencode({ "typeKind" : "DATE" })
+  }
+  arguments {
+    name      = "rows_added"
+    mode      = "OUT"
+    data_type = jsonencode({ "typeKind" : "INT64" })
+  }
+}
+
 # This resource reads the contents of a local SQL file named user_scoped_lifetime_metrics.sql
 data "local_file" "user_scoped_lifetime_metrics_file" {
   filename = "${local.sql_dir}/procedure/user_scoped_lifetime_metrics.sql"
@@ -880,6 +1018,14 @@ resource "google_bigquery_routine" "user_behaviour_revenue_insights" {
   depends_on = [
     null_resource.check_gemini_model_exists
   ]
+
+  # The lifecycle block is used to configure the lifecycle of the table. In this case, the ignore_changes attribute is set to all, which means that Terraform will ignore 
+  # any changes to the table and will not attempt to update the table. The prevent_destroy attribute is set to true, which means that Terraform will prevent the table from being destroyed.
+  lifecycle {
+    ignore_changes  = all
+    #prevent_destroy = true
+    create_before_destroy = true
+  }
 }
 
 /*
@@ -930,6 +1076,20 @@ resource "google_bigquery_routine" "invoke_backfill_churn_propensity_label" {
   description     = "Procedure that backfills the churn_propensity_label feature table. Run this procedure occasionally before training the models."
 }
 
+data "local_file" "invoke_backfill_lead_score_propensity_label_file" {
+  filename = "${local.sql_dir}/query/invoke_backfill_lead_score_propensity_label.sql"
+}
+
+resource "google_bigquery_routine" "invoke_backfill_lead_score_propensity_label" {
+  project         = null_resource.check_bigquery_api.id != "" ? local.feature_store_project_id : local.feature_store_project_id
+  dataset_id      = google_bigquery_dataset.feature_store.dataset_id
+  routine_id      = "invoke_backfill_lead_score_propensity_label"
+  routine_type    = "PROCEDURE"
+  language        = "SQL"
+  definition_body = data.local_file.invoke_backfill_lead_score_propensity_label_file.content
+  description     = "Procedure that backfills the lead_score_propensity_label feature table. Run this procedure occasionally before training the models."
+}
+
 data "local_file" "invoke_backfill_user_dimensions_file" {
   filename = "${local.sql_dir}/query/invoke_backfill_user_dimensions.sql"
 }
@@ -1003,6 +1163,20 @@ resource "google_bigquery_routine" "invoke_backfill_user_rolling_window_metrics"
   description     = "Procedure that backfills the user_rolling_window_metrics feature table. Run this procedure occasionally before training the models."
 }
 
+data "local_file" "invoke_backfill_user_rolling_window_lead_metrics_file" {
+  filename = "${local.sql_dir}/query/invoke_backfill_user_rolling_window_lead_metrics.sql"
+}
+
+resource "google_bigquery_routine" "invoke_backfill_user_rolling_window_lead_metrics" {
+  project         = null_resource.check_bigquery_api.id != "" ? local.feature_store_project_id : local.feature_store_project_id
+  dataset_id      = google_bigquery_dataset.feature_store.dataset_id
+  routine_id      = "invoke_backfill_user_rolling_window_lead_metrics"
+  routine_type    = "PROCEDURE"
+  language        = "SQL"
+  definition_body = data.local_file.invoke_backfill_user_rolling_window_lead_metrics_file.content
+  description     = "Procedure that backfills the user_rolling_window_lead_metrics feature table. Run this procedure occasionally before training the models."
+}
+
 
 data "local_file" "invoke_backfill_user_scoped_lifetime_metrics_file" {
   filename = "${local.sql_dir}/query/invoke_backfill_user_scoped_lifetime_metrics.sql"
@@ -1091,6 +1265,14 @@ resource "google_bigquery_routine" "invoke_backfill_user_behaviour_revenue_insig
     null_resource.check_gemini_model_exists,
     null_resource.create_gemini_model
   ]
+
+  # The lifecycle block is used to configure the lifecycle of the table. In this case, the ignore_changes attribute is set to all, which means that Terraform will ignore 
+  # any changes to the table and will not attempt to update the table. The prevent_destroy attribute is set to true, which means that Terraform will prevent the table from being destroyed.
+  lifecycle {
+    ignore_changes  = all
+    #prevent_destroy = true
+    create_before_destroy = true
+  }
 }
 
 /*
@@ -1139,6 +1321,19 @@ resource "google_bigquery_routine" "invoke_churn_propensity_inference_preparatio
   definition_body = data.local_file.invoke_churn_propensity_inference_preparation_file.content
 }
 
+data "local_file" "invoke_lead_score_propensity_inference_preparation_file" {
+  filename = "${local.sql_dir}/query/invoke_lead_score_propensity_inference_preparation.sql"
+}
+
+resource "google_bigquery_routine" "invoke_lead_score_propensity_inference_preparation" {
+  project         = null_resource.check_bigquery_api.id != "" ? local.lead_score_propensity_project_id : local.feature_store_project_id
+  dataset_id      = google_bigquery_dataset.lead_score_propensity.dataset_id
+  routine_id      = "invoke_lead_score_propensity_inference_preparation"
+  routine_type    = "PROCEDURE"
+  language        = "SQL"
+  definition_body = data.local_file.invoke_lead_score_propensity_inference_preparation_file.content
+}
+
 
 data "local_file" "invoke_audience_segmentation_inference_preparation_file" {
   filename = "${local.sql_dir}/query/invoke_audience_segmentation_inference_preparation.sql"
@@ -1222,6 +1417,19 @@ resource "google_bigquery_routine" "invoke_churn_propensity_training_preparation
 }
 
 
+data "local_file" "invoke_lead_score_propensity_training_preparation_file" {
+  filename = "${local.sql_dir}/query/invoke_lead_score_propensity_training_preparation.sql"
+}
+
+resource "google_bigquery_routine" "invoke_lead_score_propensity_training_preparation" {
+  project         = null_resource.check_bigquery_api.id != "" ? local.lead_score_propensity_project_id : local.feature_store_project_id
+  dataset_id      = google_bigquery_dataset.lead_score_propensity.dataset_id
+  routine_id      = "invoke_lead_score_propensity_training_preparation"
+  routine_type    = "PROCEDURE"
+  language        = "SQL"
+  definition_body = data.local_file.invoke_lead_score_propensity_training_preparation_file.content
+}
+
 data "local_file" "invoke_audience_segmentation_training_preparation_file" {
   filename = "${local.sql_dir}/query/invoke_audience_segmentation_training_preparation.sql"
 }
@@ -1298,6 +1506,20 @@ resource "google_bigquery_routine" "invoke_purchase_propensity_label" {
 }
 
 
+data "local_file" "invoke_lead_score_propensity_label_file" {
+  filename = "${local.sql_dir}/query/invoke_lead_score_propensity_label.sql"
+}
+
+resource "google_bigquery_routine" "invoke_lead_score_propensity_label" {
+  project         = null_resource.check_bigquery_api.id != "" ? local.feature_store_project_id : local.feature_store_project_id
+  dataset_id      = google_bigquery_dataset.feature_store.dataset_id
+  routine_id      = "invoke_lead_score_propensity_label"
+  routine_type    = "PROCEDURE"
+  language        = "SQL"
+  definition_body = data.local_file.invoke_lead_score_propensity_label_file.content
+  description     = "Procedure that invokes the lead_score_propensity_label table. Daily granularity level. Run this procedure daily before running prediction pipelines."
+}
+
 data "local_file" "invoke_churn_propensity_label_file" {
   filename = "${local.sql_dir}/query/invoke_churn_propensity_label.sql"
 }
@@ -1387,6 +1609,20 @@ resource "google_bigquery_routine" "invoke_user_rolling_window_metrics" {
 }
 
 
+data "local_file" "invoke_user_rolling_window_lead_metrics_file" {
+  filename = "${local.sql_dir}/query/invoke_user_rolling_window_lead_metrics.sql"
+}
+
+resource "google_bigquery_routine" "invoke_user_rolling_window_lead_metrics" {
+  project         = null_resource.check_bigquery_api.id != "" ? local.feature_store_project_id : local.feature_store_project_id
+  dataset_id      = google_bigquery_dataset.feature_store.dataset_id
+  routine_id      = "invoke_user_rolling_window_lead_metrics"
+  routine_type    = "PROCEDURE"
+  language        = "SQL"
+  definition_body = data.local_file.invoke_user_rolling_window_lead_metrics_file.content
+  description     = "Procedure that invokes the user_rolling_window_lead_metrics table. Daily granularity level. Run this procedure daily before running prediction pipelines."
+}
+
 data "local_file" "invoke_user_scoped_lifetime_metrics_file" {
   filename = "${local.sql_dir}/query/invoke_user_scoped_lifetime_metrics.sql"
 }
@@ -1448,7 +1684,7 @@ data "local_file" "invoke_user_session_event_aggregated_metrics_file" {
 }
 
 resource "google_bigquery_routine" "invoke_user_session_event_aggregated_metrics" {
-  project         = null_resource.check_bigquery_api.id != "" ? local.purchase_propensity_project_id : local.feature_store_project_id
+  project         = null_resource.check_bigquery_api.id != "" ? local.feature_store_project_id : local.feature_store_project_id
   dataset_id      = google_bigquery_dataset.feature_store.dataset_id
   routine_id      = "invoke_user_session_event_aggregated_metrics"
   routine_type    = "PROCEDURE"
@@ -1481,7 +1717,8 @@ resource "null_resource" "create_gemini_model" {
   # any changes to the table and will not attempt to update the table. The prevent_destroy attribute is set to true, which means that Terraform will prevent the table from being destroyed.
   lifecycle {
     ignore_changes  = all
-    prevent_destroy = true
+    #prevent_destroy = true
+    create_before_destroy = true
   }
 
   depends_on = [
diff --git a/infrastructure/terraform/modules/feature-store/bigquery-tables.tf b/infrastructure/terraform/modules/feature-store/bigquery-tables.tf
index 891a4b3e..e74fb1ed 100644
--- a/infrastructure/terraform/modules/feature-store/bigquery-tables.tf
+++ b/infrastructure/terraform/modules/feature-store/bigquery-tables.tf
@@ -112,6 +112,24 @@ resource "google_bigquery_table" "churn_propensity_inference_preparation" {
   schema = file("${local.sql_dir}/schema/table/churn_propensity_inference_preparation.json")
 }
 
+# This resource creates a BigQuery table named lead_score_propensity_inference_preparation 
+# in the dataset specified by google_bigquery_dataset.lead_score_propensity.dataset_id.
+resource "google_bigquery_table" "lead_score_propensity_inference_preparation" {
+  project     = google_bigquery_dataset.lead_score_propensity.project
+  dataset_id  = google_bigquery_dataset.lead_score_propensity.dataset_id
+  table_id    = local.config_bigquery.table.lead_score_propensity_inference_preparation.table_name
+  description = local.config_bigquery.table.lead_score_propensity_inference_preparation.table_description
+
+  # The deletion_protection attribute specifies whether the table should be protected from deletion. In this case, it's set to false, which means that the table can be deleted.
+  deletion_protection = false
+  labels = {
+    version = "prod"
+  }
+
+  # The schema attribute specifies the schema of the table. In this case, the schema is defined in the JSON file.
+  schema = file("${local.sql_dir}/schema/table/lead_score_propensity_inference_preparation.json")
+}
+
 # This resource creates a BigQuery table named purchase_propensity_label 
 # in the dataset specified by google_bigquery_dataset.feature_store.dataset_id.
 resource "google_bigquery_table" "purchase_propensity_label" {
@@ -162,6 +180,31 @@ resource "google_bigquery_table" "churn_propensity_label" {
   }
 }
 
+# This resource creates a BigQuery table named lead_score_propensity_label 
+# in the dataset specified by google_bigquery_dataset.feature_store.dataset_id.
+resource "google_bigquery_table" "lead_score_propensity_label" {
+  project     = google_bigquery_dataset.feature_store.project
+  dataset_id  = google_bigquery_dataset.feature_store.dataset_id
+  table_id    = local.config_bigquery.table.lead_score_propensity_label.table_name
+  description = local.config_bigquery.table.lead_score_propensity_label.table_description
+
+  # The deletion_protection attribute specifies whether the table should be protected from deletion. In this case, it's set to false, which means that the table can be deleted.
+  deletion_protection = false
+  labels = {
+    version = "prod"
+  }
+
+  # The schema attribute specifies the schema of the table. In this case, the schema is defined in the JSON file.
+  schema = file("${local.sql_dir}/schema/table/lead_score_propensity_label.json")
+
+  # The lifecycle block is used to configure the lifecycle of the table. In this case, the ignore_changes attribute is set to all, which means that Terraform will ignore 
+  # any changes to the table and will not attempt to update the table. The prevent_destroy attribute is set to true, which means that Terraform will prevent the table from being destroyed.
+  lifecycle {
+    ignore_changes  = all
+    prevent_destroy = true
+  }
+}
+
 # This resource creates a BigQuery table named user_dimensions 
 # in the dataset specified by google_bigquery_dataset.feature_store.dataset_id.
 resource "google_bigquery_table" "user_dimensions" {
@@ -287,6 +330,31 @@ resource "google_bigquery_table" "user_rolling_window_metrics" {
   }
 }
 
+# This resource creates a BigQuery table named user_rolling_window_lead_metrics 
+# in the dataset specified by google_bigquery_dataset.feature_store.dataset_id.
+resource "google_bigquery_table" "user_rolling_window_lead_metrics" {
+  project     = google_bigquery_dataset.feature_store.project
+  dataset_id  = google_bigquery_dataset.feature_store.dataset_id
+  table_id    = local.config_bigquery.table.user_rolling_window_lead_metrics.table_name
+  description = local.config_bigquery.table.user_rolling_window_lead_metrics.table_description
+
+  # The deletion_protection attribute specifies whether the table should be protected from deletion. In this case, it's set to false, which means that the table can be deleted.
+  deletion_protection = false
+  labels = {
+    version = "prod"
+  }
+
+  # The schema attribute specifies the schema of the table. In this case, the schema is defined in the JSON file.
+  schema = file("${local.sql_dir}/schema/table/user_rolling_window_lead_metrics.json")
+
+  # The lifecycle block is used to configure the lifecycle of the table. In this case, the ignore_changes attribute is set to all, which means that Terraform will ignore 
+  # any changes to the table and will not attempt to update the table. The prevent_destroy attribute is set to true, which means that Terraform will prevent the table from being destroyed.
+  lifecycle {
+    ignore_changes  = all
+    prevent_destroy = true
+  }
+}
+
 # This resource creates a BigQuery table named user_scoped_lifetime_metrics 
 # in the dataset specified by google_bigquery_dataset.feature_store.dataset_id.
 resource "google_bigquery_table" "user_scoped_lifetime_metrics" {
diff --git a/infrastructure/terraform/modules/feature-store/main.tf b/infrastructure/terraform/modules/feature-store/main.tf
index 296d812f..9f1a8d7b 100644
--- a/infrastructure/terraform/modules/feature-store/main.tf
+++ b/infrastructure/terraform/modules/feature-store/main.tf
@@ -24,6 +24,7 @@ locals {
   builder_repository_id                 = "marketing-analytics-jumpstart-base-repo"
   purchase_propensity_project_id        = null_resource.check_bigquery_api.id != "" ? local.config_vars.bigquery.dataset.purchase_propensity.project_id : local.feature_store_project_id
   churn_propensity_project_id           = null_resource.check_bigquery_api.id != "" ? local.config_vars.bigquery.dataset.churn_propensity.project_id : local.feature_store_project_id
+  lead_score_propensity_project_id      = null_resource.check_bigquery_api.id != "" ? local.config_vars.bigquery.dataset.lead_score_propensity.project_id : local.feature_store_project_id
   audience_segmentation_project_id      = null_resource.check_bigquery_api.id != "" ? local.config_vars.bigquery.dataset.audience_segmentation.project_id : local.feature_store_project_id
   auto_audience_segmentation_project_id = null_resource.check_bigquery_api.id != "" ? local.config_vars.bigquery.dataset.auto_audience_segmentation.project_id : local.feature_store_project_id
   aggregated_vbb_project_id             = null_resource.check_bigquery_api.id != "" ? local.config_vars.bigquery.dataset.aggregated_vbb.project_id : local.feature_store_project_id
@@ -36,7 +37,7 @@ module "project_services" {
   source  = "terraform-google-modules/project-factory/google//modules/project_services"
   version = "17.0.0"
 
-  disable_dependent_services  = true
+  disable_dependent_services  = false
   disable_services_on_destroy = false
 
   project_id = local.feature_store_project_id
@@ -115,6 +116,14 @@ resource "google_bigquery_connection" "vertex_ai_connection" {
   project       = null_resource.check_aiplatform_api.id != "" ? module.project_services.project_id : local.feature_store_project_id
   location      = local.config_bigquery.region
   cloud_resource {}
+
+  # The lifecycle block is used to configure the lifecycle of the table. In this case, the ignore_changes attribute is set to all, which means that Terraform will ignore 
+  # any changes to the table and will not attempt to update the table. The prevent_destroy attribute is set to true, which means that Terraform will prevent the table from being destroyed.
+  lifecycle {
+    ignore_changes  = all
+    #prevent_destroy = true
+    create_before_destroy = true
+  }
 }
 
 
@@ -144,7 +153,8 @@ resource "google_project_iam_member" "vertex_ai_connection_sa_roles" {
   # any changes to the table and will not attempt to update the table. The prevent_destroy attribute is set to true, which means that Terraform will prevent the table from being destroyed.
   lifecycle {
     ignore_changes  = all
-    prevent_destroy = true
+    #prevent_destroy = true
+    create_before_destroy = true
   }
 }
 
@@ -157,6 +167,14 @@ resource "time_sleep" "wait_for_vertex_ai_connection_sa_role_propagation" {
   depends_on = [
     google_project_iam_member.vertex_ai_connection_sa_roles
   ]
+
+  # The lifecycle block is used to configure the lifecycle of the table. In this case, the ignore_changes attribute is set to all, which means that Terraform will ignore 
+  # any changes to the table and will not attempt to update the table. The prevent_destroy attribute is set to true, which means that Terraform will prevent the table from being destroyed.
+  lifecycle {
+    ignore_changes  = all
+    #prevent_destroy = true
+    create_before_destroy = true
+  }
 }
 
 
diff --git a/infrastructure/terraform/modules/pipelines/main.tf b/infrastructure/terraform/modules/pipelines/main.tf
index 86d363d7..5de45c5d 100644
--- a/infrastructure/terraform/modules/pipelines/main.tf
+++ b/infrastructure/terraform/modules/pipelines/main.tf
@@ -37,7 +37,7 @@ module "project_services" {
   source  = "terraform-google-modules/project-factory/google//modules/project_services"
   version = "17.0.0"
 
-  disable_dependent_services  = true
+  disable_dependent_services  = false
   disable_services_on_destroy = false
 
   project_id = local.pipeline_vars.project_id
diff --git a/infrastructure/terraform/modules/pipelines/pipelines.tf b/infrastructure/terraform/modules/pipelines/pipelines.tf
index a55e9136..0074a536 100644
--- a/infrastructure/terraform/modules/pipelines/pipelines.tf
+++ b/infrastructure/terraform/modules/pipelines/pipelines.tf
@@ -18,6 +18,14 @@ resource "google_service_account" "service_account" {
   account_id   = local.pipeline_vars.service_account_id
   display_name = local.pipeline_vars.service_account_id
   description  = "Service Account to run Vertex AI Pipelines"
+
+  # The lifecycle block is used to configure the lifecycle of the table. In this case, the ignore_changes attribute is set to all, which means that Terraform will ignore 
+  # any changes to the table and will not attempt to update the table. The prevent_destroy attribute is set to true, which means that Terraform will prevent the table from being destroyed.
+  lifecycle {
+    ignore_changes  = all
+    #prevent_destroy = true
+    create_before_destroy = true
+  }
 }
 
 # Wait for the pipelines service account to be created
@@ -72,6 +80,14 @@ resource "google_project_iam_member" "pipelines_sa_roles" {
     "roles/compute.networkUser"
   ])
   role = each.key
+
+  # The lifecycle block is used to configure the lifecycle of the table. In this case, the ignore_changes attribute is set to all, which means that Terraform will ignore 
+  # any changes to the table and will not attempt to update the table. The prevent_destroy attribute is set to true, which means that Terraform will prevent the table from being destroyed.
+  lifecycle {
+    ignore_changes  = all
+    #prevent_destroy = true
+    create_before_destroy = true
+  }
 }
 
 # This resource binds the service account to the required roles in the mds project
@@ -89,6 +105,14 @@ resource "google_project_iam_member" "pipelines_sa_mds_project_roles" {
     "roles/bigquery.dataViewer"
   ])
   role = each.key
+
+  # The lifecycle block is used to configure the lifecycle of the table. In this case, the ignore_changes attribute is set to all, which means that Terraform will ignore 
+  # any changes to the table and will not attempt to update the table. The prevent_destroy attribute is set to true, which means that Terraform will prevent the table from being destroyed.
+  lifecycle {
+    ignore_changes  = all
+    #prevent_destroy = true
+    create_before_destroy = true
+  }
 }
 
 # This resource creates a service account to run the dataflow jobs
@@ -97,6 +121,14 @@ resource "google_service_account" "dataflow_worker_service_account" {
   account_id   = local.dataflow_vars.worker_service_account_id
   display_name = local.dataflow_vars.worker_service_account_id
   description  = "Service Account to run Dataflow jobs"
+
+  # The lifecycle block is used to configure the lifecycle of the table. In this case, the ignore_changes attribute is set to all, which means that Terraform will ignore 
+  # any changes to the table and will not attempt to update the table. The prevent_destroy attribute is set to true, which means that Terraform will prevent the table from being destroyed.
+  lifecycle {
+    ignore_changes  = all
+    #prevent_destroy = true
+    create_before_destroy = true
+  }
 }
 
 # Wait for the dataflow worker service account to be created
@@ -143,6 +175,14 @@ resource "google_project_iam_member" "dataflow_worker_sa_roles" {
     "roles/storage.objectAdmin",
   ])
   role = each.key
+
+  # The lifecycle block is used to configure the lifecycle of the table. In this case, the ignore_changes attribute is set to all, which means that Terraform will ignore 
+  # any changes to the table and will not attempt to update the table. The prevent_destroy attribute is set to true, which means that Terraform will prevent the table from being destroyed.
+  lifecycle {
+    ignore_changes  = all
+    #prevent_destroy = true
+    create_before_destroy = true
+  }
 }
 
 # This resource binds the service account to the required roles
@@ -157,6 +197,14 @@ resource "google_service_account_iam_member" "dataflow_sa_iam" {
   service_account_id = "projects/${module.project_services.project_id}/serviceAccounts/${google_service_account.dataflow_worker_service_account.email}"
   role               = "roles/iam.serviceAccountUser"
   member             = "serviceAccount:${google_service_account.service_account.email}"
+
+  # The lifecycle block is used to configure the lifecycle of the table. In this case, the ignore_changes attribute is set to all, which means that Terraform will ignore 
+  # any changes to the table and will not attempt to update the table. The prevent_destroy attribute is set to true, which means that Terraform will prevent the table from being destroyed.
+  lifecycle {
+    ignore_changes  = all
+    #prevent_destroy = true
+    create_before_destroy = true
+  }
 }
 
 # This resource creates a Cloud Storage Bucket for the pipeline artifacts
@@ -170,12 +218,12 @@ resource "google_storage_bucket" "pipelines_bucket" {
   # even if it contains objects. In this case, it's set to false, which means that the bucket will not be destroyed if it contains objects.
   force_destroy = false
 
-  # The lifecycle block allows you to configure the lifecycle of the bucket. 
-  # In this case, the ignore_changes attribute is set to all, which means that Terraform 
-  # will ignore any changes to the bucket's lifecycle configuration. The prevent_destroy attribute is set to false, which means that the bucket can be destroyed.
+  # The lifecycle block is used to configure the lifecycle of the table. In this case, the ignore_changes attribute is set to all, which means that Terraform will ignore 
+  # any changes to the table and will not attempt to update the table. The prevent_destroy attribute is set to true, which means that Terraform will prevent the table from being destroyed.
   lifecycle {
     ignore_changes  = all
-    prevent_destroy = false ##true
+    #prevent_destroy = true
+    create_before_destroy = true
   }
 }
 
@@ -190,12 +238,12 @@ resource "google_storage_bucket" "custom_model_bucket" {
   # even if it contains objects. In this case, it's set to false, which means that the bucket will not be destroyed if it contains objects.
   force_destroy = false
 
-  # The lifecycle block allows you to configure the lifecycle of the bucket. 
-  # In this case, the ignore_changes attribute is set to all, which means that Terraform 
-  # will ignore any changes to the bucket's lifecycle configuration. The prevent_destroy attribute is set to false, which means that the bucket can be destroyed.
+  # The lifecycle block is used to configure the lifecycle of the table. In this case, the ignore_changes attribute is set to all, which means that Terraform will ignore 
+  # any changes to the table and will not attempt to update the table. The prevent_destroy attribute is set to true, which means that Terraform will prevent the table from being destroyed.
   lifecycle {
     ignore_changes  = all
-    prevent_destroy = false ##true
+    #prevent_destroy = true
+    create_before_destroy = true
   }
 }
 
@@ -350,6 +398,31 @@ resource "null_resource" "check_pipeline_docker_image_pushed" {
 ## Feature Engineering Pipelines
 #######
 
+# This resource is used to compile and upload the Vertex AI pipeline for feature engineering - lead score propensity use case
+resource "null_resource" "compile_feature_engineering_lead_score_propensity_pipeline" {
+  triggers = {
+    working_dir                  = "${local.source_root_dir}/python"
+    tag                          = local.compile_pipelines_tag
+    pipelines_repo_id            = google_artifact_registry_repository.pipelines-repo.id
+    pipelines_repo_create_time   = google_artifact_registry_repository.pipelines-repo.create_time
+    source_content_hash          = local.pipelines_content_hash
+    upstream_resource_dependency = null_resource.check_pipeline_docker_image_pushed.id
+  }
+
+  # The provisioner block specifies the command that will be executed to compile and upload the pipeline.
+  # This command will execute the compiler function in the pipelines module, which will compile the pipeline YAML file, and the uploader function, 
+  # which will upload the pipeline YAML file to the specified Artifact Registry repository. The scheduler function will then schedule the pipeline to run on a regular basis.
+  provisioner "local-exec" {
+    command     = <<-EOT
+    ${var.uv_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.feature-creation-lead-score-propensity.execution -o fe_lead_score_propensity.yaml
+    ${var.uv_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f fe_lead_score_propensity.yaml -t ${self.triggers.tag} -t latest
+    ${var.uv_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.feature-creation-lead-score-propensity.execution -i fe_lead_score_propensity.yaml
+    EOT
+    working_dir = self.triggers.working_dir
+  }
+}
+
+
 # This resource is used to compile and upload the Vertex AI pipeline for feature engineering - auto audience segmentation use case
 resource "null_resource" "compile_feature_engineering_auto_audience_segmentation_pipeline" {
   triggers = {
@@ -358,7 +431,7 @@ resource "null_resource" "compile_feature_engineering_auto_audience_segmentation
     pipelines_repo_id            = google_artifact_registry_repository.pipelines-repo.id
     pipelines_repo_create_time   = google_artifact_registry_repository.pipelines-repo.create_time
     source_content_hash          = local.pipelines_content_hash
-    upstream_resource_dependency = null_resource.build_push_pipelines_components_image.id
+    upstream_resource_dependency = null_resource.compile_feature_engineering_lead_score_propensity_pipeline.id
   }
 
   # The provisioner block specifies the command that will be executed to compile and upload the pipeline.
@@ -498,12 +571,54 @@ resource "null_resource" "compile_feature_engineering_customer_lifetime_value_pi
 ## Training and Inference Pipelines
 ###
 
+# This resource is used to compile and upload the Vertex AI pipeline for training the propensity model - lead score propensity use case
+resource "null_resource" "compile_lead_score_propensity_training_pipelines" {
+  triggers = {
+    working_dir                  = "${local.source_root_dir}/python"
+    tag                          = local.compile_pipelines_tag
+    upstream_resource_dependency = null_resource.compile_feature_engineering_customer_lifetime_value_pipeline.id
+  }
+
+  # The provisioner block specifies the command that will be executed to compile and upload the pipeline.
+  # This command will execute the compiler function in the pipelines module, which will compile the pipeline YAML file, and the uploader function, 
+  # which will upload the pipeline YAML file to the specified Artifact Registry repository. The scheduler function will then schedule the pipeline to run on a regular basis.
+  provisioner "local-exec" {
+    command     = <<-EOT
+    ${var.uv_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.lead_score_propensity.training -o lead_score_propensity_training.yaml
+    ${var.uv_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f lead_score_propensity_training.yaml -t ${self.triggers.tag} -t latest
+    ${var.uv_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.lead_score_propensity.training -i lead_score_propensity_training.yaml
+    EOT
+    working_dir = self.triggers.working_dir
+  }
+}
+
+# This resource is used to compile and upload the Vertex AI pipeline for prediction using the propensity model - lead score propensity use case
+resource "null_resource" "compile_lead_score_propensity_prediction_pipelines" {
+  triggers = {
+    working_dir                  = "${local.source_root_dir}/python"
+    tag                          = local.compile_pipelines_tag
+    upstream_resource_dependency = null_resource.compile_lead_score_propensity_training_pipelines.id
+  }
+
+  # The provisioner block specifies the command that will be executed to compile and upload the pipeline.
+  # This command will execute the compiler function in the pipelines module, which will compile the pipeline YAML file, and the uploader function, 
+  # which will upload the pipeline YAML file to the specified Artifact Registry repository. The scheduler function will then schedule the pipeline to run on a regular basis.
+  provisioner "local-exec" {
+    command     = <<-EOT
+    ${var.uv_run_alias} python -m pipelines.compiler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.lead_score_propensity.prediction -o lead_score_propensity_prediction.yaml
+    ${var.uv_run_alias} python -m pipelines.uploader -c ${local.config_file_path_relative_python_run_dir} -f lead_score_propensity_prediction.yaml -t ${self.triggers.tag} -t latest
+    ${var.uv_run_alias} python -m pipelines.scheduler -c ${local.config_file_path_relative_python_run_dir} -p vertex_ai.pipelines.lead_score_propensity.prediction -i lead_score_propensity_prediction.yaml
+    EOT
+    working_dir = self.triggers.working_dir
+  }
+}
+
 # This resource is used to compile and upload the Vertex AI pipeline for training the propensity model - purchase propensity use case
 resource "null_resource" "compile_purchase_propensity_training_pipelines" {
   triggers = {
     working_dir                  = "${local.source_root_dir}/python"
     tag                          = local.compile_pipelines_tag
-    upstream_resource_dependency = null_resource.compile_feature_engineering_customer_lifetime_value_pipeline.id
+    upstream_resource_dependency = null_resource.compile_lead_score_propensity_prediction_pipelines.id
   }
 
   # The provisioner block specifies the command that will be executed to compile and upload the pipeline.
diff --git a/infrastructure/terraform/terraform-sample.tfvars b/infrastructure/terraform/terraform-sample.tfvars
index 71f440d7..ff4fd015 100644
--- a/infrastructure/terraform/terraform-sample.tfvars
+++ b/infrastructure/terraform/terraform-sample.tfvars
@@ -39,6 +39,151 @@ source_ads_export_data = [
 
 feature_store_project_id = "Project ID where feature store resources will be created"
 
+###################  PIPELINE CONFIGURATIONS  ##################################
+
+pipeline_configuration = {
+    feature-creation-auto-audience-segmentation = {
+      execution = {
+        schedule = {
+          state                    = "ACTIVE"
+        }
+      }
+    }
+    feature-creation-audience-segmentation = {
+      execution = {
+        schedule = {
+          state                    = "ACTIVE"
+        }
+      }
+    }
+    feature-creation-purchase-propensity = {
+      execution = {
+        schedule = {
+          state                    = "ACTIVE"
+        }
+      }
+    }
+    feature-creation-churn-propensity = {
+      execution = {
+        schedule = {
+          state                    = "ACTIVE"
+        }
+      }
+    }
+    feature-creation-customer-ltv = {
+      execution = {
+        schedule = {
+          state                    = "ACTIVE"
+        }
+      }
+    }
+    feature-creation-aggregated-value-based-bidding = {
+      execution = {
+        schedule = {
+          state                    = "ACTIVE"
+        }
+      }
+    }
+    feature-creation-lead-score-propensity = {
+      execution = {
+        schedule = {
+          state                    = "ACTIVE"
+        }
+      }
+    }
+    value_based_bidding = {
+      training = {
+        schedule = {
+          state                    = "PAUSED"
+        }
+      }
+      explanation = {
+        schedule = {
+          state                    = "ACTIVE"
+        }
+      }
+    }
+    purchase_propensity = {
+      training = {
+        schedule = {
+          state                    = "PAUSED"
+        }
+      }
+      prediction = {
+        schedule = {
+          state                    = "ACTIVE"
+        }
+      }
+    }
+    churn_propensity = {
+      training = {
+        schedule = {
+          state                    = "PAUSED"
+        }
+      }
+      prediction = {
+        schedule = {
+          state                    = "ACTIVE"
+        }
+      }
+    }
+    segmentation = {
+      training = {
+        schedule = {
+          state                    = "PAUSED"
+        }
+      }
+      prediction = {
+        schedule = {
+          state                    = "ACTIVE"
+        }
+      }
+    }
+    auto_segmentation = {
+      training = {
+        schedule = {
+          state                    = "PAUSED"
+        }
+      }
+      prediction = {
+        schedule = {
+          state                    = "ACTIVE"
+        }
+      }
+    }
+    propensity_clv = {
+      training = {
+        schedule = {
+          state                    = "PAUSED"
+        }
+      }
+    }
+    clv = {
+      training = {
+        schedule = {
+          state                    = "ACTIVE"
+        }
+      }
+      prediction = {
+        schedule = {
+          state                    = "ACTIVE"
+        }
+      }
+    }
+    lead_score_propensity = {
+      training = {
+        schedule = {
+          state                    = "PAUSED"
+        }
+      }
+      prediction = {
+        schedule = {
+          state                    = "ACTIVE"
+        }
+      }
+    }
+  }
+
 ####################     ML MODEL VARIABLES    #################################
 
 website_url = "Customer Website URL" # i.e. "https://shop.googlemerchandisestore.com/"
diff --git a/python/activation/main.py b/python/activation/main.py
index cfa7319e..21c416bd 100644
--- a/python/activation/main.py
+++ b/python/activation/main.py
@@ -62,6 +62,7 @@ def _add_argparse_args(cls, parser):
         - purchase-propensity-15-15
         - purchase-propensity-15-7
         - churn-propensity-30-15
+        - lead-score-propensity-5-1
       activation_type_configuration: The GCS path to the configuration file for all activation types.
     """
 
@@ -110,6 +111,7 @@ def _add_argparse_args(cls, parser):
         purchase-propensity-15-15
         purchase-propensity-15-7
         churn-propensity-30-15
+        lead-score-propensity-5-1
       ''',
       required=True
     )
diff --git a/python/ga4_setup/setup.py b/python/ga4_setup/setup.py
index 658f1650..dd4c885d 100644
--- a/python/ga4_setup/setup.py
+++ b/python/ga4_setup/setup.py
@@ -276,6 +276,7 @@ def create_custom_dimensions(configuration: map):
   create_custom_dimensions_for('CLTV', ['cltv_decile'], existing_dimensions, configuration)
   create_custom_dimensions_for('Auto Audience Segmentation', ['a_a_s_prediction'], existing_dimensions, configuration)
   create_custom_dimensions_for('Churn Propensity', ['c_p_prediction', 'c_p_decile'], existing_dimensions, configuration)
+  create_custom_dimensions_for('Lead Score Propensity', ['l_s_p_prediction', 'l_s_p_decile'], existing_dimensions, configuration)
 
 
 
diff --git a/python/pipelines/automl_tabular_pl_v4.yaml b/python/pipelines/automl_tabular_pl_v4.yaml
index 4d20b803..6bdc8cfb 100644
--- a/python/pipelines/automl_tabular_pl_v4.yaml
+++ b/python/pipelines/automl_tabular_pl_v4.yaml
@@ -11151,21 +11151,21 @@ root:
         isOptional: true
         parameterType: BOOLEAN
       distill_batch_predict_machine_type:
-        defaultValue: n1-standard-16
+        defaultValue: n1-highmem-8
         description: 'The prediction server machine type for
 
           batch predict component in the model distillation.'
         isOptional: true
         parameterType: STRING
       distill_batch_predict_max_replica_count:
-        defaultValue: 25.0
+        defaultValue: 5.0
         description: 'The max number of prediction server
 
           for batch predict component in the model distillation.'
         isOptional: true
         parameterType: NUMBER_INTEGER
       distill_batch_predict_starting_replica_count:
-        defaultValue: 25.0
+        defaultValue: 5.0
         description: 'The initial number of
 
           prediction server for batch predict component in the model distillation.'
@@ -11201,14 +11201,14 @@ root:
         isOptional: true
         parameterType: STRING
       evaluation_batch_explain_max_replica_count:
-        defaultValue: 10.0
+        defaultValue: 5.0
         description: 'The max number of prediction
 
           server for batch explain components during evaluation.'
         isOptional: true
         parameterType: NUMBER_INTEGER
       evaluation_batch_explain_starting_replica_count:
-        defaultValue: 10.0
+        defaultValue: 5.0
         description: 'The initial number of
 
           prediction server for batch explain components during evaluation.'
@@ -11222,14 +11222,14 @@ root:
         isOptional: true
         parameterType: STRING
       evaluation_batch_predict_max_replica_count:
-        defaultValue: 20.0
+        defaultValue: 5.0
         description: 'The max number of prediction
 
           server for batch predict components during evaluation.'
         isOptional: true
         parameterType: NUMBER_INTEGER
       evaluation_batch_predict_starting_replica_count:
-        defaultValue: 20.0
+        defaultValue: 5.0
         description: 'The initial number of
 
           prediction server for batch predict components during evaluation.'
@@ -11279,7 +11279,7 @@ root:
         description: The GCP region that runs the pipeline components.
         parameterType: STRING
       max_selected_features:
-        defaultValue: 1000.0
+        defaultValue: 100.0
         description: number of features to select for training.
         isOptional: true
         parameterType: NUMBER_INTEGER
@@ -11356,7 +11356,7 @@ root:
         isOptional: true
         parameterType: BOOLEAN
       stage_1_num_parallel_trials:
-        defaultValue: 35.0
+        defaultValue: 5.0
         description: Number of parallel trails for stage 1.
         isOptional: true
         parameterType: NUMBER_INTEGER
@@ -11367,7 +11367,7 @@ root:
         isOptional: true
         parameterType: LIST
       stage_2_num_parallel_trials:
-        defaultValue: 35.0
+        defaultValue: 5.0
         description: Number of parallel trails for stage 2.
         isOptional: true
         parameterType: NUMBER_INTEGER
diff --git a/python/pipelines/compiler.py b/python/pipelines/compiler.py
index 6b5224dd..97bbc62c 100644
--- a/python/pipelines/compiler.py
+++ b/python/pipelines/compiler.py
@@ -31,6 +31,7 @@
     'vertex_ai.pipelines.feature-creation-purchase-propensity.execution': "pipelines.feature_engineering_pipelines.purchase_propensity_feature_engineering_pipeline",
     'vertex_ai.pipelines.feature-creation-churn-propensity.execution': "pipelines.feature_engineering_pipelines.churn_propensity_feature_engineering_pipeline",
     'vertex_ai.pipelines.feature-creation-customer-ltv.execution': "pipelines.feature_engineering_pipelines.customer_lifetime_value_feature_engineering_pipeline",
+    'vertex_ai.pipelines.feature-creation-lead-score-propensity.execution': "pipelines.feature_engineering_pipelines.lead_score_propensity_feature_engineering_pipeline",
     'vertex_ai.pipelines.auto_segmentation.training': "pipelines.auto_segmentation_pipelines.training_pl",
     'vertex_ai.pipelines.auto_segmentation.prediction': "pipelines.auto_segmentation_pipelines.prediction_pl",
     'vertex_ai.pipelines.segmentation.training': "pipelines.segmentation_pipelines.training_pl",
@@ -39,6 +40,8 @@
     'vertex_ai.pipelines.purchase_propensity.prediction': "pipelines.tabular_pipelines.prediction_binary_classification_pl",
     'vertex_ai.pipelines.churn_propensity.training': None, # tabular workflows pipelines is precompiled
     'vertex_ai.pipelines.churn_propensity.prediction': "pipelines.tabular_pipelines.prediction_binary_classification_pl",
+    'vertex_ai.pipelines.lead_score_propensity.training': None, # tabular workflows pipelines is precompiled
+    'vertex_ai.pipelines.lead_score_propensity.prediction': "pipelines.tabular_pipelines.prediction_binary_classification_pl",
     'vertex_ai.pipelines.propensity_clv.training': None, # tabular workflows pipelines is precompiled
     'vertex_ai.pipelines.clv.training': None, # tabular workflows pipelines is precompiled
     'vertex_ai.pipelines.clv.prediction': "pipelines.tabular_pipelines.prediction_binary_classification_regression_pl",
diff --git a/python/pipelines/feature_engineering_pipelines.py b/python/pipelines/feature_engineering_pipelines.py
index deb7b88b..a15ffa12 100644
--- a/python/pipelines/feature_engineering_pipelines.py
+++ b/python/pipelines/feature_engineering_pipelines.py
@@ -196,8 +196,73 @@ def audience_segmentation_feature_engineering_pipeline(
         location=location,
         query=query_audience_segmentation_inference_preparation,
         timeout=timeout).set_display_name('audience_segmentation_inference_preparation').after(*phase_1)
-  
-      
+
+
+@dsl.pipeline()
+def lead_score_propensity_feature_engineering_pipeline(
+    project_id: str,
+    location: Optional[str],
+    query_lead_score_propensity_label: str,
+    query_user_dimensions: str,
+    query_user_rolling_window_metrics: str,
+    query_lead_score_propensity_inference_preparation: str,
+    query_lead_score_propensity_training_preparation: str,
+    timeout: Optional[float] = 3600.0
+):
+    """
+    This pipeline defines the steps for feature engineering for the lead score propensity model.
+
+    Args:
+        project_id: The Google Cloud project ID.
+        location: The Google Cloud region where the pipeline will be run.
+        query_lead_score_propensity_label: The SQL query that will be used to calculate the purchase propensity label.
+        query_user_dimensions: The SQL query that will be used to calculate the user dimensions.
+        query_user_rolling_window_metrics: The SQL query that will be used to calculate the user rolling window metrics.
+        query_lead_score_propensity_inference_preparation: The SQL query that will be used to prepare the inference data.
+        query_lead_score_propensity_training_preparation: The SQL query that will be used to prepare the training data.
+        timeout: The timeout for the pipeline in seconds.
+
+    Returns:
+        None
+    """
+
+    # Features Preparation
+    phase_1 = list()
+    phase_1.append(
+        sp(
+            project=project_id,
+            location=location,
+            query=query_lead_score_propensity_label,
+            timeout=timeout).set_display_name('lead_score_propensity_label')
+    )
+    phase_1.append(
+        sp(
+            project=project_id,
+            location=location,
+            query=query_user_dimensions,
+            timeout=timeout).set_display_name('user_dimensions')
+    )
+    phase_1.append(
+        sp(
+            project=project_id,
+            location=location,
+            query=query_user_rolling_window_metrics,
+            timeout=timeout).set_display_name('user_rolling_window_metrics')
+    )
+    # Training data preparation
+    purchase_propensity_train_prep = sp(
+        project=project_id,
+        location=location,
+        query=query_lead_score_propensity_training_preparation,
+        timeout=timeout).set_display_name('lead_score_propensity_training_preparation').after(*phase_1)
+    # Inference data preparation
+    purchase_propensity_inf_prep = sp(
+        project=project_id,
+        location=location,
+        query=query_lead_score_propensity_inference_preparation,
+        timeout=timeout).set_display_name('lead_score_propensity_inference_preparation').after(*phase_1)
+
+
 @dsl.pipeline()
 def purchase_propensity_feature_engineering_pipeline(
     project_id: str,
diff --git a/python/pipelines/scheduler.py b/python/pipelines/scheduler.py
index ce554ddd..7e00dc8e 100644
--- a/python/pipelines/scheduler.py
+++ b/python/pipelines/scheduler.py
@@ -37,8 +37,11 @@ def check_extention(file_path: str, type: str = '.yaml'):
     'vertex_ai.pipelines.feature-creation-purchase-propensity.execution': "pipelines.feature_engineering_pipelines.purchase_propensity_feature_engineering_pipeline",
     'vertex_ai.pipelines.feature-creation-churn-propensity.execution': "pipelines.feature_engineering_pipelines.churn_propensity_feature_engineering_pipeline",
     'vertex_ai.pipelines.feature-creation-customer-ltv.execution': "pipelines.feature_engineering_pipelines.customer_lifetime_value_feature_engineering_pipeline",
+    'vertex_ai.pipelines.feature-creation-lead-score-propensity.execution': "pipelines.feature_engineering_pipelines.lead_score_propensity_feature_engineering_pipeline",
     'vertex_ai.pipelines.purchase_propensity.training': None,  # tabular workflows pipelines is precompiled
     'vertex_ai.pipelines.purchase_propensity.prediction': "pipelines.tabular_pipelines.prediction_binary_classification_pl",
+    'vertex_ai.pipelines.lead_score_propensity.training': None, # tabular workflows pipelines is precompiled
+    'vertex_ai.pipelines.lead_score_propensity.prediction': "pipelines.tabular_pipelines.prediction_binary_classification_pl",
     'vertex_ai.pipelines.churn_propensity.training': None, # tabular workflows pipelines is precompiled
     'vertex_ai.pipelines.churn_propensity.prediction': "pipelines.tabular_pipelines.prediction_binary_classification_pl",
     'vertex_ai.pipelines.segmentation.training': "pipelines.segmentation_pipelines.training_pl",
diff --git a/python/pipelines/transformations-lead-score-propensity.json b/python/pipelines/transformations-lead-score-propensity.json
new file mode 100644
index 00000000..28ca5e70
--- /dev/null
+++ b/python/pipelines/transformations-lead-score-propensity.json
@@ -0,0 +1,368 @@
+[
+	{
+	  "numeric": {
+		"column_name": "user_ltv_revenue",
+		"invalid_values_allowed": true
+	  }
+	},
+	{
+	  "categorical": {
+		"column_name": "device_category"
+	  }
+	},
+	{
+	  "categorical": {
+		"column_name": "device_mobile_brand_name"
+	  }
+	},
+	{
+	  "categorical": {
+		"column_name": "device_mobile_model_name"
+	  }
+	},
+	{
+	  "categorical": {
+		"column_name": "device_os"
+	  }
+	},
+	{
+	  "categorical": {
+		"column_name": "device_language"
+	  }
+	},
+	{
+	  "categorical": {
+		"column_name": "device_web_browser"
+	  }
+	},
+	{
+	  "categorical": {
+		"column_name": "geo_sub_continent"
+	  }
+	},
+	{
+	  "categorical": {
+		"column_name": "geo_country"
+	  }
+	},
+	{
+	  "categorical": {
+		"column_name": "geo_region"
+	  }
+	},
+	{
+	  "categorical": {
+		"column_name": "geo_city"
+	  }
+	},
+	{
+	  "categorical": {
+		"column_name": "geo_metro"
+	  }
+	},
+	{
+	  "categorical": {
+		"column_name": "last_traffic_source_medium"
+	  }
+	},
+	{
+	  "categorical": {
+		"column_name": "last_traffic_source_name"
+	  }
+	},
+	{
+	  "categorical": {
+		"column_name": "last_traffic_source_source"
+	  }
+	},
+	{
+	  "categorical": {
+		"column_name": "first_traffic_source_medium"
+	  }
+	},
+	{
+	  "categorical": {
+		"column_name": "first_traffic_source_name"
+	  }
+	},
+	{
+	  "categorical": {
+		"column_name": "first_traffic_source_source"
+	  }
+	},
+	{
+	  "categorical": {
+		"column_name": "has_signed_in_with_user_id"
+	  }
+	},
+	{
+	  "numeric": {
+		"column_name": "scroll_50_past_1_day",
+		"invalid_values_allowed": true
+	  }
+	},
+	{
+	  "numeric": {
+		"column_name": "scroll_50_past_2_day",
+		"invalid_values_allowed": true
+	  }
+	},
+	{
+	  "numeric": {
+		"column_name": "scroll_50_past_3_day",
+		"invalid_values_allowed": true
+	  }
+	},
+	{
+	  "numeric": {
+		"column_name": "scroll_50_past_4_day",
+		"invalid_values_allowed": true
+	  }
+	},
+	{
+	  "numeric": {
+		"column_name": "scroll_50_past_5_day",
+		"invalid_values_allowed": true
+	  }
+	},
+	{
+	  "numeric": {
+		"column_name": "scroll_90_past_1_day",
+		"invalid_values_allowed": true
+	  }
+	},
+	{
+	  "numeric": {
+		"column_name": "scroll_90_past_2_day",
+		"invalid_values_allowed": true
+	  }
+	},
+	{
+	  "numeric": {
+		"column_name": "scroll_90_past_3_day",
+		"invalid_values_allowed": true
+	  }
+	},
+	{
+	  "numeric": {
+		"column_name": "scroll_90_past_4_day",
+		"invalid_values_allowed": true
+	  }
+	},
+	{
+	  "numeric": {
+		"column_name": "scroll_90_past_5_day",
+		"invalid_values_allowed": true
+	  }
+	},
+	{
+	  "numeric": {
+		"column_name": "view_search_results_past_1_day",
+		"invalid_values_allowed": true
+	  }
+	},
+	{
+	  "numeric": {
+		"column_name": "view_search_results_past_2_day",
+		"invalid_values_allowed": true
+	  }
+	},
+	{
+	  "numeric": {
+		"column_name": "view_search_results_past_3_day",
+		"invalid_values_allowed": true
+	  }
+	},
+	{
+	  "numeric": {
+		"column_name": "view_search_results_past_4_day",
+		"invalid_values_allowed": true
+	  }
+	},
+	{
+	  "numeric": {
+		"column_name": "view_search_results_past_5_day",
+		"invalid_values_allowed": true
+	  }
+	},
+	{
+	  "numeric": {
+		"column_name": "file_download_past_1_day",
+		"invalid_values_allowed": true
+	  }
+	},
+	{
+	  "numeric": {
+		"column_name": "file_download_past_2_day",
+		"invalid_values_allowed": true
+	  }
+	},
+	{
+	  "numeric": {
+		"column_name": "file_download_past_3_day",
+		"invalid_values_allowed": true
+	  }
+	},
+	{
+	  "numeric": {
+		"column_name": "file_download_past_4_day",
+		"invalid_values_allowed": true
+	  }
+	},
+	{
+	  "numeric": {
+		"column_name": "file_download_past_5_day",
+		"invalid_values_allowed": true
+	  }
+	},
+	{
+	  "numeric": {
+		"column_name": "recipe_add_to_list_past_1_day",
+		"invalid_values_allowed": true
+	  }
+	},
+	{
+	  "numeric": {
+		"column_name": "recipe_add_to_list_past_2_day",
+		"invalid_values_allowed": true
+	  }
+	},
+	{
+	  "numeric": {
+		"column_name": "recipe_add_to_list_past_3_day",
+		"invalid_values_allowed": true
+	  }
+	},
+	{
+	  "numeric": {
+		"column_name": "recipe_add_to_list_past_4_day",
+		"invalid_values_allowed": true
+	  }
+	},
+	{
+	  "numeric": {
+		"column_name": "recipe_add_to_list_past_5_day",
+		"invalid_values_allowed": true
+	  }
+	},
+	{
+    "numeric": {
+      "column_name": "recipe_print_past_1_day",
+      "invalid_values_allowed": true
+    }
+  },
+  {
+    "numeric": {
+      "column_name": "recipe_print_past_2_day",
+      "invalid_values_allowed": true
+    }
+  },
+  {
+    "numeric": {
+      "column_name": "recipe_print_past_3_day",
+      "invalid_values_allowed": true
+    }
+  },
+  {
+    "numeric": {
+      "column_name": "recipe_print_past_4_day",
+      "invalid_values_allowed": true
+    }
+  },
+  {
+    "numeric": {
+      "column_name": "recipe_print_past_5_day",
+      "invalid_values_allowed": true
+    }
+  },
+  {
+    "numeric": {
+      "column_name": "sign_up_past_1_day",
+      "invalid_values_allowed": true
+    }
+  },
+  {
+    "numeric": {
+      "column_name": "sign_up_past_2_day",
+      "invalid_values_allowed": true
+    }
+  },
+  {
+    "numeric": {
+      "column_name": "sign_up_past_3_day",
+      "invalid_values_allowed": true
+    }
+  },
+  {
+    "numeric": {
+      "column_name": "sign_up_past_4_day",
+      "invalid_values_allowed": true
+    }
+  },
+  {
+    "numeric": {
+      "column_name": "sign_up_past_5_day",
+      "invalid_values_allowed": true
+    }
+  },
+  {
+    "numeric": {
+      "column_name": "recipe_favorite_past_1_day",
+      "invalid_values_allowed": true
+      }
+    },
+  {
+    "numeric": {
+      "column_name": "recipe_favorite_past_2_day",
+      "invalid_values_allowed": true
+      }
+    },
+  {
+    "numeric": {
+      "column_name": "recipe_favorite_past_3_day",
+      "invalid_values_allowed": true
+      }
+    },
+  {
+    "numeric": {
+      "column_name": "recipe_favorite_past_4_day",
+      "invalid_values_allowed": true
+      }
+    },
+  {
+    "numeric": {
+      "column_name": "recipe_favorite_past_5_day",
+      "invalid_values_allowed": true
+      }
+    },
+  {
+    "numeric": {
+      "column_name": "recipe_add_to_menu_past_1_day",
+      "invalid_values_allowed": true
+      }
+    },
+    {
+    "numeric": {
+      "column_name": "recipe_add_to_menu_past_2_day",
+      "invalid_values_allowed": true
+      }
+    },
+    {
+    "numeric": {
+      "column_name": "recipe_add_to_menu_past_3_day",
+      "invalid_values_allowed": true
+      }
+    },
+    {
+    "numeric": {
+      "column_name": "recipe_add_to_menu_past_4_day",
+      "invalid_values_allowed": true
+      }
+    },
+    {
+    "numeric": {
+      "column_name": "recipe_add_to_menu_past_5_day",
+      "invalid_values_allowed": true
+      }
+    }
+]
\ No newline at end of file
diff --git a/sql/procedure/lead_score_propensity_inference_preparation.sqlx b/sql/procedure/lead_score_propensity_inference_preparation.sqlx
new file mode 100644
index 00000000..ec12d7fe
--- /dev/null
+++ b/sql/procedure/lead_score_propensity_inference_preparation.sqlx
@@ -0,0 +1,672 @@
+-- Copyright 2023 Google LLC
+--
+-- Licensed under the Apache License, Version 2.0 (the "License");
+-- you may not use this file except in compliance with the License.
+-- You may obtain a copy of the License at
+--
+--     http://www.apache.org/licenses/LICENSE-2.0
+--
+-- Unless required by applicable law or agreed to in writing, software
+-- distributed under the License is distributed on an "AS IS" BASIS,
+-- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+-- See the License for the specific language governing permissions and
+-- limitations under the License.
+
+DECLARE lastest_processed_time_ud TIMESTAMP;
+DECLARE lastest_processed_time_useam TIMESTAMP;
+DECLARE lastest_processed_time_uwlm TIMESTAMP;
+DECLARE lastest_processed_time_um TIMESTAMP;
+
+-- Setting procedure to lookback from the day before `inference_date`
+SET inference_date = DATE_SUB(inference_date, INTERVAL 1 DAY);
+
+SET lastest_processed_time_ud = (SELECT MAX(processed_timestamp) FROM `{{feature_store_project_id}}.{{feature_store_dataset}}.user_dimensions` WHERE feature_date = inference_date LIMIT 1);
+SET lastest_processed_time_useam = (SELECT MAX(processed_timestamp) FROM `{{feature_store_project_id}}.{{feature_store_dataset}}.user_session_event_aggregated_metrics` WHERE feature_date = inference_date LIMIT 1);
+SET lastest_processed_time_uwlm = (SELECT MAX(processed_timestamp) FROM `{{feature_store_project_id}}.{{feature_store_dataset}}.user_rolling_window_lead_metrics` WHERE feature_date = inference_date LIMIT 1);
+SET lastest_processed_time_um = (SELECT MAX(processed_timestamp) FROM `{{feature_store_project_id}}.{{feature_store_dataset}}.user_scoped_metrics` WHERE feature_date = inference_date LIMIT 1);
+
+CREATE OR REPLACE TEMP TABLE inference_preparation_ud as (
+  SELECT DISTINCT
+    -- The user pseudo id
+    UD.user_pseudo_id,
+    -- The user id 
+    MAX(UD.user_id) OVER(user_dimensions_window) AS user_id,
+    -- The feature date
+    UD.feature_date,
+    -- The user lifetime value revenue
+    MAX(UD.user_ltv_revenue) OVER(user_dimensions_window) AS user_ltv_revenue,
+    -- The device category
+    MAX(UD.device_category) OVER(user_dimensions_window) AS device_category,
+    -- The device brand name
+    MAX(UD.device_mobile_brand_name) OVER(user_dimensions_window) AS device_mobile_brand_name,
+    -- The device model name
+    MAX(UD.device_mobile_model_name) OVER(user_dimensions_window) AS device_mobile_model_name,
+    -- The device operating system
+    MAX(UD.device_os) OVER(user_dimensions_window) AS device_os,
+    -- The device language
+    MAX(UD.device_language) OVER(user_dimensions_window) AS device_language,
+    -- The device web browser
+    MAX(UD.device_web_browser) OVER(user_dimensions_window) AS device_web_browser,
+    -- The user sub continent
+    MAX(UD.geo_sub_continent) OVER(user_dimensions_window) AS geo_sub_continent,
+    -- The user country
+    MAX(UD.geo_country) OVER(user_dimensions_window) AS geo_country,
+    -- The user region
+    MAX(UD.geo_region) OVER(user_dimensions_window) AS geo_region,
+    -- The user city
+    MAX(UD.geo_city) OVER(user_dimensions_window) AS geo_city,
+    -- The user metro
+    MAX(UD.geo_metro) OVER(user_dimensions_window) AS geo_metro,
+    -- The user last traffic source medium
+    MAX(UD.last_traffic_source_medium) OVER(user_dimensions_window) AS last_traffic_source_medium,
+    -- The user last traffic source name
+    MAX(UD.last_traffic_source_name) OVER(user_dimensions_window) AS last_traffic_source_name,
+    -- The user last traffic source source
+    MAX(UD.last_traffic_source_source) OVER(user_dimensions_window) AS last_traffic_source_source,
+    -- The user first traffic source medium
+    MAX(UD.first_traffic_source_medium) OVER(user_dimensions_window) AS first_traffic_source_medium,
+    -- The user first traffic source name
+    MAX(UD.first_traffic_source_name) OVER(user_dimensions_window) AS first_traffic_source_name,
+    -- The user first traffic source source
+    MAX(UD.first_traffic_source_source) OVER(user_dimensions_window) AS first_traffic_source_source,
+    -- Whether the user has signed in with user ID
+    MAX(UD.has_signed_in_with_user_id) OVER(user_dimensions_window) AS has_signed_in_with_user_id,
+FROM
+  `{{feature_store_project_id}}.{{feature_store_dataset}}.user_dimensions` UD
+INNER JOIN
+  `{{project_id}}.{{mds_dataset}}.latest_event_per_user_last_72_hours` LEU
+ON
+  UD.user_pseudo_id = LEU.user_pseudo_id
+WHERE
+  -- In the future consider `feature_date BETWEEN start_date AND end_date`, to process multiple days. Modify Partition BY
+  UD.feature_date = inference_date 
+  AND UD.processed_timestamp = lastest_processed_time_ud
+WINDOW 
+  user_dimensions_window AS (PARTITION BY UD.user_pseudo_id ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)
+);
+
+
+CREATE OR REPLACE TEMP TABLE inference_preparation_uwlm as (
+  SELECT DISTINCT
+    -- User pseudo id
+    UWLM.user_pseudo_id,
+    -- Feature date
+    UWLM.feature_date,
+    -- Calculate the maximum value for each metric over the window
+    MAX(UWLM.scroll_50_past_1_day) OVER(user_rolling_lead_window) AS scroll_50_past_1_day,
+    -- Maximum active users in the past 2 days
+    MAX(UWLM.scroll_50_past_2_day) OVER(user_rolling_lead_window) AS scroll_50_past_2_day,
+    -- Maximum active users in the past 3 days
+    MAX(UWLM.scroll_50_past_3_day) OVER(user_rolling_lead_window) AS scroll_50_past_3_day,
+    -- Maximum active users in the past 4 days
+    MAX(UWLM.scroll_50_past_4_day) OVER(user_rolling_lead_window) AS scroll_50_past_4_day,
+    -- Maximum active users in the past 5 days
+    MAX(UWLM.scroll_50_past_5_day) OVER(user_rolling_lead_window) AS scroll_50_past_5_day,
+    -- Maximum scroll_90 in the past 1 day
+    MAX(UWLM.scroll_90_past_1_day) OVER(user_rolling_lead_window) AS scroll_90_past_1_day,
+    -- Maximum scroll_90 in the past 2 days
+    MAX(UWLM.scroll_90_past_2_day) OVER(user_rolling_lead_window) AS scroll_90_past_2_day,
+    -- Maximum scroll_90 in the past 3 days
+    MAX(UWLM.scroll_90_past_3_day) OVER(user_rolling_lead_window) AS scroll_90_past_3_day,
+    -- Maximum scroll_90 in the past 4 days
+    MAX(UWLM.scroll_90_past_4_day) OVER(user_rolling_lead_window) AS scroll_90_past_4_day,
+    -- Maximum scroll_90 in the past 5 days
+    MAX(UWLM.scroll_90_past_5_day) OVER(user_rolling_lead_window) AS scroll_90_past_5_day,
+    -- Maximum view_search_results in the past 1 day
+    MAX(UWLM.view_search_results_past_1_day) OVER(user_rolling_lead_window) AS view_search_results_past_1_day,
+    -- Maximum view_search_results in the past 2 days
+    MAX(UWLM.view_search_results_past_2_day) OVER(user_rolling_lead_window) AS view_search_results_past_2_day,
+    -- Maximum view_search_results in the past 3 days
+    MAX(UWLM.view_search_results_past_3_day) OVER(user_rolling_lead_window) AS view_search_results_past_3_day,
+    -- Maximum view_search_results in the past 4 days
+    MAX(UWLM.view_search_results_past_4_day) OVER(user_rolling_lead_window) AS view_search_results_past_4_day,
+    -- Maximum view_search_results in the past 5 days
+    MAX(UWLM.view_search_results_past_5_day) OVER(user_rolling_lead_window) AS view_search_results_past_5_day,
+    -- Maximum recipe_add_to_list in the past 1 day
+    MAX(UWLM.recipe_add_to_list_past_1_day) OVER(user_rolling_lead_window) AS recipe_add_to_list_past_1_day,
+    -- Maximum recipe_add_to_list in the past 2 days
+    MAX(UWLM.recipe_add_to_list_past_2_day) OVER(user_rolling_lead_window) AS recipe_add_to_list_past_2_day,
+    -- Maximum recipe_add_to_list in the past 3 days
+    MAX(UWLM.recipe_add_to_list_past_3_day) OVER(user_rolling_lead_window) AS recipe_add_to_list_past_3_day,
+    -- Maximum recipe_add_to_list in the past 4 days
+    MAX(UWLM.recipe_add_to_list_past_4_day) OVER(user_rolling_lead_window) AS recipe_add_to_list_past_4_day,
+    -- Maximum recipe_add_to_list in the past 5 days
+    MAX(UWLM.recipe_add_to_list_past_5_day) OVER(user_rolling_lead_window) AS recipe_add_to_list_past_5_day,
+    -- Maximum recipe_print in the past 1 day
+    MAX(UWLM.recipe_print_past_1_day) OVER(user_rolling_lead_window) AS recipe_print_past_1_day,
+    -- Maximum recipe_print in the past 2 days
+    MAX(UWLM.recipe_print_past_2_day) OVER(user_rolling_lead_window) AS recipe_print_past_2_day,
+    -- Maximum recipe_print in the past 3 days
+    MAX(UWLM.recipe_print_past_3_day) OVER(user_rolling_lead_window) AS recipe_print_past_3_day,
+    -- Maximum recipe_print in the past 4 days
+    MAX(UWLM.recipe_print_past_4_day) OVER(user_rolling_lead_window) AS recipe_print_past_4_day,
+    -- Maximum recipe_print in the past 5 days
+    MAX(UWLM.recipe_print_past_5_day) OVER(user_rolling_lead_window) AS recipe_print_past_5_day,
+    -- Maximum sign_up_past in the past 1 day
+    MAX(UWLM.sign_up_past_1_day) OVER(user_rolling_lead_window) AS sign_up_past_1_day,
+    -- Maximum sign_up_past in the past 2 days
+    MAX(UWLM.sign_up_past_2_day) OVER(user_rolling_lead_window) AS sign_up_past_2_day,
+    -- Maximum sign_up_past in the past 3 days
+    MAX(UWLM.sign_up_past_3_day) OVER(user_rolling_lead_window) AS sign_up_past_3_day,
+    -- Maximum sign_up_past in the past 4 days
+    MAX(UWLM.sign_up_past_4_day) OVER(user_rolling_lead_window) AS sign_up_past_4_day,
+    -- Maximum sign_up_past in the past 5 days
+    MAX(UWLM.sign_up_past_5_day) OVER(user_rolling_lead_window) AS sign_up_past_5_day,
+    -- Maximum recipe_favorite in the past 1 day
+    MAX(UWLM.recipe_favorite_past_1_day) OVER(user_rolling_lead_window) AS recipe_favorite_past_1_day,
+    -- Maximum recipe_favorite in the past 2 days
+    MAX(UWLM.recipe_favorite_past_2_day) OVER(user_rolling_lead_window) AS recipe_favorite_past_2_day,
+    -- Maximum recipe_favorite in the past 3 days
+    MAX(UWLM.recipe_favorite_past_3_day) OVER(user_rolling_lead_window) AS recipe_favorite_past_3_day,
+    -- Maximum recipe_favorite in the past 4 days
+    MAX(UWLM.recipe_favorite_past_4_day) OVER(user_rolling_lead_window) AS recipe_favorite_past_4_day,
+    -- Maximum recipe_favorite in the past 5 days
+    MAX(UWLM.recipe_favorite_past_5_day) OVER(user_rolling_lead_window) AS recipe_favorite_past_5_day,
+    -- Maximum recipe_add_to_menu in the past 1 day
+    MAX(UWLM.recipe_add_to_menu_past_1_day) OVER(user_rolling_lead_window) AS recipe_add_to_menu_past_1_day,
+    -- Maximum recipe_add_to_menu in the past 2 days
+    MAX(UWLM.recipe_add_to_menu_past_2_day) OVER(user_rolling_lead_window) AS recipe_add_to_menu_past_2_day,
+    -- Maximum recipe_add_to_menu in the past 3 days
+    MAX(UWLM.recipe_add_to_menu_past_3_day) OVER(user_rolling_lead_window) AS recipe_add_to_menu_past_3_day,
+    -- Maximum recipe_add_to_menu in the past 4 days
+    MAX(UWLM.recipe_add_to_menu_past_4_day) OVER(user_rolling_lead_window) AS recipe_add_to_menu_past_4_day,
+    -- Maximum recipe_add_to_menu in the past 5 days
+    MAX(UWLM.recipe_add_to_menu_past_5_day) OVER(user_rolling_lead_window) AS recipe_add_to_menu_past_5_day,
+FROM
+  `{{feature_store_project_id}}.{{feature_store_dataset}}.user_rolling_window_lead_metrics` UWLM
+INNER JOIN
+  `{{project_id}}.{{mds_dataset}}.latest_event_per_user_last_72_hours` LEU
+ON
+  UWLM.user_pseudo_id = LEU.user_pseudo_id
+WHERE
+  -- Filter for the features in the inferecen date
+  UWLM.feature_date = inference_date
+  AND UWLM.processed_timestamp = lastest_processed_time_uwlm
+WINDOW 
+  user_rolling_lead_window AS (PARTITION BY UWLM.user_pseudo_id ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)
+);
+
+-- This is a temp table consolidating all features over the dates intervals.
+CREATE OR REPLACE TEMP TABLE inference_preparation as (
+  SELECT DISTINCT
+    UD.user_pseudo_id,
+    UD.user_id,
+    UD.feature_date,
+    UD.user_ltv_revenue,
+    UD.device_category,
+    UD.device_mobile_brand_name,
+    UD.device_mobile_model_name,
+    UD.device_os,
+    UD.device_language,
+    UD.device_web_browser,
+    UD.geo_sub_continent,
+    UD.geo_country,
+    UD.geo_region,
+    UD.geo_city,
+    UD.geo_metro,
+    UD.last_traffic_source_medium,
+    UD.last_traffic_source_name,
+    UD.last_traffic_source_source,
+    UD.first_traffic_source_medium,
+    UD.first_traffic_source_name,
+    UD.first_traffic_source_source,
+    UD.has_signed_in_with_user_id,
+    UWLM.scroll_50_past_1_day,
+    UWLM.scroll_50_past_2_day,
+    UWLM.scroll_50_past_3_day,
+    UWLM.scroll_50_past_4_day,
+    UWLM.scroll_50_past_5_day,
+    UWLM.scroll_90_past_1_day,
+    UWLM.scroll_90_past_2_day,
+    UWLM.scroll_90_past_3_day,
+    UWLM.scroll_90_past_4_day,
+    UWLM.scroll_90_past_5_day,
+    UWLM.view_search_results_past_1_day,
+    UWLM.view_search_results_past_2_day,
+    UWLM.view_search_results_past_3_day,
+    UWLM.view_search_results_past_4_day,
+    UWLM.view_search_results_past_5_day,
+    UWLM.recipe_add_to_list_past_1_day,
+    UWLM.recipe_add_to_list_past_2_day,
+    UWLM.recipe_add_to_list_past_3_day,
+    UWLM.recipe_add_to_list_past_4_day,
+    UWLM.recipe_add_to_list_past_5_day,
+    UWLM.recipe_print_past_1_day,
+    UWLM.recipe_print_past_2_day,
+    UWLM.recipe_print_past_3_day,
+    UWLM.recipe_print_past_4_day,
+    UWLM.recipe_print_past_5_day,
+    UWLM.sign_up_past_1_day,
+    UWLM.sign_up_past_2_day,
+    UWLM.sign_up_past_3_day,
+    UWLM.sign_up_past_4_day,
+    UWLM.sign_up_past_5_day,
+    UWLM.recipe_favorite_past_1_day,
+    UWLM.recipe_favorite_past_2_day,
+    UWLM.recipe_favorite_past_3_day,
+    UWLM.recipe_favorite_past_4_day,
+    UWLM.recipe_favorite_past_5_day,
+    UWLM.recipe_add_to_menu_past_1_day,
+    UWLM.recipe_add_to_menu_past_2_day,
+    UWLM.recipe_add_to_menu_past_3_day,
+    UWLM.recipe_add_to_menu_past_4_day,
+    UWLM.recipe_add_to_menu_past_5_day
+FROM
+  inference_preparation_ud UD
+INNER JOIN
+  inference_preparation_uwlm UWLM
+ON
+  UWLM.user_pseudo_id = UD.user_pseudo_id
+  AND UWLM.feature_date = UD.feature_date
+);
+
+DELETE FROM `{{project_id}}.{{dataset}}.{{insert_table}}` WHERE TRUE;
+
+INSERT INTO `{{project_id}}.{{dataset}}.{{insert_table}}`
+(
+  feature_date,
+  user_pseudo_id,
+  user_id,
+  user_ltv_revenue,
+  device_category,
+  device_mobile_brand_name,
+  device_mobile_model_name,
+  device_os,
+  device_language,
+  device_web_browser,
+  geo_sub_continent,
+  geo_country,
+  geo_region,
+  geo_city,
+  geo_metro,
+  last_traffic_source_medium,
+  last_traffic_source_name,
+  last_traffic_source_source,
+  first_traffic_source_medium,
+  first_traffic_source_name,
+  first_traffic_source_source,
+  has_signed_in_with_user_id,
+  scroll_50_past_1_day,
+  scroll_50_past_2_day,
+  scroll_50_past_3_day,
+  scroll_50_past_4_day,
+  scroll_50_past_5_day,
+  scroll_90_past_1_day,
+  scroll_90_past_2_day,
+  scroll_90_past_3_day,
+  scroll_90_past_4_day,
+  scroll_90_past_5_day,
+  view_search_results_past_1_day,
+  view_search_results_past_2_day,
+  view_search_results_past_3_day,
+  view_search_results_past_4_day,
+  view_search_results_past_5_day,
+  recipe_add_to_list_past_1_day,
+  recipe_add_to_list_past_2_day,
+  recipe_add_to_list_past_3_day,
+  recipe_add_to_list_past_4_day,
+  recipe_add_to_list_past_5_day,
+  recipe_print_past_1_day,
+  recipe_print_past_2_day,
+  recipe_print_past_3_day,
+  recipe_print_past_4_day,
+  recipe_print_past_5_day,
+  sign_up_past_1_day,
+  sign_up_past_2_day,
+  sign_up_past_3_day,
+  sign_up_past_4_day,
+  sign_up_past_5_day,
+  recipe_favorite_past_1_day,
+  recipe_favorite_past_2_day,
+  recipe_favorite_past_3_day,
+  recipe_favorite_past_4_day,
+  recipe_favorite_past_5_day,
+  recipe_add_to_menu_past_1_day,
+  recipe_add_to_menu_past_2_day,
+  recipe_add_to_menu_past_3_day,
+  recipe_add_to_menu_past_4_day,
+  recipe_add_to_menu_past_5_day
+)
+SELECT DISTINCT 
+feature_date,
+  user_pseudo_id,
+  user_id,
+  MIN(user_ltv_revenue) OVER(PARTITION BY user_pseudo_id, feature_date) as user_ltv_revenue,
+  device_category,
+  device_mobile_brand_name,
+  device_mobile_model_name,
+  device_os,
+  device_language,
+  device_web_browser,
+  geo_sub_continent,
+  geo_country,
+  geo_region,
+  geo_city,
+  geo_metro,
+  last_traffic_source_medium,
+  last_traffic_source_name,
+  last_traffic_source_source,
+  first_traffic_source_medium,
+  first_traffic_source_name,
+  first_traffic_source_source,
+  has_signed_in_with_user_id,
+  scroll_50_past_1_day,
+  scroll_50_past_2_day,
+  scroll_50_past_3_day,
+  scroll_50_past_4_day,
+  scroll_50_past_5_day,
+  scroll_90_past_1_day,
+  scroll_90_past_2_day,
+  scroll_90_past_3_day,
+  scroll_90_past_4_day,
+  scroll_90_past_5_day,
+  view_search_results_past_1_day,
+  view_search_results_past_2_day,
+  view_search_results_past_3_day,
+  view_search_results_past_4_day,
+  view_search_results_past_5_day,
+  recipe_add_to_list_past_1_day,
+  recipe_add_to_list_past_2_day,
+  recipe_add_to_list_past_3_day,
+  recipe_add_to_list_past_4_day,
+  recipe_add_to_list_past_5_day,
+  recipe_print_past_1_day,
+  recipe_print_past_2_day,
+  recipe_print_past_3_day,
+  recipe_print_past_4_day,
+  recipe_print_past_5_day,
+  sign_up_past_1_day,
+  sign_up_past_2_day,
+  sign_up_past_3_day,
+  sign_up_past_4_day,
+  sign_up_past_5_day,
+  recipe_favorite_past_1_day,
+  recipe_favorite_past_2_day,
+  recipe_favorite_past_3_day,
+  recipe_favorite_past_4_day,
+  recipe_favorite_past_5_day,
+  recipe_add_to_menu_past_1_day,
+  recipe_add_to_menu_past_2_day,
+  recipe_add_to_menu_past_3_day,
+  recipe_add_to_menu_past_4_day,
+  recipe_add_to_menu_past_5_day
+FROM inference_preparation;
+
+
+CREATE OR REPLACE TABLE `{{project_id}}.{{dataset}}.lead_score_propensity_inference_5_1` AS(
+  SELECT DISTINCT
+  CURRENT_TIMESTAMP() AS processed_timestamp,
+  feature_date,
+  user_pseudo_id,
+  LAST_VALUE(user_id) OVER(PARTITION BY user_pseudo_id ORDER BY feature_date DESC) AS user_id,
+  LAST_VALUE(user_ltv_revenue) OVER(PARTITION BY user_pseudo_id ORDER BY feature_date DESC) AS user_ltv_revenue,
+  LAST_VALUE(device_category) OVER(PARTITION BY user_pseudo_id ORDER BY feature_date DESC) AS device_category,
+  LAST_VALUE(device_mobile_brand_name) OVER(PARTITION BY user_pseudo_id ORDER BY feature_date DESC) AS device_mobile_brand_name,
+  LAST_VALUE(device_mobile_model_name) OVER(PARTITION BY user_pseudo_id ORDER BY feature_date DESC) AS device_mobile_model_name,
+  LAST_VALUE(device_os) OVER(PARTITION BY user_pseudo_id ORDER BY feature_date DESC) AS device_os,
+  LAST_VALUE(device_language) OVER(PARTITION BY user_pseudo_id ORDER BY feature_date DESC) AS device_language,
+  LAST_VALUE(device_web_browser) OVER(PARTITION BY user_pseudo_id ORDER BY feature_date DESC) AS device_web_browser,
+  LAST_VALUE(geo_sub_continent) OVER(PARTITION BY user_pseudo_id ORDER BY feature_date DESC) AS geo_sub_continent,
+  LAST_VALUE(geo_country) OVER(PARTITION BY user_pseudo_id ORDER BY feature_date DESC) AS geo_country,
+  LAST_VALUE(geo_region) OVER(PARTITION BY user_pseudo_id ORDER BY feature_date DESC) AS geo_region,
+  LAST_VALUE(geo_city) OVER(PARTITION BY user_pseudo_id ORDER BY feature_date DESC) AS geo_city,
+  LAST_VALUE(geo_metro) OVER(PARTITION BY user_pseudo_id ORDER BY feature_date DESC) AS geo_metro,
+  LAST_VALUE(last_traffic_source_medium) OVER(PARTITION BY user_pseudo_id ORDER BY feature_date DESC) AS last_traffic_source_medium,
+  LAST_VALUE(last_traffic_source_name) OVER(PARTITION BY user_pseudo_id ORDER BY feature_date DESC) AS last_traffic_source_name,
+  LAST_VALUE(last_traffic_source_source) OVER(PARTITION BY user_pseudo_id ORDER BY feature_date DESC) AS last_traffic_source_source,
+  LAST_VALUE(first_traffic_source_medium) OVER(PARTITION BY user_pseudo_id ORDER BY feature_date DESC) AS first_traffic_source_medium,
+  LAST_VALUE(first_traffic_source_name) OVER(PARTITION BY user_pseudo_id ORDER BY feature_date DESC) AS first_traffic_source_name,
+  LAST_VALUE(first_traffic_source_source) OVER(PARTITION BY user_pseudo_id ORDER BY feature_date DESC) AS first_traffic_source_source,
+  LAST_VALUE(has_signed_in_with_user_id) OVER(PARTITION BY user_pseudo_id ORDER BY feature_date DESC) AS has_signed_in_with_user_id,
+  LAST_VALUE(scroll_50_past_1_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS scroll_50_past_1_day,
+  LAST_VALUE(scroll_50_past_2_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS scroll_50_past_2_day,
+  LAST_VALUE(scroll_50_past_3_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS scroll_50_past_3_day,
+  LAST_VALUE(scroll_50_past_4_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS scroll_50_past_4_day,
+  LAST_VALUE(scroll_50_past_5_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS scroll_50_past_5_day,
+  LAST_VALUE(scroll_90_past_1_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS scroll_90_past_1_day,
+  LAST_VALUE(scroll_90_past_2_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS scroll_90_past_2_day,
+  LAST_VALUE(scroll_90_past_3_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS scroll_90_past_3_day,
+  LAST_VALUE(scroll_90_past_4_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS scroll_90_past_4_day,
+  LAST_VALUE(scroll_90_past_5_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS scroll_90_past_5_day,
+  LAST_VALUE(view_search_results_past_1_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS view_search_results_past_1_day,
+  LAST_VALUE(view_search_results_past_2_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS view_search_results_past_2_day,
+  LAST_VALUE(view_search_results_past_3_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS view_search_results_past_3_day,
+  LAST_VALUE(view_search_results_past_4_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS view_search_results_past_4_day,
+  LAST_VALUE(view_search_results_past_5_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS view_search_results_past_5_day,
+  LAST_VALUE(recipe_add_to_list_past_1_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS recipe_add_to_list_past_1_day,
+  LAST_VALUE(recipe_add_to_list_past_2_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS recipe_add_to_list_past_2_day,
+  LAST_VALUE(recipe_add_to_list_past_3_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS recipe_add_to_list_past_3_day,
+  LAST_VALUE(recipe_add_to_list_past_4_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS recipe_add_to_list_past_4_day,
+  LAST_VALUE(recipe_add_to_list_past_5_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS recipe_add_to_list_past_5_day,
+  LAST_VALUE(recipe_print_past_1_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS recipe_print_past_1_day,
+  LAST_VALUE(recipe_print_past_2_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS recipe_print_past_2_day,
+  LAST_VALUE(recipe_print_past_3_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS recipe_print_past_3_day,
+  LAST_VALUE(recipe_print_past_4_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS recipe_print_past_4_day,
+  LAST_VALUE(recipe_print_past_5_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS recipe_print_past_5_day,
+  LAST_VALUE(sign_up_past_1_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS sign_up_past_1_day,
+  LAST_VALUE(sign_up_past_2_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS sign_up_past_2_day,
+  LAST_VALUE(sign_up_past_3_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS sign_up_past_3_day,
+  LAST_VALUE(sign_up_past_4_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS sign_up_past_4_day,
+  LAST_VALUE(sign_up_past_5_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS sign_up_past_5_day,
+  LAST_VALUE(recipe_favorite_past_1_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS recipe_favorite_past_1_day,
+  LAST_VALUE(recipe_favorite_past_2_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS recipe_favorite_past_2_day,
+  LAST_VALUE(recipe_favorite_past_3_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS recipe_favorite_past_3_day,
+  LAST_VALUE(recipe_favorite_past_4_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS recipe_favorite_past_4_day,
+  LAST_VALUE(recipe_favorite_past_5_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS recipe_favorite_past_5_day,
+  LAST_VALUE(recipe_add_to_menu_past_1_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS recipe_add_to_menu_past_1_day,
+  LAST_VALUE(recipe_add_to_menu_past_2_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS recipe_add_to_menu_past_2_day,
+  LAST_VALUE(recipe_add_to_menu_past_3_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS recipe_add_to_menu_past_3_day,
+  LAST_VALUE(recipe_add_to_menu_past_4_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS recipe_add_to_menu_past_4_day,
+  LAST_VALUE(recipe_add_to_menu_past_5_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS recipe_add_to_menu_past_5_day
+  FROM `{{project_id}}.{{dataset}}.{{insert_table}}`
+);
+
+
+CREATE OR REPLACE VIEW `{{project_id}}.{{dataset}}.v_lead_score_propensity_inference_5_1`
+(processed_timestamp, 
+  feature_date,
+  user_pseudo_id,
+  user_id,
+  user_ltv_revenue,
+  device_category,
+  device_mobile_brand_name,
+  device_mobile_model_name,
+  device_os,
+  device_language,
+  device_web_browser,
+  geo_sub_continent,
+  geo_country,
+  geo_region,
+  geo_city,
+  geo_metro,
+  last_traffic_source_medium,
+  last_traffic_source_name,
+  last_traffic_source_source,
+  first_traffic_source_medium,
+  first_traffic_source_name,
+  first_traffic_source_source,
+  has_signed_in_with_user_id,
+  scroll_50_past_1_day,
+  scroll_50_past_2_day,
+  scroll_50_past_3_day,
+  scroll_50_past_4_day,
+  scroll_50_past_5_day,
+  scroll_90_past_1_day,
+  scroll_90_past_2_day,
+  scroll_90_past_3_day,
+  scroll_90_past_4_day,
+  scroll_90_past_5_day,
+  view_search_results_past_1_day,
+  view_search_results_past_2_day,
+  view_search_results_past_3_day,
+  view_search_results_past_4_day,
+  view_search_results_past_5_day,
+  recipe_add_to_list_past_1_day,
+  recipe_add_to_list_past_2_day,
+  recipe_add_to_list_past_3_day,
+  recipe_add_to_list_past_4_day,
+  recipe_add_to_list_past_5_day,
+  recipe_print_past_1_day,
+  recipe_print_past_2_day,
+  recipe_print_past_3_day,
+  recipe_print_past_4_day,
+  recipe_print_past_5_day,
+  sign_up_past_1_day,
+  sign_up_past_2_day,
+  sign_up_past_3_day,
+  sign_up_past_4_day,
+  sign_up_past_5_day,
+  recipe_favorite_past_1_day,
+  recipe_favorite_past_2_day,
+  recipe_favorite_past_3_day,
+  recipe_favorite_past_4_day,
+  recipe_favorite_past_5_day,
+  recipe_add_to_menu_past_1_day,
+  recipe_add_to_menu_past_2_day,
+  recipe_add_to_menu_past_3_day,
+  recipe_add_to_menu_past_4_day,
+  recipe_add_to_menu_past_5_day
+  )
+OPTIONS(
+  --expiration_timestamp=TIMESTAMP_ADD(CURRENT_TIMESTAMP(), INTERVAL {{expiration_duration_hours}} HOUR),
+  friendly_name="v_lead_score_propensity_inference_5_1",
+  description="View Lead Score Propensity Inference dataset using 5 days back to predict 1 day ahead. View expires after 48h and should run daily.",
+  labels=[("org_unit", "development")]
+) AS 
+SELECT DISTINCT
+  processed_timestamp, 
+  feature_date,
+  user_pseudo_id,
+  user_id,
+  user_ltv_revenue,
+  device_category,
+  device_mobile_brand_name,
+  device_mobile_model_name,
+  device_os,
+  device_language,
+  device_web_browser,
+  geo_sub_continent,
+  geo_country,
+  geo_region,
+  geo_city,
+  geo_metro,
+  last_traffic_source_medium,
+  last_traffic_source_name,
+  last_traffic_source_source,
+  first_traffic_source_medium,
+  first_traffic_source_name,
+  first_traffic_source_source,
+  has_signed_in_with_user_id,
+  scroll_50_past_1_day,
+  scroll_50_past_2_day,
+  scroll_50_past_3_day,
+  scroll_50_past_4_day,
+  scroll_50_past_5_day,
+  scroll_90_past_1_day,
+  scroll_90_past_2_day,
+  scroll_90_past_3_day,
+  scroll_90_past_4_day,
+  scroll_90_past_5_day,
+  view_search_results_past_1_day,
+  view_search_results_past_2_day,
+  view_search_results_past_3_day,
+  view_search_results_past_4_day,
+  view_search_results_past_5_day,
+  recipe_add_to_list_past_1_day,
+  recipe_add_to_list_past_2_day,
+  recipe_add_to_list_past_3_day,
+  recipe_add_to_list_past_4_day,
+  recipe_add_to_list_past_5_day,
+  recipe_print_past_1_day,
+  recipe_print_past_2_day,
+  recipe_print_past_3_day,
+  recipe_print_past_4_day,
+  recipe_print_past_5_day,
+  sign_up_past_1_day,
+  sign_up_past_2_day,
+  sign_up_past_3_day,
+  sign_up_past_4_day,
+  sign_up_past_5_day,
+  recipe_favorite_past_1_day,
+  recipe_favorite_past_2_day,
+  recipe_favorite_past_3_day,
+  recipe_favorite_past_4_day,
+  recipe_favorite_past_5_day,
+  recipe_add_to_menu_past_1_day,
+  recipe_add_to_menu_past_2_day,
+  recipe_add_to_menu_past_3_day,
+  recipe_add_to_menu_past_4_day,
+  recipe_add_to_menu_past_5_day
+FROM (
+SELECT DISTINCT
+  processed_timestamp, 
+  feature_date,
+  user_pseudo_id,
+  user_id,
+  user_ltv_revenue,
+  device_category,
+  device_mobile_brand_name,
+  device_mobile_model_name,
+  device_os,
+  device_language,
+  device_web_browser,
+  geo_sub_continent,
+  geo_country,
+  geo_region,
+  geo_city,
+  geo_metro,
+  last_traffic_source_medium,
+  last_traffic_source_name,
+  last_traffic_source_source,
+  first_traffic_source_medium,
+  first_traffic_source_name,
+  first_traffic_source_source,
+  has_signed_in_with_user_id,
+  scroll_50_past_1_day,
+  scroll_50_past_2_day,
+  scroll_50_past_3_day,
+  scroll_50_past_4_day,
+  scroll_50_past_5_day,
+  scroll_90_past_1_day,
+  scroll_90_past_2_day,
+  scroll_90_past_3_day,
+  scroll_90_past_4_day,
+  scroll_90_past_5_day,
+  view_search_results_past_1_day,
+  view_search_results_past_2_day,
+  view_search_results_past_3_day,
+  view_search_results_past_4_day,
+  view_search_results_past_5_day,
+  recipe_add_to_list_past_1_day,
+  recipe_add_to_list_past_2_day,
+  recipe_add_to_list_past_3_day,
+  recipe_add_to_list_past_4_day,
+  recipe_add_to_list_past_5_day,
+  recipe_print_past_1_day,
+  recipe_print_past_2_day,
+  recipe_print_past_3_day,
+  recipe_print_past_4_day,
+  recipe_print_past_5_day,
+  sign_up_past_1_day,
+  sign_up_past_2_day,
+  sign_up_past_3_day,
+  sign_up_past_4_day,
+  sign_up_past_5_day,
+  recipe_favorite_past_1_day,
+  recipe_favorite_past_2_day,
+  recipe_favorite_past_3_day,
+  recipe_favorite_past_4_day,
+  recipe_favorite_past_5_day,
+  recipe_add_to_menu_past_1_day,
+  recipe_add_to_menu_past_2_day,
+  recipe_add_to_menu_past_3_day,
+  recipe_add_to_menu_past_4_day,
+  recipe_add_to_menu_past_5_day,
+  -- Row number partitioned by user pseudo id ordered by feature date descending
+  ROW_NUMBER() OVER (PARTITION BY user_pseudo_id ORDER BY feature_date DESC) AS user_row_order
+  FROM `{{project_id}}.{{dataset}}.lead_score_propensity_inference_5_1`
+)
+WHERE
+  -- Filter only for the most recent user example
+  user_row_order = 1;
+
diff --git a/sql/procedure/lead_score_propensity_label.sqlx b/sql/procedure/lead_score_propensity_label.sqlx
new file mode 100644
index 00000000..c92afd27
--- /dev/null
+++ b/sql/procedure/lead_score_propensity_label.sqlx
@@ -0,0 +1,103 @@
+-- Copyright 2023 Google LLC
+--
+-- Licensed under the Apache License, Version 2.0 (the "License");
+-- you may not use this file except in compliance with the License.
+-- You may obtain a copy of the License at
+--
+--     http://www.apache.org/licenses/LICENSE-2.0
+--
+-- Unless required by applicable law or agreed to in writing, software
+-- distributed under the License is distributed on an "AS IS" BASIS,
+-- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+-- See the License for the specific language governing permissions and
+-- limitations under the License.
+
+-- Run these windows aggregations every day. For each date in training and inference date ranges.
+-- Setting procedure to lookback from the day before `input_date` until the day before `end_date`
+SET input_date = DATE_SUB(input_date, INTERVAL 1 DAY);
+SET end_date = DATE_SUB(end_date, INTERVAL 1 DAY);
+
+-- Future User metrics: 1-day future logins per user
+CREATE OR REPLACE TEMP TABLE future_logins_per_user AS (
+  SELECT
+    -- User's unique identifier
+    user_pseudo_id,
+    -- The date for which future logins are being calculated
+    input_date as event_date,
+    -- Calculates the maximum count of distinct events for users who made a logins 1 day after `input_date`
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(event_date, input_date, DAY) = 1 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS login_day_1
+  FROM `{{mds_project_id}}.{{mds_dataset}}.event` as E
+  INNER JOIN `{{mds_project_id}}.{{mds_dataset}}.device` as D
+    ON E.device_type_id = D.device_type_id
+  -- Filters events to be within the date range defined by input_date and end_date from dates_interval
+  WHERE event_date BETWEEN input_date AND end_date
+    -- Filter event with event name login
+    AND LOWER(E.event_name) IN ('login')
+    AND E.ga_session_id IS NOT NULL
+    AND D.device_os IS NOT NULL
+  -- Grouping by user pseudo ids
+  GROUP BY user_pseudo_id
+);
+
+-- All users in the platform
+CREATE OR REPLACE TEMP TABLE all_users_possible_logins as (
+  SELECT DISTINCT
+    -- User's unique identifier
+    Users.user_pseudo_id,
+    -- The event date for which logins are being considered
+    Days.event_date as event_date,
+    -- Placeholder columns for login counts in future days
+    NULL as login_day_1
+  FROM `{{mds_project_id}}.{{mds_dataset}}.event` Users
+  CROSS JOIN 
+  -- Generates a list of dates for the current date (`input_date`)
+  (SELECT event_date FROM UNNEST(GENERATE_DATE_ARRAY(input_date, end_date, INTERVAL 1 DAY)) AS event_date) Days
+  WHERE Days.event_date = input_date
+    -- Filter event with event name login
+    -- AND Users.event_name='login'
+    AND Users.ga_session_id IS NOT NULL
+);
+
+
+CREATE OR REPLACE TEMP TABLE DataForTargetTable AS
+SELECT DISTINCT
+  -- Timestamp when the data was processed
+  CURRENT_TIMESTAMP() AS processed_timestamp,
+  -- The date for which logins are being considered
+  A.event_date as feature_date,
+  -- User's unique identifier
+  A.user_pseudo_id,
+  -- The maximum of 0 and the login count for day 1 (if it exists)
+  LEAST(COALESCE(B.login_day_1, 0), 1) AS login_day_1
+FROM all_users_possible_logins AS A
+LEFT JOIN future_logins_per_user AS B
+ON B.user_pseudo_id = A.user_pseudo_id
+;
+
+-- Updates or inserts data into the target table
+MERGE `{{project_id}}.{{dataset}}.{{insert_table}}` I
+USING DataForTargetTable T
+ON I.feature_date = T.feature_date 
+  AND I.user_pseudo_id = T.user_pseudo_id
+WHEN MATCHED THEN
+  -- Updates existing records
+  UPDATE SET 
+    -- Updates the processed timestamp
+    I.processed_timestamp = T.processed_timestamp,
+    -- Updates login counts for each day
+    I.login_day_1 = T.login_day_1
+WHEN NOT MATCHED THEN
+  -- Inserts new records
+  INSERT 
+    (processed_timestamp,
+     feature_date,
+     user_pseudo_id,
+     login_day_1)
+  VALUES
+    (T.processed_timestamp,
+     T.feature_date,
+     T.user_pseudo_id,
+     T.login_day_1)
+;
+
+SET rows_added = (SELECT COUNT(DISTINCT user_pseudo_id) FROM `{{project_id}}.{{dataset}}.{{insert_table}}`);
diff --git a/sql/procedure/lead_score_propensity_training_preparation.sqlx b/sql/procedure/lead_score_propensity_training_preparation.sqlx
new file mode 100644
index 00000000..6e0eed69
--- /dev/null
+++ b/sql/procedure/lead_score_propensity_training_preparation.sqlx
@@ -0,0 +1,1093 @@
+-- Copyright 2023 Google LLC
+--
+-- Licensed under the Apache License, Version 2.0 (the "License");
+-- you may not use this file except in compliance with the License.
+-- You may obtain a copy of the License at
+--
+--     http://www.apache.org/licenses/LICENSE-2.0
+--
+-- Unless required by applicable law or agreed to in writing, software
+-- distributed under the License is distributed on an "AS IS" BASIS,
+-- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+-- See the License for the specific language governing permissions and
+-- limitations under the License.
+
+DECLARE custom_start_date DATE DEFAULT NULL;
+DECLARE custom_end_date DATE DEFAULT NULL;
+
+-- custom_start_date: The start date of the data to be used for training.
+-- custom_end_date: The end date of the data to be used for training.
+SET custom_start_date = PARSE_DATE("%Y-%m-%d", {{custom_start_date}});
+SET custom_end_date = PARSE_DATE("%Y-%m-%d", {{custom_end_date}});
+
+-- The procedure first checks if the custom_start_date and custom_end_date parameters are valid. 
+-- If either parameter is not valid, the procedure sets the corresponding date to the maximum or
+-- minimum date of the available data.
+IF custom_start_date IS NOT NULL AND custom_start_date >= start_date AND custom_start_date <= end_date 
+   AND custom_start_date < custom_end_date THEN
+  SET start_date = custom_start_date;
+END IF;
+
+IF custom_end_date IS NOT NULL AND custom_end_date <= end_date AND custom_end_date >= start_date 
+   AND custom_end_date > custom_start_date THEN
+  SET end_date = custom_end_date;
+END IF;
+
+-- This is a temp table consolidating user_dimensions over the dates intervals.
+CREATE OR REPLACE TEMP TABLE training_preparation_ud as (
+  SELECT DISTINCT
+    -- The user pseudo id
+    UD.user_pseudo_id,
+    -- The user id 
+    MAX(UD.user_id) OVER(user_dimensions_window) AS user_id,
+    -- The feature date
+    UD.feature_date,
+    -- The user lifetime value revenue
+    MAX(UD.user_ltv_revenue) OVER(user_dimensions_window) AS user_ltv_revenue,
+    -- The device category
+    MAX(UD.device_category) OVER(user_dimensions_window) AS device_category,
+    -- The device brand name
+    MAX(UD.device_mobile_brand_name) OVER(user_dimensions_window) AS device_mobile_brand_name,
+    -- The device model name
+    MAX(UD.device_mobile_model_name) OVER(user_dimensions_window) AS device_mobile_model_name,
+    -- The device operating system
+    MAX(UD.device_os) OVER(user_dimensions_window) AS device_os,
+    -- The device language
+    MAX(UD.device_language) OVER(user_dimensions_window) AS device_language,
+    -- The device web browser
+    MAX(UD.device_web_browser) OVER(user_dimensions_window) AS device_web_browser,
+    -- The user sub continent
+    MAX(UD.geo_sub_continent) OVER(user_dimensions_window) AS geo_sub_continent,
+    -- The user country
+    MAX(UD.geo_country) OVER(user_dimensions_window) AS geo_country,
+    -- The user region
+    MAX(UD.geo_region) OVER(user_dimensions_window) AS geo_region,
+    -- The user city
+    MAX(UD.geo_city) OVER(user_dimensions_window) AS geo_city,
+    -- The user metro
+    MAX(UD.geo_metro) OVER(user_dimensions_window) AS geo_metro,
+    -- The user last traffic source medium
+    MAX(UD.last_traffic_source_medium) OVER(user_dimensions_window) AS last_traffic_source_medium,
+    -- The user last traffic source name
+    MAX(UD.last_traffic_source_name) OVER(user_dimensions_window) AS last_traffic_source_name,
+    -- The user last traffic source source
+    MAX(UD.last_traffic_source_source) OVER(user_dimensions_window) AS last_traffic_source_source,
+    -- The user first traffic source medium
+    MAX(UD.first_traffic_source_medium) OVER(user_dimensions_window) AS first_traffic_source_medium,
+    -- The user first traffic source name
+    MAX(UD.first_traffic_source_name) OVER(user_dimensions_window) AS first_traffic_source_name,
+    -- The user first traffic source source
+    MAX(UD.first_traffic_source_source) OVER(user_dimensions_window) AS first_traffic_source_source,
+    -- Whether the user has signed in with user ID
+    MAX(UD.has_signed_in_with_user_id) OVER(user_dimensions_window) AS has_signed_in_with_user_id,
+FROM
+  `{{feature_store_project_id}}.{{feature_store_dataset}}.user_dimensions` UD
+WHERE
+  -- Filter feature dates according to the defined date interval
+  UD.feature_date BETWEEN start_date AND end_date
+WINDOW 
+  user_dimensions_window AS (PARTITION BY UD.user_pseudo_id, UD.feature_date ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)
+);
+
+-- This is a temp table consolidating user rolling metrics over the dates intervals.
+CREATE OR REPLACE TEMP TABLE training_preparation_uwlm as (
+  SELECT DISTINCT
+    -- User pseudo id
+    UWLM.user_pseudo_id,
+    -- Feature date
+    UWLM.feature_date,
+    -- Calculate the maximum value for each metric over the window
+    MAX(UWLM.scroll_50_past_1_day) OVER(user_rolling_lead_window) AS scroll_50_past_1_day,
+    -- Maximum active users in the past 2 days
+    MAX(UWLM.scroll_50_past_2_day) OVER(user_rolling_lead_window) AS scroll_50_past_2_day,
+    -- Maximum active users in the past 3 days
+    MAX(UWLM.scroll_50_past_3_day) OVER(user_rolling_lead_window) AS scroll_50_past_3_day,
+    -- Maximum active users in the past 4 days
+    MAX(UWLM.scroll_50_past_4_day) OVER(user_rolling_lead_window) AS scroll_50_past_4_day,
+    -- Maximum active users in the past 5 days
+    MAX(UWLM.scroll_50_past_5_day) OVER(user_rolling_lead_window) AS scroll_50_past_5_day,
+    -- Maximum scroll_90 in the past 1 day
+    MAX(UWLM.scroll_90_past_1_day) OVER(user_rolling_lead_window) AS scroll_90_past_1_day,
+    -- Maximum scroll_90 in the past 2 days
+    MAX(UWLM.scroll_90_past_2_day) OVER(user_rolling_lead_window) AS scroll_90_past_2_day,
+    -- Maximum scroll_90 in the past 3 days
+    MAX(UWLM.scroll_90_past_3_day) OVER(user_rolling_lead_window) AS scroll_90_past_3_day,
+    -- Maximum scroll_90 in the past 4 days
+    MAX(UWLM.scroll_90_past_4_day) OVER(user_rolling_lead_window) AS scroll_90_past_4_day,
+    -- Maximum scroll_90 in the past 5 days
+    MAX(UWLM.scroll_90_past_5_day) OVER(user_rolling_lead_window) AS scroll_90_past_5_day,
+    -- Maximum view_search_results in the past 1 day
+    MAX(UWLM.view_search_results_past_1_day) OVER(user_rolling_lead_window) AS view_search_results_past_1_day,
+    -- Maximum view_search_results in the past 2 days
+    MAX(UWLM.view_search_results_past_2_day) OVER(user_rolling_lead_window) AS view_search_results_past_2_day,
+    -- Maximum view_search_results in the past 3 days
+    MAX(UWLM.view_search_results_past_3_day) OVER(user_rolling_lead_window) AS view_search_results_past_3_day,
+    -- Maximum view_search_results in the past 4 days
+    MAX(UWLM.view_search_results_past_4_day) OVER(user_rolling_lead_window) AS view_search_results_past_4_day,
+    -- Maximum view_search_results in the past 5 days
+    MAX(UWLM.view_search_results_past_5_day) OVER(user_rolling_lead_window) AS view_search_results_past_5_day,
+    -- Maximum file_download in the past 1 day
+    MAX(UWLM.file_download_past_1_day) OVER(user_rolling_lead_window) AS file_download_past_1_day,
+    -- Maximum file_download in the past 2 days
+    MAX(UWLM.file_download_past_2_day) OVER(user_rolling_lead_window) AS file_download_past_2_day,
+    -- Maximum file_download in the past 3 days
+    MAX(UWLM.file_download_past_3_day) OVER(user_rolling_lead_window) AS file_download_past_3_day,
+    -- Maximum file_download in the past 4 days
+    MAX(UWLM.file_download_past_4_day) OVER(user_rolling_lead_window) AS file_download_past_4_day,
+    -- Maximum file_download in the past 5 days
+    MAX(UWLM.file_download_past_5_day) OVER(user_rolling_lead_window) AS file_download_past_5_day,
+    -- Maximum recipe_add_to_list in the past 1 day
+    MAX(UWLM.recipe_add_to_list_past_1_day) OVER(user_rolling_lead_window) AS recipe_add_to_list_past_1_day,
+    -- Maximum recipe_add_to_list in the past 2 days
+    MAX(UWLM.recipe_add_to_list_past_2_day) OVER(user_rolling_lead_window) AS recipe_add_to_list_past_2_day,
+    -- Maximum recipe_add_to_list in the past 3 days
+    MAX(UWLM.recipe_add_to_list_past_3_day) OVER(user_rolling_lead_window) AS recipe_add_to_list_past_3_day,
+    -- Maximum recipe_add_to_list in the past 4 days
+    MAX(UWLM.recipe_add_to_list_past_4_day) OVER(user_rolling_lead_window) AS recipe_add_to_list_past_4_day,
+    -- Maximum recipe_add_to_list in the past 5 days
+    MAX(UWLM.recipe_add_to_list_past_5_day) OVER(user_rolling_lead_window) AS recipe_add_to_list_past_5_day,
+    -- Maximum recipe_print in the past 1 day
+    MAX(UWLM.recipe_print_past_1_day) OVER(user_rolling_lead_window) AS recipe_print_past_1_day,
+    -- Maximum recipe_print in the past 2 days
+    MAX(UWLM.recipe_print_past_2_day) OVER(user_rolling_lead_window) AS recipe_print_past_2_day,
+    -- Maximum recipe_print in the past 3 days
+    MAX(UWLM.recipe_print_past_3_day) OVER(user_rolling_lead_window) AS recipe_print_past_3_day,
+    -- Maximum recipe_print in the past 4 days
+    MAX(UWLM.recipe_print_past_4_day) OVER(user_rolling_lead_window) AS recipe_print_past_4_day,
+    -- Maximum recipe_print in the past 5 days
+    MAX(UWLM.recipe_print_past_5_day) OVER(user_rolling_lead_window) AS recipe_print_past_5_day,
+    -- Maximum sign_up_past in the past 1 day
+    MAX(UWLM.sign_up_past_1_day) OVER(user_rolling_lead_window) AS sign_up_past_1_day,
+    -- Maximum sign_up_past in the past 2 days
+    MAX(UWLM.sign_up_past_2_day) OVER(user_rolling_lead_window) AS sign_up_past_2_day,
+    -- Maximum sign_up_past in the past 3 days
+    MAX(UWLM.sign_up_past_3_day) OVER(user_rolling_lead_window) AS sign_up_past_3_day,
+    -- Maximum sign_up_past in the past 4 days
+    MAX(UWLM.sign_up_past_4_day) OVER(user_rolling_lead_window) AS sign_up_past_4_day,
+    -- Maximum sign_up_past in the past 5 days
+    MAX(UWLM.sign_up_past_5_day) OVER(user_rolling_lead_window) AS sign_up_past_5_day,
+    -- Maximum recipe_favorite in the past 1 day
+    MAX(UWLM.recipe_favorite_past_1_day) OVER(user_rolling_lead_window) AS recipe_favorite_past_1_day,
+    -- Maximum recipe_favorite in the past 2 days
+    MAX(UWLM.recipe_favorite_past_2_day) OVER(user_rolling_lead_window) AS recipe_favorite_past_2_day,
+    -- Maximum recipe_favorite in the past 3 days
+    MAX(UWLM.recipe_favorite_past_3_day) OVER(user_rolling_lead_window) AS recipe_favorite_past_3_day,
+    -- Maximum recipe_favorite in the past 4 days
+    MAX(UWLM.recipe_favorite_past_4_day) OVER(user_rolling_lead_window) AS recipe_favorite_past_4_day,
+    -- Maximum recipe_favorite in the past 5 days
+    MAX(UWLM.recipe_favorite_past_5_day) OVER(user_rolling_lead_window) AS recipe_favorite_past_5_day,
+    -- Maximum recipe_add_to_menu in the past 1 day
+    MAX(UWLM.recipe_add_to_menu_past_1_day) OVER(user_rolling_lead_window) AS recipe_add_to_menu_past_1_day,
+    -- Maximum recipe_add_to_menu in the past 2 days
+    MAX(UWLM.recipe_add_to_menu_past_2_day) OVER(user_rolling_lead_window) AS recipe_add_to_menu_past_2_day,
+    -- Maximum recipe_add_to_menu in the past 3 days
+    MAX(UWLM.recipe_add_to_menu_past_3_day) OVER(user_rolling_lead_window) AS recipe_add_to_menu_past_3_day,
+    -- Maximum recipe_add_to_menu in the past 4 days
+    MAX(UWLM.recipe_add_to_menu_past_4_day) OVER(user_rolling_lead_window) AS recipe_add_to_menu_past_4_day,
+    -- Maximum recipe_add_to_menu in the past 5 days
+    MAX(UWLM.recipe_add_to_menu_past_5_day) OVER(user_rolling_lead_window) AS recipe_add_to_menu_past_5_day,
+FROM
+  `{{feature_store_project_id}}.{{feature_store_dataset}}.user_rolling_window_lead_metrics` UWLM
+WHERE
+  -- In the future consider `feature_date BETWEEN start_date AND end_date`, to process multiple days. Modify Partition BY
+  UWLM.feature_date BETWEEN start_date AND end_date
+WINDOW 
+  user_rolling_lead_window AS (PARTITION BY UWLM.user_pseudo_id, UWLM.feature_date ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)
+);
+
+-- This is a temp table consolidating user labels over the dates intervals.
+CREATE OR REPLACE TEMP TABLE training_preparation_label as (
+  SELECT DISTINCT
+  LABEL.user_pseudo_id, -- The unique identifier for the user.
+  LABEL.feature_date, -- The date for which the features are extracted.
+  MAX(LABEL.login_day_1) OVER(lead_score_propensity_label_window) AS login_day_1, -- Whether the user made a login on day 1.
+FROM
+  `{{feature_store_project_id}}.{{feature_store_dataset}}.lead_score_propensity_label` LABEL
+WHERE
+  -- Define the training subset interval
+  LABEL.feature_date BETWEEN start_date AND end_date
+WINDOW 
+  lead_score_propensity_label_window AS (PARTITION BY LABEL.user_pseudo_id, LABEL.feature_date ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)
+);
+
+-- This is a temp table consolidating all features and labels over the dates intervals.
+CREATE OR REPLACE TEMP TABLE training_preparation as (
+  SELECT DISTINCT
+    UD.user_pseudo_id,
+    UD.user_id,
+    UD.feature_date,
+    COALESCE(UD.user_ltv_revenue, 0.0) AS user_ltv_revenue,
+    UD.device_category,
+    UD.device_mobile_brand_name,
+    UD.device_mobile_model_name,
+    UD.device_os,
+    UD.device_language,
+    UD.device_web_browser,
+    UD.geo_sub_continent,
+    UD.geo_country,
+    UD.geo_region,
+    UD.geo_city,
+    UD.geo_metro,
+    UD.last_traffic_source_medium,
+    UD.last_traffic_source_name,
+    UD.last_traffic_source_source,
+    UD.first_traffic_source_medium,
+    UD.first_traffic_source_name,
+    UD.first_traffic_source_source,
+    UD.has_signed_in_with_user_id,
+    UWLM.scroll_50_past_1_day,
+    UWLM.scroll_50_past_2_day,
+    UWLM.scroll_50_past_3_day,
+    UWLM.scroll_50_past_4_day,
+    UWLM.scroll_50_past_5_day,
+    UWLM.scroll_90_past_1_day,
+    UWLM.scroll_90_past_2_day,
+    UWLM.scroll_90_past_3_day,
+    UWLM.scroll_90_past_4_day,
+    UWLM.scroll_90_past_5_day,
+    UWLM.view_search_results_past_1_day,
+    UWLM.view_search_results_past_2_day,
+    UWLM.view_search_results_past_3_day,
+    UWLM.view_search_results_past_4_day,
+    UWLM.view_search_results_past_5_day,
+    UWLM.file_download_past_1_day,
+    UWLM.file_download_past_2_day,
+    UWLM.file_download_past_3_day,
+    UWLM.file_download_past_4_day,
+    UWLM.file_download_past_5_day,
+    UWLM.recipe_add_to_list_past_1_day,
+    UWLM.recipe_add_to_list_past_2_day,
+    UWLM.recipe_add_to_list_past_3_day,
+    UWLM.recipe_add_to_list_past_4_day,
+    UWLM.recipe_add_to_list_past_5_day,
+    UWLM.recipe_print_past_1_day,
+    UWLM.recipe_print_past_2_day,
+    UWLM.recipe_print_past_3_day,
+    UWLM.recipe_print_past_4_day,
+    UWLM.recipe_print_past_5_day,
+    UWLM.sign_up_past_1_day,
+    UWLM.sign_up_past_2_day,
+    UWLM.sign_up_past_3_day,
+    UWLM.sign_up_past_4_day,
+    UWLM.sign_up_past_5_day,
+    UWLM.recipe_favorite_past_1_day,
+    UWLM.recipe_favorite_past_2_day,
+    UWLM.recipe_favorite_past_3_day,
+    UWLM.recipe_favorite_past_4_day,
+    UWLM.recipe_favorite_past_5_day,
+    UWLM.recipe_add_to_menu_past_1_day,
+    UWLM.recipe_add_to_menu_past_2_day,
+    UWLM.recipe_add_to_menu_past_3_day,
+    UWLM.recipe_add_to_menu_past_4_day,
+    UWLM.recipe_add_to_menu_past_5_day,
+    LABEL.login_day_1
+FROM
+  training_preparation_ud UD
+INNER JOIN
+  training_preparation_uwlm UWLM
+ON
+  UWLM.user_pseudo_id = UD.user_pseudo_id
+  AND UWLM.feature_date = UD.feature_date
+INNER JOIN
+  training_preparation_label LABEL
+ON
+  LABEL.user_pseudo_id = UD.user_pseudo_id
+  AND LABEL.feature_date = UD.feature_date
+);
+
+-- This is a temp table split the rows in each different data_split (TRAIN, VALIDATE, TEST) split
+CREATE OR REPLACE TEMP TABLE DataForTargetTable AS(
+  SELECT DISTINCT
+  CASE 
+    WHEN (ABS(MOD(FARM_FINGERPRINT(user_pseudo_id), 10)) BETWEEN 0 AND train_split_end_number) THEN "TRAIN" 
+    WHEN (ABS(MOD(FARM_FINGERPRINT(user_pseudo_id), 10)) BETWEEN train_split_end_number AND validation_split_end_number) THEN "VALIDATE" 
+    WHEN (ABS(MOD(FARM_FINGERPRINT(user_pseudo_id), 10)) BETWEEN validation_split_end_number AND 9) THEN "TEST"
+  END as data_split,
+  feature_date,
+  user_pseudo_id,
+  user_id,
+  user_ltv_revenue,
+  device_category,
+  device_mobile_brand_name,
+  device_mobile_model_name,
+  device_os,
+  device_language,
+  device_web_browser,
+  geo_sub_continent,
+  geo_country,
+  geo_region,
+  geo_city,
+  geo_metro,
+  last_traffic_source_medium,
+  last_traffic_source_name,
+  last_traffic_source_source,
+  first_traffic_source_medium,
+  first_traffic_source_name,
+  first_traffic_source_source,
+  has_signed_in_with_user_id,
+  scroll_50_past_1_day,
+  scroll_50_past_2_day,
+  scroll_50_past_3_day,
+  scroll_50_past_4_day,
+  scroll_50_past_5_day,
+  scroll_90_past_1_day,
+  scroll_90_past_2_day,
+  scroll_90_past_3_day,
+  scroll_90_past_4_day,
+  scroll_90_past_5_day,
+  view_search_results_past_1_day,
+  view_search_results_past_2_day,
+  view_search_results_past_3_day,
+  view_search_results_past_4_day,
+  view_search_results_past_5_day,
+  file_download_past_1_day,
+  file_download_past_2_day,
+  file_download_past_3_day,
+  file_download_past_4_day,
+  file_download_past_5_day,
+  recipe_add_to_list_past_1_day,
+  recipe_add_to_list_past_2_day,
+  recipe_add_to_list_past_3_day,
+  recipe_add_to_list_past_4_day,
+  recipe_add_to_list_past_5_day,
+  recipe_print_past_1_day,
+  recipe_print_past_2_day,
+  recipe_print_past_3_day,
+  recipe_print_past_4_day,
+  recipe_print_past_5_day,
+  sign_up_past_1_day,
+  sign_up_past_2_day,
+  sign_up_past_3_day,
+  sign_up_past_4_day,
+  sign_up_past_5_day,
+  recipe_favorite_past_1_day,
+  recipe_favorite_past_2_day,
+  recipe_favorite_past_3_day,
+  recipe_favorite_past_4_day,
+  recipe_favorite_past_5_day,
+  recipe_add_to_menu_past_1_day,
+  recipe_add_to_menu_past_2_day,
+  recipe_add_to_menu_past_3_day,
+  recipe_add_to_menu_past_4_day,
+  recipe_add_to_menu_past_5_day,
+  login_day_1
+  FROM training_preparation);
+
+CREATE OR REPLACE TABLE `{{project_id}}.{{dataset}}.lead_score_propensity_training_full_dataset` AS
+SELECT DISTINCT * FROM DataForTargetTable
+WHERE data_split IS NOT NULL;
+
+
+-- This is a table preparing rows for lead score propensity modelling looking back 5 days and looking ahead 1 day.
+CREATE OR REPLACE TABLE `{{project_id}}.{{dataset}}.lead_score_propensity_training_5_1` AS(
+  SELECT DISTINCT
+  CURRENT_TIMESTAMP() AS processed_timestamp, 
+  data_split,
+  feature_date,
+  user_pseudo_id,
+  LAST_VALUE(user_id) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS user_id,
+  LAST_VALUE(user_ltv_revenue) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS user_ltv_revenue,
+  LAST_VALUE(device_category) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS device_category,
+  LAST_VALUE(device_mobile_brand_name) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS device_mobile_brand_name,
+  LAST_VALUE(device_mobile_model_name) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS device_mobile_model_name,
+  LAST_VALUE(device_os) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS device_os,
+  LAST_VALUE(device_language) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS device_language,
+  LAST_VALUE(device_web_browser) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS device_web_browser,
+  LAST_VALUE(geo_sub_continent) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS geo_sub_continent,
+  LAST_VALUE(geo_country) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS geo_country,
+  LAST_VALUE(geo_region) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS geo_region,
+  LAST_VALUE(geo_city) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS geo_city,
+  LAST_VALUE(geo_metro) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS geo_metro,
+  LAST_VALUE(last_traffic_source_medium) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS last_traffic_source_medium,
+  LAST_VALUE(last_traffic_source_name) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS last_traffic_source_name,
+  LAST_VALUE(last_traffic_source_source) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS last_traffic_source_source,
+  LAST_VALUE(first_traffic_source_medium) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS first_traffic_source_medium,
+  LAST_VALUE(first_traffic_source_name) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS first_traffic_source_name,
+  LAST_VALUE(first_traffic_source_source) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS first_traffic_source_source,
+  LAST_VALUE(has_signed_in_with_user_id) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS has_signed_in_with_user_id,
+  LAST_VALUE(scroll_50_past_1_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS scroll_50_past_1_day,
+  LAST_VALUE(scroll_50_past_2_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS scroll_50_past_2_day,
+  LAST_VALUE(scroll_50_past_3_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS scroll_50_past_3_day,
+  LAST_VALUE(scroll_50_past_4_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS scroll_50_past_4_day,
+  LAST_VALUE(scroll_50_past_5_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS scroll_50_past_5_day,
+  LAST_VALUE(scroll_90_past_1_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS scroll_90_past_1_day,
+  LAST_VALUE(scroll_90_past_2_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS scroll_90_past_2_day,
+  LAST_VALUE(scroll_90_past_3_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS scroll_90_past_3_day,
+  LAST_VALUE(scroll_90_past_4_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS scroll_90_past_4_day,
+  LAST_VALUE(scroll_90_past_5_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS scroll_90_past_5_day,
+  LAST_VALUE(view_search_results_past_1_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS view_search_results_past_1_day,
+  LAST_VALUE(view_search_results_past_2_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS view_search_results_past_2_day,
+  LAST_VALUE(view_search_results_past_3_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS view_search_results_past_3_day,
+  LAST_VALUE(view_search_results_past_4_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS view_search_results_past_4_day,
+  LAST_VALUE(view_search_results_past_5_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS view_search_results_past_5_day,
+  LAST_VALUE(file_download_past_1_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS file_download_past_1_day,
+  LAST_VALUE(file_download_past_2_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS file_download_past_2_day,
+  LAST_VALUE(file_download_past_3_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS file_download_past_3_day,
+  LAST_VALUE(file_download_past_4_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS file_download_past_4_day,
+  LAST_VALUE(file_download_past_5_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS file_download_past_5_day,
+  LAST_VALUE(recipe_add_to_list_past_1_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS recipe_add_to_list_past_1_day,
+  LAST_VALUE(recipe_add_to_list_past_2_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS recipe_add_to_list_past_2_day,
+  LAST_VALUE(recipe_add_to_list_past_3_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS recipe_add_to_list_past_3_day,
+  LAST_VALUE(recipe_add_to_list_past_4_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS recipe_add_to_list_past_4_day,
+  LAST_VALUE(recipe_add_to_list_past_5_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS recipe_add_to_list_past_5_day,
+  LAST_VALUE(recipe_print_past_1_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS recipe_print_past_1_day,
+  LAST_VALUE(recipe_print_past_2_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS recipe_print_past_2_day,
+  LAST_VALUE(recipe_print_past_3_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS recipe_print_past_3_day,
+  LAST_VALUE(recipe_print_past_4_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS recipe_print_past_4_day,
+  LAST_VALUE(recipe_print_past_5_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS recipe_print_past_5_day,
+  LAST_VALUE(sign_up_past_1_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS sign_up_past_1_day,
+  LAST_VALUE(sign_up_past_2_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS sign_up_past_2_day,
+  LAST_VALUE(sign_up_past_3_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS sign_up_past_3_day,
+  LAST_VALUE(sign_up_past_4_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS sign_up_past_4_day,
+  LAST_VALUE(sign_up_past_5_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS sign_up_past_5_day,
+  LAST_VALUE(recipe_favorite_past_1_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS recipe_favorite_past_1_day,
+  LAST_VALUE(recipe_favorite_past_2_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS recipe_favorite_past_2_day,
+  LAST_VALUE(recipe_favorite_past_3_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS recipe_favorite_past_3_day,
+  LAST_VALUE(recipe_favorite_past_4_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS recipe_favorite_past_4_day,
+  LAST_VALUE(recipe_favorite_past_5_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS recipe_favorite_past_5_day,
+  LAST_VALUE(recipe_add_to_menu_past_1_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS recipe_add_to_menu_past_1_day,
+  LAST_VALUE(recipe_add_to_menu_past_2_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS recipe_add_to_menu_past_2_day,
+  LAST_VALUE(recipe_add_to_menu_past_3_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS recipe_add_to_menu_past_3_day,
+  LAST_VALUE(recipe_add_to_menu_past_4_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS recipe_add_to_menu_past_4_day,
+  LAST_VALUE(recipe_add_to_menu_past_5_day) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) AS recipe_add_to_menu_past_5_day,
+  -- Calculate the will login label.
+  -- Label for a lead score propensity model. It indicates whether a user made a lead score within the next 30 days based on their lead score history. 
+  -- This label is then used to train a model that can predict the likelihood of future logins for other users.
+  LAST_VALUE(CASE WHEN (login_day_1) = 0 THEN 0 ELSE 1 END) OVER(PARTITION BY user_pseudo_id, feature_date ORDER BY feature_date DESC) as will_login
+  FROM `{{project_id}}.{{dataset}}.lead_score_propensity_training_full_dataset`
+);
+
+
+-- This is a view preparing rows for lead score propensity modelling looking back 5 days and looking ahead 1 days.
+CREATE OR REPLACE VIEW `{{project_id}}.{{dataset}}.v_lead_score_propensity_training_5_1`
+(processed_timestamp, 
+  data_split,
+  user_pseudo_id,
+  user_id,
+  user_ltv_revenue,
+  device_category,
+  device_mobile_brand_name,
+  device_mobile_model_name,
+  device_os,
+  device_language,
+  device_web_browser,
+  geo_sub_continent,
+  geo_country,
+  geo_region,
+  geo_city,
+  geo_metro,
+  last_traffic_source_medium,
+  last_traffic_source_name,
+  last_traffic_source_source,
+  first_traffic_source_medium,
+  first_traffic_source_name,
+  first_traffic_source_source,
+  has_signed_in_with_user_id,
+  scroll_50_past_1_day,
+  scroll_50_past_2_day,
+  scroll_50_past_3_day,
+  scroll_50_past_4_day,
+  scroll_50_past_5_day,
+  scroll_90_past_1_day,
+  scroll_90_past_2_day,
+  scroll_90_past_3_day,
+  scroll_90_past_4_day,
+  scroll_90_past_5_day,
+  view_search_results_past_1_day,
+  view_search_results_past_2_day,
+  view_search_results_past_3_day,
+  view_search_results_past_4_day,
+  view_search_results_past_5_day,
+  file_download_past_1_day,
+  file_download_past_2_day,
+  file_download_past_3_day,
+  file_download_past_4_day,
+  file_download_past_5_day,
+  recipe_add_to_list_past_1_day,
+  recipe_add_to_list_past_2_day,
+  recipe_add_to_list_past_3_day,
+  recipe_add_to_list_past_4_day,
+  recipe_add_to_list_past_5_day,
+  recipe_print_past_1_day,
+  recipe_print_past_2_day,
+  recipe_print_past_3_day,
+  recipe_print_past_4_day,
+  recipe_print_past_5_day,
+  sign_up_past_1_day,
+  sign_up_past_2_day,
+  sign_up_past_3_day,
+  sign_up_past_4_day,
+  sign_up_past_5_day,
+  recipe_favorite_past_1_day,
+  recipe_favorite_past_2_day,
+  recipe_favorite_past_3_day,
+  recipe_favorite_past_4_day,
+  recipe_favorite_past_5_day,
+  recipe_add_to_menu_past_1_day,
+  recipe_add_to_menu_past_2_day,
+  recipe_add_to_menu_past_3_day,
+  recipe_add_to_menu_past_4_day,
+  recipe_add_to_menu_past_5_day,
+  will_login)
+OPTIONS(
+  --expiration_timestamp=TIMESTAMP_ADD(CURRENT_TIMESTAMP(), INTERVAL 48 HOUR),
+  friendly_name="v_lead_score_propensity_training_5_1",
+  description="View Lead Score Propensity Training dataset using 5 days back to predict 1 day ahead. View expires after 48h and should run daily.",
+  labels=[("org_unit", "development")]
+) AS 
+SELECT DISTINCT
+ * EXCEPT(feature_date, row_order_peruser_persplit) 
+FROM (
+SELECT DISTINCT
+  processed_timestamp, 
+  user_pseudo_id,
+  data_split,
+  feature_date,
+  -- Now, I want to skip rows per user, per split every 1 day.
+  ROW_NUMBER() OVER (PARTITION BY user_pseudo_id, data_split, will_login ORDER BY feature_date ASC) AS row_order_peruser_persplit,
+  user_id,
+  user_ltv_revenue,
+  device_category,
+  device_mobile_brand_name,
+  device_mobile_model_name,
+  device_os,
+  device_language,
+  device_web_browser,
+  geo_sub_continent,
+  geo_country,
+  geo_region,
+  geo_city,
+  geo_metro,
+  last_traffic_source_medium,
+  last_traffic_source_name,
+  last_traffic_source_source,
+  first_traffic_source_medium,
+  first_traffic_source_name,
+  first_traffic_source_source,
+  has_signed_in_with_user_id,
+  scroll_50_past_1_day,
+  scroll_50_past_2_day,
+  scroll_50_past_3_day,
+  scroll_50_past_4_day,
+  scroll_50_past_5_day,
+  scroll_90_past_1_day,
+  scroll_90_past_2_day,
+  scroll_90_past_3_day,
+  scroll_90_past_4_day,
+  scroll_90_past_5_day,
+  view_search_results_past_1_day,
+  view_search_results_past_2_day,
+  view_search_results_past_3_day,
+  view_search_results_past_4_day,
+  view_search_results_past_5_day,
+  file_download_past_1_day,
+  file_download_past_2_day,
+  file_download_past_3_day,
+  file_download_past_4_day,
+  file_download_past_5_day,
+  recipe_add_to_list_past_1_day,
+  recipe_add_to_list_past_2_day,
+  recipe_add_to_list_past_3_day,
+  recipe_add_to_list_past_4_day,
+  recipe_add_to_list_past_5_day,
+  recipe_print_past_1_day,
+  recipe_print_past_2_day,
+  recipe_print_past_3_day,
+  recipe_print_past_4_day,
+  recipe_print_past_5_day,
+  sign_up_past_1_day,
+  sign_up_past_2_day,
+  sign_up_past_3_day,
+  sign_up_past_4_day,
+  sign_up_past_5_day,
+  recipe_favorite_past_1_day,
+  recipe_favorite_past_2_day,
+  recipe_favorite_past_3_day,
+  recipe_favorite_past_4_day,
+  recipe_favorite_past_5_day,
+  recipe_add_to_menu_past_1_day,
+  recipe_add_to_menu_past_2_day,
+  recipe_add_to_menu_past_3_day,
+  recipe_add_to_menu_past_4_day,
+  recipe_add_to_menu_past_5_day,
+  will_login
+FROM(
+SELECT DISTINCT
+  processed_timestamp, 
+  data_split,
+  feature_date,
+  user_pseudo_id,
+  user_id,
+  user_ltv_revenue,
+  device_category,
+  device_mobile_brand_name,
+  device_mobile_model_name,
+  device_os,
+  device_language,
+  device_web_browser,
+  geo_sub_continent,
+  geo_country,
+  geo_region,
+  geo_city,
+  geo_metro,
+  last_traffic_source_medium,
+  last_traffic_source_name,
+  last_traffic_source_source,
+  first_traffic_source_medium,
+  first_traffic_source_name,
+  first_traffic_source_source,
+  has_signed_in_with_user_id,
+  scroll_50_past_1_day,
+  scroll_50_past_2_day,
+  scroll_50_past_3_day,
+  scroll_50_past_4_day,
+  scroll_50_past_5_day,
+  scroll_90_past_1_day,
+  scroll_90_past_2_day,
+  scroll_90_past_3_day,
+  scroll_90_past_4_day,
+  scroll_90_past_5_day,
+  view_search_results_past_1_day,
+  view_search_results_past_2_day,
+  view_search_results_past_3_day,
+  view_search_results_past_4_day,
+  view_search_results_past_5_day,
+  file_download_past_1_day,
+  file_download_past_2_day,
+  file_download_past_3_day,
+  file_download_past_4_day,
+  file_download_past_5_day,
+  recipe_add_to_list_past_1_day,
+  recipe_add_to_list_past_2_day,
+  recipe_add_to_list_past_3_day,
+  recipe_add_to_list_past_4_day,
+  recipe_add_to_list_past_5_day,
+  recipe_print_past_1_day,
+  recipe_print_past_2_day,
+  recipe_print_past_3_day,
+  recipe_print_past_4_day,
+  recipe_print_past_5_day,
+  sign_up_past_1_day,
+  sign_up_past_2_day,
+  sign_up_past_3_day,
+  sign_up_past_4_day,
+  sign_up_past_5_day,
+  recipe_favorite_past_1_day,
+  recipe_favorite_past_2_day,
+  recipe_favorite_past_3_day,
+  recipe_favorite_past_4_day,
+  recipe_favorite_past_5_day,
+  recipe_add_to_menu_past_1_day,
+  recipe_add_to_menu_past_2_day,
+  recipe_add_to_menu_past_3_day,
+  recipe_add_to_menu_past_4_day,
+  recipe_add_to_menu_past_5_day,
+  will_login,
+  -- Number of rows per user, per day, per split. Only one row per user, per day, per slip.
+  ROW_NUMBER() OVER (PARTITION BY user_pseudo_id, feature_date, data_split, will_login ORDER BY feature_date DESC) AS row_order_peruser_perday_persplit
+  FROM `{{project_id}}.{{dataset}}.lead_score_propensity_training_5_1`
+)
+WHERE
+  row_order_peruser_perday_persplit = 1
+)
+WHERE
+  --Skipping windows of 5 days, which is the past window size.
+  MOD(row_order_peruser_persplit-1, 5) = 0;
+
+
+-- This is a view preparing rows for lead score propensity modelling looking back 5 days and looking ahead 1 day.
+-- This specifically filter rows which are most recent for each user.
+CREATE OR REPLACE VIEW `{{project_id}}.{{dataset}}.v_lead_score_propensity_training_5_1_last_window`
+(processed_timestamp, 
+  data_split,
+  user_pseudo_id,
+  user_id,
+  user_ltv_revenue,
+  device_category,
+  device_mobile_brand_name,
+  device_mobile_model_name,
+  device_os,
+  device_language,
+  device_web_browser,
+  geo_sub_continent,
+  geo_country,
+  geo_region,
+  geo_city,
+  geo_metro,
+  last_traffic_source_medium,
+  last_traffic_source_name,
+  last_traffic_source_source,
+  first_traffic_source_medium,
+  first_traffic_source_name,
+  first_traffic_source_source,
+  has_signed_in_with_user_id,
+  scroll_50_past_1_day,
+  scroll_50_past_2_day,
+  scroll_50_past_3_day,
+  scroll_50_past_4_day,
+  scroll_50_past_5_day,
+  scroll_90_past_1_day,
+  scroll_90_past_2_day,
+  scroll_90_past_3_day,
+  scroll_90_past_4_day,
+  scroll_90_past_5_day,
+  view_search_results_past_1_day,
+  view_search_results_past_2_day,
+  view_search_results_past_3_day,
+  view_search_results_past_4_day,
+  view_search_results_past_5_day,
+  file_download_past_1_day,
+  file_download_past_2_day,
+  file_download_past_3_day,
+  file_download_past_4_day,
+  file_download_past_5_day,
+  recipe_add_to_list_past_1_day,
+  recipe_add_to_list_past_2_day,
+  recipe_add_to_list_past_3_day,
+  recipe_add_to_list_past_4_day,
+  recipe_add_to_list_past_5_day,
+  recipe_print_past_1_day,
+  recipe_print_past_2_day,
+  recipe_print_past_3_day,
+  recipe_print_past_4_day,
+  recipe_print_past_5_day,
+  sign_up_past_1_day,
+  sign_up_past_2_day,
+  sign_up_past_3_day,
+  sign_up_past_4_day,
+  sign_up_past_5_day,
+  recipe_favorite_past_1_day,
+  recipe_favorite_past_2_day,
+  recipe_favorite_past_3_day,
+  recipe_favorite_past_4_day,
+  recipe_favorite_past_5_day,
+  recipe_add_to_menu_past_1_day,
+  recipe_add_to_menu_past_2_day,
+  recipe_add_to_menu_past_3_day,
+  recipe_add_to_menu_past_4_day,
+  recipe_add_to_menu_past_5_day,
+  will_login)
+OPTIONS(
+  --expiration_timestamp=TIMESTAMP_ADD(CURRENT_TIMESTAMP(), INTERVAL 48 HOUR),
+  friendly_name="v_lead_score_propensity_training_5_1_last_window",
+  description="View Lead Score Propensity Training dataset using 5 days back to predict 1 day ahead.",
+  labels=[("org_unit", "development")]
+) AS 
+SELECT DISTINCT
+  processed_timestamp, 
+  data_split,
+  user_pseudo_id,
+  user_id,
+  user_ltv_revenue,
+  device_category,
+  device_mobile_brand_name,
+  device_mobile_model_name,
+  device_os,
+  device_language,
+  device_web_browser,
+  geo_sub_continent,
+  geo_country,
+  geo_region,
+  geo_city,
+  geo_metro,
+  last_traffic_source_medium,
+  last_traffic_source_name,
+  last_traffic_source_source,
+  first_traffic_source_medium,
+  first_traffic_source_name,
+  first_traffic_source_source,
+  has_signed_in_with_user_id,
+  scroll_50_past_1_day,
+  scroll_50_past_2_day,
+  scroll_50_past_3_day,
+  scroll_50_past_4_day,
+  scroll_50_past_5_day,
+  scroll_90_past_1_day,
+  scroll_90_past_2_day,
+  scroll_90_past_3_day,
+  scroll_90_past_4_day,
+  scroll_90_past_5_day,
+  view_search_results_past_1_day,
+  view_search_results_past_2_day,
+  view_search_results_past_3_day,
+  view_search_results_past_4_day,
+  view_search_results_past_5_day,
+  file_download_past_1_day,
+  file_download_past_2_day,
+  file_download_past_3_day,
+  file_download_past_4_day,
+  file_download_past_5_day,
+  recipe_add_to_list_past_1_day,
+  recipe_add_to_list_past_2_day,
+  recipe_add_to_list_past_3_day,
+  recipe_add_to_list_past_4_day,
+  recipe_add_to_list_past_5_day,
+  recipe_print_past_1_day,
+  recipe_print_past_2_day,
+  recipe_print_past_3_day,
+  recipe_print_past_4_day,
+  recipe_print_past_5_day,
+  sign_up_past_1_day,
+  sign_up_past_2_day,
+  sign_up_past_3_day,
+  sign_up_past_4_day,
+  sign_up_past_5_day,
+  recipe_favorite_past_1_day,
+  recipe_favorite_past_2_day,
+  recipe_favorite_past_3_day,
+  recipe_favorite_past_4_day,
+  recipe_favorite_past_5_day,
+  recipe_add_to_menu_past_1_day,
+  recipe_add_to_menu_past_2_day,
+  recipe_add_to_menu_past_3_day,
+  recipe_add_to_menu_past_4_day,
+  recipe_add_to_menu_past_5_day,
+  will_login
+FROM(
+SELECT DISTINCT
+  processed_timestamp, 
+  data_split,
+  user_pseudo_id,
+  user_id,
+  user_ltv_revenue,
+  device_category,
+  device_mobile_brand_name,
+  device_mobile_model_name,
+  device_os,
+  device_language,
+  device_web_browser,
+  geo_sub_continent,
+  geo_country,
+  geo_region,
+  geo_city,
+  geo_metro,
+  last_traffic_source_medium,
+  last_traffic_source_name,
+  last_traffic_source_source,
+  first_traffic_source_medium,
+  first_traffic_source_name,
+  first_traffic_source_source,
+  has_signed_in_with_user_id,
+  scroll_50_past_1_day,
+  scroll_50_past_2_day,
+  scroll_50_past_3_day,
+  scroll_50_past_4_day,
+  scroll_50_past_5_day,
+  scroll_90_past_1_day,
+  scroll_90_past_2_day,
+  scroll_90_past_3_day,
+  scroll_90_past_4_day,
+  scroll_90_past_5_day,
+  view_search_results_past_1_day,
+  view_search_results_past_2_day,
+  view_search_results_past_3_day,
+  view_search_results_past_4_day,
+  view_search_results_past_5_day,
+  file_download_past_1_day,
+  file_download_past_2_day,
+  file_download_past_3_day,
+  file_download_past_4_day,
+  file_download_past_5_day,
+  recipe_add_to_list_past_1_day,
+  recipe_add_to_list_past_2_day,
+  recipe_add_to_list_past_3_day,
+  recipe_add_to_list_past_4_day,
+  recipe_add_to_list_past_5_day,
+  recipe_print_past_1_day,
+  recipe_print_past_2_day,
+  recipe_print_past_3_day,
+  recipe_print_past_4_day,
+  recipe_print_past_5_day,
+  sign_up_past_1_day,
+  sign_up_past_2_day,
+  sign_up_past_3_day,
+  sign_up_past_4_day,
+  sign_up_past_5_day,
+  recipe_favorite_past_1_day,
+  recipe_favorite_past_2_day,
+  recipe_favorite_past_3_day,
+  recipe_favorite_past_4_day,
+  recipe_favorite_past_5_day,
+  recipe_add_to_menu_past_1_day,
+  recipe_add_to_menu_past_2_day,
+  recipe_add_to_menu_past_3_day,
+  recipe_add_to_menu_past_4_day,
+  recipe_add_to_menu_past_5_day,
+  will_login,
+  ROW_NUMBER() OVER (PARTITION BY user_pseudo_id, data_split, will_login ORDER BY feature_date DESC) AS user_row_order
+  --ROW_NUMBER() OVER (PARTITION BY user_pseudo_id, data_split ORDER BY feature_date DESC) AS user_row_order
+  FROM `{{project_id}}.{{dataset}}.lead_score_propensity_training_5_1`
+)
+WHERE
+  user_row_order = 1;
+
+
+-- This is a view preparing rows for lead score propensity modelling looking back 5 days and looking ahead 1 day.
+-- This is to be used in case recently no logins are registered, and you don't have a way to train the classification model.
+CREATE OR REPLACE VIEW `{{project_id}}.{{dataset}}.v_lead_score_propensity_training_5_1_rare_logins`
+(processed_timestamp, 
+  data_split,
+  user_pseudo_id,
+  user_id,
+  user_ltv_revenue,
+  device_category,
+  device_mobile_brand_name,
+  device_mobile_model_name,
+  device_os,
+  device_language,
+  device_web_browser,
+  geo_sub_continent,
+  geo_country,
+  geo_region,
+  geo_city,
+  geo_metro,
+  last_traffic_source_medium,
+  last_traffic_source_name,
+  last_traffic_source_source,
+  first_traffic_source_medium,
+  first_traffic_source_name,
+  first_traffic_source_source,
+  has_signed_in_with_user_id,
+  scroll_50_past_1_day,
+  scroll_50_past_2_day,
+  scroll_50_past_3_day,
+  scroll_50_past_4_day,
+  scroll_50_past_5_day,
+  scroll_90_past_1_day,
+  scroll_90_past_2_day,
+  scroll_90_past_3_day,
+  scroll_90_past_4_day,
+  scroll_90_past_5_day,
+  view_search_results_past_1_day,
+  view_search_results_past_2_day,
+  view_search_results_past_3_day,
+  view_search_results_past_4_day,
+  view_search_results_past_5_day,
+  file_download_past_1_day,
+  file_download_past_2_day,
+  file_download_past_3_day,
+  file_download_past_4_day,
+  file_download_past_5_day,
+  recipe_add_to_list_past_1_day,
+  recipe_add_to_list_past_2_day,
+  recipe_add_to_list_past_3_day,
+  recipe_add_to_list_past_4_day,
+  recipe_add_to_list_past_5_day,
+  recipe_print_past_1_day,
+  recipe_print_past_2_day,
+  recipe_print_past_3_day,
+  recipe_print_past_4_day,
+  recipe_print_past_5_day,
+  sign_up_past_1_day,
+  sign_up_past_2_day,
+  sign_up_past_3_day,
+  sign_up_past_4_day,
+  sign_up_past_5_day,
+  recipe_favorite_past_1_day,
+  recipe_favorite_past_2_day,
+  recipe_favorite_past_3_day,
+  recipe_favorite_past_4_day,
+  recipe_favorite_past_5_day,
+  recipe_add_to_menu_past_1_day,
+  recipe_add_to_menu_past_2_day,
+  recipe_add_to_menu_past_3_day,
+  recipe_add_to_menu_past_4_day,
+  recipe_add_to_menu_past_5_day,
+  will_login)
+OPTIONS(
+  --expiration_timestamp=TIMESTAMP_ADD(CURRENT_TIMESTAMP(), INTERVAL 48 HOUR),
+  friendly_name="v_lead_score_propensity_training_5_1_rare_logins",
+  description="View Lead Score Propensity Training dataset using 5 days back to predict 1 day ahead.",
+  labels=[("org_unit", "development")]
+) AS 
+SELECT DISTINCT
+    processed_timestamp, 
+    data_split,
+    user_pseudo_id,
+    user_id,
+    user_ltv_revenue,
+    device_category,
+    device_mobile_brand_name,
+    device_mobile_model_name,
+    device_os,
+    device_language,
+    device_web_browser,
+    geo_sub_continent,
+    geo_country,
+    geo_region,
+    geo_city,
+    geo_metro,
+    last_traffic_source_medium,
+    last_traffic_source_name,
+    last_traffic_source_source,
+    first_traffic_source_medium,
+    first_traffic_source_name,
+    first_traffic_source_source,
+    has_signed_in_with_user_id,
+    scroll_50_past_1_day,
+    scroll_50_past_2_day,
+    scroll_50_past_3_day,
+    scroll_50_past_4_day,
+    scroll_50_past_5_day,
+    scroll_90_past_1_day,
+    scroll_90_past_2_day,
+    scroll_90_past_3_day,
+    scroll_90_past_4_day,
+    scroll_90_past_5_day,
+    view_search_results_past_1_day,
+    view_search_results_past_2_day,
+    view_search_results_past_3_day,
+    view_search_results_past_4_day,
+    view_search_results_past_5_day,
+    file_download_past_1_day,
+    file_download_past_2_day,
+    file_download_past_3_day,
+    file_download_past_4_day,
+    file_download_past_5_day,
+    recipe_add_to_list_past_1_day,
+    recipe_add_to_list_past_2_day,
+    recipe_add_to_list_past_3_day,
+    recipe_add_to_list_past_4_day,
+    recipe_add_to_list_past_5_day,
+    recipe_print_past_1_day,
+    recipe_print_past_2_day,
+    recipe_print_past_3_day,
+    recipe_print_past_4_day,
+    recipe_print_past_5_day,
+    sign_up_past_1_day,
+    sign_up_past_2_day,
+    sign_up_past_3_day,
+    sign_up_past_4_day,
+    sign_up_past_5_day,
+    recipe_favorite_past_1_day,
+    recipe_favorite_past_2_day,
+    recipe_favorite_past_3_day,
+    recipe_favorite_past_4_day,
+    recipe_favorite_past_5_day,
+    recipe_add_to_menu_past_1_day,
+    recipe_add_to_menu_past_2_day,
+    recipe_add_to_menu_past_3_day,
+    recipe_add_to_menu_past_4_day,
+    recipe_add_to_menu_past_5_day,
+    will_login
+  FROM
+  (SELECT DISTINCT
+  *
+  FROM `{{project_id}}.{{dataset}}.v_lead_score_propensity_training_5_1_last_window`
+    )
+  UNION ALL
+  (
+  SELECT DISTINCT
+   * EXCEPT(user_row_order, feature_date)
+  FROM(
+  SELECT DISTINCT
+    *,
+    ROW_NUMBER() OVER (PARTITION BY user_pseudo_id, data_split ORDER BY feature_date DESC) AS user_row_order
+    FROM `{{project_id}}.{{dataset}}.lead_score_propensity_training_5_1`
+    WHERE will_login = 1
+  )
+  WHERE
+    user_row_order = 1
+  LIMIT 100
+    )
+;
\ No newline at end of file
diff --git a/sql/procedure/user_rolling_window_lead_metrics.sqlx b/sql/procedure/user_rolling_window_lead_metrics.sqlx
new file mode 100644
index 00000000..5e15edce
--- /dev/null
+++ b/sql/procedure/user_rolling_window_lead_metrics.sqlx
@@ -0,0 +1,520 @@
+-- Copyright 2023 Google LLC
+--
+-- Licensed under the Apache License, Version 2.0 (the "License");
+-- you may not use this file except in compliance with the License.
+-- You may obtain a copy of the License at
+--
+--     http://www.apache.org/licenses/LICENSE-2.0
+--
+-- Unless required by applicable law or agreed to in writing, software
+-- distributed under the License is distributed on an "AS IS" BASIS,
+-- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+-- See the License for the specific language governing permissions and
+-- limitations under the License.
+
+-- Setting procedure to lookback from the day before `input_date` until the day before `end_date`
+-- Subtract one day from `input_date`
+SET input_date = DATE_SUB(input_date, INTERVAL 1 DAY);
+-- Subtract one day from `end_date`
+SET end_date = DATE_SUB(end_date, INTERVAL 1 DAY);
+
+-- Past User metrics: 1-day scroll_50 events per user, 2-5-day scroll_50 events per user
+-- Create a temporary table `rolling_scroll_50_past_days` to store the rolling scroll_50 events count for each user
+CREATE OR REPLACE TEMP TABLE rolling_scroll_50_past_days AS (
+SELECT
+  -- User's unique identifier
+  user_pseudo_id,
+  -- Calculate the number of scroll_50 made in the past 1 day
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 1 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS scroll_50_past_1_day,
+  -- Calculate the number of scroll_50 made in the past 2 days
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 2 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS scroll_50_past_2_day,
+  -- Calculate the number of scroll_50 made in the past 3 days
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 3 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS scroll_50_past_3_day,
+  -- Calculate the number of scroll_50 made in the past 4 days
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 4 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS scroll_50_past_4_day,
+  -- Calculate the number of scroll_50 made in the past 5 days
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 5 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS scroll_50_past_5_day
+FROM `{{mds_project_id}}.{{mds_dataset}}.event` as E
+-- Filter events within the defined date range
+WHERE event_date BETWEEN end_date AND input_date
+-- Filter for scroll_50 events
+AND event_name='scroll_50'
+-- Ensure valid session ID
+AND ga_session_id IS NOT NULL
+-- Group the results by user pseudo ID
+GROUP BY user_pseudo_id
+);
+
+-- Past User metrics: 1-day scroll_90 per user, 2-5-day scroll_90 per user
+-- Create a temporary table `rolling_scroll_90_past_days` to store the rolling visit count for each user
+CREATE OR REPLACE TEMP TABLE rolling_scroll_90_past_days AS (
+SELECT
+  -- User's unique identifier
+  user_pseudo_id,
+  -- Calculate the number of scroll_90 made in the past 1 day
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 1 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS scroll_90_past_1_day,
+  -- Calculate the number of scroll_90 made in the past 2 days
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 2 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS scroll_90_past_2_day,
+  -- Calculate the number of scroll_90 made in the past 3 days
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 3 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS scroll_90_past_3_day,
+  -- Calculate the number of scroll_90 made in the past 4 days
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 4 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS scroll_90_past_4_day,
+  -- Calculate the number of scroll_90 made in the past 5 days
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 5 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS scroll_90_past_5_day
+FROM `{{mds_project_id}}.{{mds_dataset}}.event` as E
+-- Filter events within the defined date range
+WHERE event_date BETWEEN end_date AND input_date
+-- Filter for scroll_90 events
+AND event_name='scroll_90'
+-- Ensure valid session ID
+AND ga_session_id IS NOT NULL
+-- Group the results by user pseudo ID
+GROUP BY user_pseudo_id
+);
+
+-- Past User metrics: 1-day view_search_results per user, 2-5-day view_search_results per user
+-- Create a temporary table `rolling_view_search_results_past_days` to store the rolling view item count for each user
+CREATE OR REPLACE TEMP TABLE rolling_view_search_results_past_days AS (
+SELECT
+  -- User's unique identifier
+  user_pseudo_id,
+  -- Calculate the number of times the user view_search_results in the past 1 day
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 1 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS view_search_results_past_1_day,
+  -- Calculate the number of times the user view_search_results in the past 2 days
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 2 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS view_search_results_past_2_day,
+  -- Calculate the number of times the user view_search_results in the past 3 days
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 3 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS view_search_results_past_3_day,
+  -- Calculate the number of times the user view_search_results in the past 4 days
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 4 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS view_search_results_past_4_day,
+  -- Calculate the number of times the user view_search_results in the past 5 days
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 5 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS view_search_results_past_5_day
+FROM `{{mds_project_id}}.{{mds_dataset}}.event` as E
+-- Filter events within the defined date range
+WHERE event_date BETWEEN end_date AND input_date
+-- Filter for view_search_results events
+AND event_name='view_search_results'
+-- Ensure valid session ID
+AND ga_session_id IS NOT NULL
+-- Group the results by user pseudo ID
+GROUP BY user_pseudo_id
+);
+
+-- Past User metrics: 1-day file_download per user, 2-5-day file_download
+-- Create a temporary table `rolling_file_download_past_days` to store the rolling file_download count for each user
+CREATE OR REPLACE TEMP TABLE rolling_file_download_past_days AS (
+SELECT
+  -- User's unique identifier
+  user_pseudo_id,
+  -- Calculate the number of times the user file_download in the past 1 day
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 1 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS file_download_past_1_day,
+  -- Calculate the number of times the user file_download in the past 2 days
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 2 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS file_download_past_2_day,
+  -- Calculate the number of times the user file_download in the past 3 days
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 3 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS file_download_past_3_day,
+  -- Calculate the number of times the user file_download in the past 4 days
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 4 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS file_download_past_4_day,
+  -- Calculate the number of times the user file_download in the past 5 days
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 5 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS file_download_past_5_day
+FROM `{{mds_project_id}}.{{mds_dataset}}.event` as E
+-- Filter events within the defined date range
+WHERE event_date BETWEEN end_date AND input_date
+-- Filter for file_download events
+AND event_name='file_download'
+-- Ensure valid session ID
+AND ga_session_id IS NOT NULL
+-- Group the results by user pseudo ID
+GROUP BY user_pseudo_id
+);
+
+-- Past User metrics: 1-day recipe_add_to_list per user, 2-5-day recipe_add_to_list per user
+-- 2-5-day recipe_add_to_list per user
+CREATE OR REPLACE TEMP TABLE rolling_recipe_add_to_list_past_days AS (
+SELECT
+  -- User pseudo ID, a unique identifier for the user
+  user_pseudo_id,
+  -- Number of recipe_add_to_list in the past 1st day, calculated using a window function partitioned by user_pseudo_id
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 1 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS recipe_add_to_list_past_1_day,
+  -- Number of recipe_add_to_list in the past 2nd day, calculated using a window function partitioned by user_pseudo_id
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 2 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS recipe_add_to_list_past_2_day,
+  -- Number of recipe_add_to_list in the past 3rd day, calculated using a window function partitioned by user_pseudo_id
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 3 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS recipe_add_to_list_past_3_day,
+  -- Number of recipe_add_to_list in the past 4th day, calculated using a window function partitioned by user_pseudo_id
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 4 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS recipe_add_to_list_past_4_day,
+  -- Number of recipe_add_to_list in the past 5th day, calculated using a window function partitioned by user_pseudo_id
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 5 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS recipe_add_to_list_past_5_day
+FROM `{{mds_project_id}}.{{mds_dataset}}.event` as E
+-- Filter events within the defined date range
+WHERE event_date BETWEEN end_date AND input_date
+-- Filter for recipe_add_to_list events
+AND event_name='recipe_add_to_list'
+-- Ensure valid session ID
+AND ga_session_id IS NOT NULL
+-- Group the results by user pseudo ID
+GROUP BY user_pseudo_id
+);
+
+-- Past User metrics: 1-day recipe_print per user, 2-5-day recipe_print per user
+CREATE OR REPLACE TEMP TABLE rolling_recipe_print_past_days AS (
+SELECT
+  -- User pseudo ID, a unique identifier for the user
+  user_pseudo_id,
+  -- Number of recipe_print in the past 1st day, calculated using a window function partitioned by user_pseudo_id
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 1 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS recipe_print_past_1_day,
+  -- Number of recipe_print in the past 2nd day, calculated using a window function partitioned by user_pseudo_id
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 2 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS recipe_print_past_2_day,
+  -- Number of recipe_print in the past 3rd day, calculated using a window function partitioned by user_pseudo_id
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 3 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS recipe_print_past_3_day,
+  -- Number of recipe_print in the past 4th day, calculated using a window function partitioned by user_pseudo_id
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 4 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS recipe_print_past_4_day,
+  -- Number of recipe_print in the past 5th day, calculated using a window function partitioned by user_pseudo_id
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 5 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS recipe_print_past_5_day
+FROM `{{mds_project_id}}.{{mds_dataset}}.event` as E
+-- Filter events within the defined date range
+WHERE event_date BETWEEN end_date AND input_date
+-- Filter for recipe_print events
+AND event_name='recipe_print'
+-- Ensure valid session ID
+AND ga_session_id IS NOT NULL
+-- Group the results by user pseudo ID
+GROUP BY user_pseudo_id
+);
+
+-- Past User metrics: 1-day sign_up per user, 2-5-day sign_up per user
+-- 2-5-day sign_up per user
+CREATE OR REPLACE TEMP TABLE rolling_sign_up_past_days AS (
+SELECT
+  -- User pseudo ID, a unique identifier for the user
+  user_pseudo_id,
+  -- Number of sign_up in the past 1st day, calculated using a window function partitioned by user_pseudo_id
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 1 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS sign_up_past_1_day,
+  -- Number of sign_up in the past 2nd day, calculated using a window function partitioned by user_pseudo_id
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 2 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS sign_up_past_2_day,
+  -- Number of sign_up in the past 3rd day, calculated using a window function partitioned by user_pseudo_id
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 3 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS sign_up_past_3_day,
+  -- Number of sign_up in the past 4th day, calculated using a window function partitioned by user_pseudo_id
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 4 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS sign_up_past_4_day,
+  -- Number of sign_up in the past 5th day, calculated using a window function partitioned by user_pseudo_id
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 5 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS sign_up_past_5_day
+FROM `{{mds_project_id}}.{{mds_dataset}}.event` as E
+-- Filter events within the defined date range
+WHERE event_date BETWEEN end_date AND input_date
+-- Filter for sign_up events
+AND event_name='sign_up'
+-- Ensure valid session ID
+AND ga_session_id IS NOT NULL
+-- Group the results by user pseudo ID
+GROUP BY user_pseudo_id
+);
+
+
+-- Past User metrics: 1-day recipe_favorite per user, 2-5-day recipe_favorite per user
+-- 2-5-day recipe_favorite per user
+CREATE OR REPLACE TEMP TABLE rolling_recipe_favorite_past_days AS (
+SELECT
+  -- User pseudo ID, a unique identifier for the user
+  user_pseudo_id,
+  -- Number of recipe_favorite in the past 1st day, calculated using a window function partitioned by user_pseudo_id
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 1 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS recipe_favorite_past_1_day,
+  -- Number of recipe_favorite in the past 2nd day, calculated using a window function partitioned by user_pseudo_id
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 2 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS recipe_favorite_past_2_day,
+  -- Number of recipe_favorite in the past 3rd day, calculated using a window function partitioned by user_pseudo_id
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 3 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS recipe_favorite_past_3_day,
+  -- Number of recipe_favorite in the past 4th day, calculated using a window function partitioned by user_pseudo_id
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 4 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS recipe_favorite_past_4_day,
+  -- Number of recipe_favorite in the past 5th day, calculated using a window function partitioned by user_pseudo_id
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 5 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS recipe_favorite_past_5_day
+FROM `{{mds_project_id}}.{{mds_dataset}}.event` as E
+-- Filter events within the defined date range
+WHERE event_date BETWEEN end_date AND input_date
+-- Filter for recipe_favorite events
+AND event_name='recipe_favorite'
+-- Ensure valid session ID
+AND ga_session_id IS NOT NULL
+-- Group the results by user pseudo ID
+GROUP BY user_pseudo_id
+);
+
+
+-- Past User metrics: 1-day recipe_add_to_menu per user, 2-5-day recipe_add_to_menu per user
+-- 2-5-day recipe_add_to_menu per user
+CREATE OR REPLACE TEMP TABLE rolling_recipe_add_to_menu_past_days AS (
+SELECT
+  -- User pseudo ID, a unique identifier for the user
+  user_pseudo_id,
+  -- Number of recipe_add_to_menu in the past 1st day, calculated using a window function partitioned by user_pseudo_id
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 1 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS recipe_add_to_menu_past_1_day,
+  -- Number of recipe_add_to_menu in the past 2nd day, calculated using a window function partitioned by user_pseudo_id
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 2 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS recipe_add_to_menu_past_2_day,
+  -- Number of recipe_add_to_menu in the past 3rd day, calculated using a window function partitioned by user_pseudo_id
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 3 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS recipe_add_to_menu_past_3_day,
+  -- Number of recipe_add_to_menu in the past 4th day, calculated using a window function partitioned by user_pseudo_id
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 4 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS recipe_add_to_menu_past_4_day,
+  -- Number of recipe_add_to_menu in the past 5th day, calculated using a window function partitioned by user_pseudo_id
+  MAX(COUNT(DISTINCT CASE WHEN DATE_DIFF(input_date, event_date, DAY) = 5 THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id) AS recipe_add_to_menu_past_5_day
+FROM `{{mds_project_id}}.{{mds_dataset}}.event` as E
+-- Filter events within the defined date range
+WHERE event_date BETWEEN end_date AND input_date
+-- Filter for recipe_add_to_menu events
+AND event_name='recipe_add_to_menu'
+-- Ensure valid session ID
+AND ga_session_id IS NOT NULL
+-- Group the results by user pseudo ID
+GROUP BY user_pseudo_id
+);
+
+
+-- All users in the platform
+CREATE OR REPLACE TEMP TABLE events_users_days as (
+  SELECT DISTINCT
+  -- User pseudo ID
+  Users.user_pseudo_id,
+  -- distinct event date
+  Days.event_date as event_date
+  FROM `{{mds_project_id}}.{{mds_dataset}}.event` Users
+  -- 'Days' is an alias for a temporary table containing distinct event dates
+  CROSS JOIN 
+  (SELECT DISTINCT event_date FROM `{{mds_project_id}}.{{mds_dataset}}.event`) Days
+  INNER JOIN `{{mds_project_id}}.{{mds_dataset}}.device` as D
+  ON Users.device_type_id = D.device_type_id 
+  -- Exclude events without a valid session ID
+  WHERE Users.ga_session_id IS NOT NULL
+  -- Exclude events without a valid device operating system
+  AND D.device_os IS NOT NULL
+  -- Filter events within the defined date range
+  AND Days.event_date BETWEEN end_date AND input_date)
+;
+
+-- Create a temporary table to store data for the target table
+CREATE OR REPLACE TEMP TABLE DataForTargetTable AS
+SELECT DISTINCT
+  -- Current timestamp
+  CURRENT_TIMESTAMP() AS processed_timestamp,
+  -- Feature date
+  input_date AS feature_date,
+  -- User pseudo ID
+  EUD.user_pseudo_id,
+  COALESCE(scroll_50_past_1_day,0) AS scroll_50_past_1_day,
+  COALESCE(scroll_50_past_2_day,0) AS scroll_50_past_2_day,
+  COALESCE(scroll_50_past_3_day,0) AS scroll_50_past_3_day,
+  COALESCE(scroll_50_past_4_day,0) AS scroll_50_past_4_day,
+  COALESCE(scroll_50_past_5_day,0) AS scroll_50_past_5_day,
+  COALESCE(scroll_90_past_1_day,0) AS scroll_90_past_1_day,
+  COALESCE(scroll_90_past_2_day,0) AS scroll_90_past_2_day,
+  COALESCE(scroll_90_past_3_day,0) AS scroll_90_past_3_day,
+  COALESCE(scroll_90_past_4_day,0) AS scroll_90_past_4_day,
+  COALESCE(scroll_90_past_5_day,0) AS scroll_90_past_5_day,
+  COALESCE(view_search_results_past_1_day,0) AS view_search_results_past_1_day,
+  COALESCE(view_search_results_past_2_day,0) AS view_search_results_past_2_day,
+  COALESCE(view_search_results_past_3_day,0) AS view_search_results_past_3_day,
+  COALESCE(view_search_results_past_4_day,0) AS view_search_results_past_4_day,
+  COALESCE(view_search_results_past_5_day,0) AS view_search_results_past_5_day,
+  COALESCE(file_download_past_1_day,0) AS file_download_past_1_day,
+  COALESCE(file_download_past_2_day,0) AS file_download_past_2_day,
+  COALESCE(file_download_past_3_day,0) AS file_download_past_3_day,
+  COALESCE(file_download_past_4_day,0) AS file_download_past_4_day,
+  COALESCE(file_download_past_5_day,0) AS file_download_past_5_day,
+  COALESCE(recipe_add_to_list_past_1_day,0) AS recipe_add_to_list_past_1_day,
+  COALESCE(recipe_add_to_list_past_2_day,0) AS recipe_add_to_list_past_2_day,
+  COALESCE(recipe_add_to_list_past_3_day,0) AS recipe_add_to_list_past_3_day,
+  COALESCE(recipe_add_to_list_past_4_day,0) AS recipe_add_to_list_past_4_day,
+  COALESCE(recipe_add_to_list_past_5_day,0) AS recipe_add_to_list_past_5_day,
+  COALESCE(recipe_print_past_1_day,0) AS recipe_print_past_1_day,
+  COALESCE(recipe_print_past_2_day,0) AS recipe_print_past_2_day,
+  COALESCE(recipe_print_past_3_day,0) AS recipe_print_past_3_day,
+  COALESCE(recipe_print_past_4_day,0) AS recipe_print_past_4_day,
+  COALESCE(recipe_print_past_5_day,0) AS recipe_print_past_5_day,
+  COALESCE(sign_up_past_1_day,0) AS sign_up_past_1_day,
+  COALESCE(sign_up_past_2_day,0) AS sign_up_past_2_day,
+  COALESCE(sign_up_past_3_day,0) AS sign_up_past_3_day,
+  COALESCE(sign_up_past_4_day,0) AS sign_up_past_4_day,
+  COALESCE(sign_up_past_5_day,0) AS sign_up_past_5_day,
+  COALESCE(recipe_favorite_past_1_day,0) AS recipe_favorite_past_1_day,
+  COALESCE(recipe_favorite_past_2_day,0) AS recipe_favorite_past_2_day,
+  COALESCE(recipe_favorite_past_3_day,0) AS recipe_favorite_past_3_day,
+  COALESCE(recipe_favorite_past_4_day,0) AS recipe_favorite_past_4_day,
+  COALESCE(recipe_favorite_past_5_day,0) AS recipe_favorite_past_5_day,
+  COALESCE(recipe_add_to_menu_past_1_day,0) AS recipe_add_to_menu_past_1_day,
+  COALESCE(recipe_add_to_menu_past_2_day,0) AS recipe_add_to_menu_past_2_day,
+  COALESCE(recipe_add_to_menu_past_3_day,0) AS recipe_add_to_menu_past_3_day,
+  COALESCE(recipe_add_to_menu_past_4_day,0) AS recipe_add_to_menu_past_4_day,
+  COALESCE(recipe_add_to_menu_past_5_day,0) AS recipe_add_to_menu_past_5_day
+  FROM events_users_days AS EUD
+  FULL OUTER JOIN rolling_scroll_50_past_days AS A
+  ON EUD.user_pseudo_id = A.user_pseudo_id
+  FULL OUTER JOIN rolling_scroll_90_past_days AS B
+  ON EUD.user_pseudo_id = B.user_pseudo_id
+  FULL OUTER JOIN rolling_view_search_results_past_days AS C
+  ON EUD.user_pseudo_id = C.user_pseudo_id
+  FULL OUTER JOIN rolling_file_download_past_days AS D
+  ON EUD.user_pseudo_id = D.user_pseudo_id
+  FULL OUTER JOIN rolling_recipe_add_to_list_past_days AS E
+  ON EUD.user_pseudo_id = E.user_pseudo_id
+  FULL OUTER JOIN rolling_recipe_print_past_days AS F
+  ON EUD.user_pseudo_id = F.user_pseudo_id
+  FULL OUTER JOIN rolling_sign_up_past_days AS G
+  ON EUD.user_pseudo_id = G.user_pseudo_id
+  FULL OUTER JOIN rolling_recipe_favorite_past_days AS H
+  ON EUD.user_pseudo_id = H.user_pseudo_id
+  FULL OUTER JOIN rolling_recipe_add_to_menu_past_days AS I
+  ON EUD.user_pseudo_id = I.user_pseudo_id
+  -- Exclude rows without a valid user pseudo ID
+  WHERE EUD.user_pseudo_id IS NOT NULL
+  ;
+
+-- Merge data into the target table
+MERGE `{{project_id}}.{{dataset}}.{{insert_table}}` I
+USING DataForTargetTable T
+ON I.feature_date = T.feature_date
+  AND I.user_pseudo_id = T.user_pseudo_id
+WHEN MATCHED THEN
+  UPDATE SET 
+    -- Update the processed timestamp and rolling window features
+    I.processed_timestamp = T.processed_timestamp,
+    I.scroll_50_past_1_day = T.scroll_50_past_1_day,
+    I.scroll_50_past_2_day = T.scroll_50_past_2_day,
+    I.scroll_50_past_3_day = T.scroll_50_past_3_day,
+    I.scroll_50_past_4_day = T.scroll_50_past_4_day,
+    I.scroll_50_past_5_day = T.scroll_50_past_5_day,
+    I.scroll_90_past_1_day = T.scroll_90_past_1_day,
+    I.scroll_90_past_2_day = T.scroll_90_past_2_day,
+    I.scroll_90_past_3_day = T.scroll_90_past_3_day,
+    I.scroll_90_past_4_day = T.scroll_90_past_4_day,
+    I.scroll_90_past_5_day = T.scroll_90_past_5_day,
+    I.view_search_results_past_1_day = T.view_search_results_past_1_day,
+    I.view_search_results_past_2_day = T.view_search_results_past_2_day,
+    I.view_search_results_past_3_day = T.view_search_results_past_3_day,
+    I.view_search_results_past_4_day = T.view_search_results_past_4_day,
+    I.view_search_results_past_5_day = T.view_search_results_past_5_day,
+    I.file_download_past_1_day = T.file_download_past_1_day,
+    I.file_download_past_2_day = T.file_download_past_2_day,
+    I.file_download_past_3_day = T.file_download_past_3_day,
+    I.file_download_past_4_day = T.file_download_past_4_day,
+    I.file_download_past_5_day = T.file_download_past_5_day,
+    I.recipe_add_to_list_past_1_day = T.recipe_add_to_list_past_1_day,
+    I.recipe_add_to_list_past_2_day = T.recipe_add_to_list_past_2_day,
+    I.recipe_add_to_list_past_3_day = T.recipe_add_to_list_past_3_day,
+    I.recipe_add_to_list_past_4_day = T.recipe_add_to_list_past_4_day,
+    I.recipe_add_to_list_past_5_day = T.recipe_add_to_list_past_5_day,
+    I.recipe_print_past_1_day = T.recipe_print_past_1_day,
+    I.recipe_print_past_2_day = T.recipe_print_past_2_day,
+    I.recipe_print_past_3_day = T.recipe_print_past_3_day,
+    I.recipe_print_past_4_day = T.recipe_print_past_4_day,
+    I.recipe_print_past_5_day = T.recipe_print_past_5_day,
+    I.sign_up_past_1_day = T.sign_up_past_1_day,
+    I.sign_up_past_2_day = T.sign_up_past_2_day,
+    I.sign_up_past_3_day = T.sign_up_past_3_day,
+    I.sign_up_past_4_day = T.sign_up_past_4_day,
+    I.sign_up_past_5_day = T.sign_up_past_5_day,
+    I.recipe_favorite_past_1_day = T.recipe_favorite_past_1_day,
+    I.recipe_favorite_past_2_day = T.recipe_favorite_past_2_day,
+    I.recipe_favorite_past_3_day = T.recipe_favorite_past_3_day,
+    I.recipe_favorite_past_4_day = T.recipe_favorite_past_4_day,
+    I.recipe_favorite_past_5_day = T.recipe_favorite_past_5_day,
+    I.recipe_add_to_menu_past_1_day = T.recipe_add_to_menu_past_1_day,
+    I.recipe_add_to_menu_past_2_day = T.recipe_add_to_menu_past_2_day,
+    I.recipe_add_to_menu_past_3_day = T.recipe_add_to_menu_past_3_day,
+    I.recipe_add_to_menu_past_4_day = T.recipe_add_to_menu_past_4_day,
+    I.recipe_add_to_menu_past_5_day = T.recipe_add_to_menu_past_5_day
+WHEN NOT MATCHED THEN
+  INSERT 
+    (processed_timestamp,
+     feature_date,
+     user_pseudo_id,
+     scroll_50_past_1_day,
+     scroll_50_past_2_day,
+     scroll_50_past_3_day,
+     scroll_50_past_4_day,
+     scroll_50_past_5_day,
+     scroll_90_past_1_day,
+     scroll_90_past_2_day,
+     scroll_90_past_3_day,
+     scroll_90_past_4_day,
+     scroll_90_past_5_day,
+     view_search_results_past_1_day,
+     view_search_results_past_2_day,
+     view_search_results_past_3_day,
+     view_search_results_past_4_day,
+     view_search_results_past_5_day,
+     file_download_past_1_day,
+     file_download_past_2_day,
+     file_download_past_3_day,
+     file_download_past_4_day,
+     file_download_past_5_day,
+     recipe_add_to_list_past_1_day,
+     recipe_add_to_list_past_2_day,
+     recipe_add_to_list_past_3_day,
+     recipe_add_to_list_past_4_day,
+     recipe_add_to_list_past_5_day,
+     recipe_print_past_1_day,
+     recipe_print_past_2_day,
+     recipe_print_past_3_day,
+     recipe_print_past_4_day,
+     recipe_print_past_5_day,
+     sign_up_past_1_day,
+     sign_up_past_2_day,
+     sign_up_past_3_day,
+     sign_up_past_4_day,
+     sign_up_past_5_day,
+     recipe_favorite_past_1_day,
+     recipe_favorite_past_2_day,
+     recipe_favorite_past_3_day,
+     recipe_favorite_past_4_day,
+     recipe_favorite_past_5_day,
+     recipe_add_to_menu_past_1_day,
+     recipe_add_to_menu_past_2_day,
+     recipe_add_to_menu_past_3_day,
+     recipe_add_to_menu_past_4_day,
+     recipe_add_to_menu_past_5_day)
+  VALUES
+    (T.processed_timestamp,
+     T.feature_date,
+     T.user_pseudo_id,
+     T.scroll_50_past_1_day,
+     T.scroll_50_past_2_day,
+     T.scroll_50_past_3_day,
+     T.scroll_50_past_4_day,
+     T.scroll_50_past_5_day,
+     T.scroll_90_past_1_day,
+     T.scroll_90_past_2_day,
+     T.scroll_90_past_3_day,
+     T.scroll_90_past_4_day,
+     T.scroll_90_past_5_day,
+     T.view_search_results_past_1_day,
+     T.view_search_results_past_2_day,
+     T.view_search_results_past_3_day,
+     T.view_search_results_past_4_day,
+     T.view_search_results_past_5_day,
+     T.file_download_past_1_day,
+     T.file_download_past_2_day,
+     T.file_download_past_3_day,
+     T.file_download_past_4_day,
+     T.file_download_past_5_day,
+     T.recipe_add_to_list_past_1_day,
+     T.recipe_add_to_list_past_2_day,
+     T.recipe_add_to_list_past_3_day,
+     T.recipe_add_to_list_past_4_day,
+     T.recipe_add_to_list_past_5_day,
+     T.recipe_print_past_1_day,
+     T.recipe_print_past_2_day,
+     T.recipe_print_past_3_day,
+     T.recipe_print_past_4_day,
+     T.recipe_print_past_5_day,
+     T.sign_up_past_1_day,
+     T.sign_up_past_2_day,
+     T.sign_up_past_3_day,
+     T.sign_up_past_4_day,
+     T.sign_up_past_5_day,
+     T.recipe_favorite_past_1_day,
+     T.recipe_favorite_past_2_day,
+     T.recipe_favorite_past_3_day,
+     T.recipe_favorite_past_4_day,
+     T.recipe_favorite_past_5_day,
+     T.recipe_add_to_menu_past_1_day,
+     T.recipe_add_to_menu_past_2_day,
+     T.recipe_add_to_menu_past_3_day,
+     T.recipe_add_to_menu_past_4_day,
+     T.recipe_add_to_menu_past_5_day)
+;
+
+-- Set a variable to track the number of rows added
+SET rows_added = (SELECT COUNT(DISTINCT user_pseudo_id) FROM `{{project_id}}.{{dataset}}.{{insert_table}}`);
diff --git a/sql/query/create_gemini_model.sqlx b/sql/query/create_gemini_model.sqlx
index 84612d8f..4e365c4a 100644
--- a/sql/query/create_gemini_model.sqlx
+++ b/sql/query/create_gemini_model.sqlx
@@ -18,6 +18,6 @@
 -- Your supervised tuning computations also occur in the europe-west4 region, because that's where TPU resources are located.
 
 -- Create a {{endpoint_name}} model using a remote connection to {{region}}.{{connection_name}}
-CREATE OR REPLACE MODEL `{{project_id}}.{{dataset}}.{{model_name}}`
+CREATE MODEL IF NOT EXISTS `{{project_id}}.{{dataset}}.{{model_name}}`
 REMOTE WITH CONNECTION `{{project_id}}.{{region}}.{{connection_name}}`
 OPTIONS (ENDPOINT = '{{endpoint_name}}');
\ No newline at end of file
diff --git a/sql/query/invoke_backfill_lead_score_propensity_label.sqlx b/sql/query/invoke_backfill_lead_score_propensity_label.sqlx
new file mode 100644
index 00000000..84ef7fc6
--- /dev/null
+++ b/sql/query/invoke_backfill_lead_score_propensity_label.sqlx
@@ -0,0 +1,116 @@
+-- Copyright 2023 Google LLC
+--
+-- Licensed under the Apache License, Version 2.0 (the "License");
+-- you may not use this file except in compliance with the License.
+-- You may obtain a copy of the License at
+--
+--     http://www.apache.org/licenses/LICENSE-2.0
+--
+-- Unless required by applicable law or agreed to in writing, software
+-- distributed under the License is distributed on an "AS IS" BASIS,
+-- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+-- See the License for the specific language governing permissions and
+-- limitations under the License.
+
+-- Declares a variable to store the maximum date for analysis
+DECLARE max_date DATE;
+-- Declares a variable to store the minimum date for analysis
+DECLARE min_date DATE;
+-- Sets the max_date variable to the latest event_date minus a specified number of days ({{interval_max_date}}) from the 'event' table
+SET max_date = (SELECT DATE_SUB(MAX(event_date), INTERVAL {{interval_max_date}} DAY) FROM `{{mds_project_id}}.{{mds_dataset}}.event`); 
+-- Sets the min_date variable to the earliest event_date plus a specified number of days ({{interval_min_date}}) from the 'event' table
+SET min_date = (SELECT DATE_ADD(MIN(event_date), INTERVAL {{interval_min_date}} DAY) FROM `{{mds_project_id}}.{{mds_dataset}}.event`); 
+
+-- If min_date > maximum event_date OR max_date < minimum event_date, then set min_date for the max event_date and set max_date for the min event_date
+IF min_date >= (SELECT MAX(event_date) FROM `{{mds_project_id}}.{{mds_dataset}}.event`) OR max_date <= (SELECT MIN(event_date) FROM `{{mds_project_id}}.{{mds_dataset}}.event`) OR min_date >= max_date THEN
+  SET min_date = (SELECT MIN(event_date) FROM `{{mds_project_id}}.{{mds_dataset}}.event`);
+  SET max_date = (SELECT MAX(event_date) FROM `{{mds_project_id}}.{{mds_dataset}}.event`);
+END IF;
+
+-- This code block acts as a safeguard to ensure that the min_date and max_date used for further analysis are always within the bounds of the actual data available in the table. 
+-- It prevents situations where calculations might mistakenly consider dates beyond the real data range, which could lead to errors or misleading results.
+IF max_date > (SELECT MAX(event_date) FROM `{{mds_project_id}}.{{mds_dataset}}.event`) OR min_date < (SELECT MIN(event_date) FROM `{{mds_project_id}}.{{mds_dataset}}.event`) THEN
+  SET min_date = (SELECT MIN(event_date) FROM `{{mds_project_id}}.{{mds_dataset}}.event`);
+  SET max_date = (SELECT MAX(event_date) FROM `{{mds_project_id}}.{{mds_dataset}}.event`);
+END IF;
+
+-- Creates a temporary table called dates_interval to store distinct event dates and their corresponding end dates
+CREATE OR REPLACE TEMP TABLE dates_interval as (
+  SELECT DISTINCT 
+    -- Selects the distinct event_date and assigns it to the column input_date
+    event_date as input_date,
+    -- Calculates the end date by adding a specified number of days ({{interval_end_date}}) to the input_date
+    DATE_ADD(event_date, INTERVAL {{interval_end_date}} DAY) as end_date
+  FROM `{{mds_project_id}}.{{mds_dataset}}.event`
+  -- Filters the events to include only those within the defined date range (between min_date and max_date)
+  WHERE event_date BETWEEN min_date AND max_date
+    ORDER BY input_date DESC
+);
+
+-- All users in the platform
+-- Creates a temporary table called all_users_possible_logins to store user login data
+CREATE OR REPLACE TEMP TABLE all_users_possible_logins as (
+    SELECT DISTINCT
+      -- Selects the user_pseudo_id from the 'event' table and assigns it to the column user_pseudo_id
+      Users.user_pseudo_id,
+      -- Selects the event_date from the date array generated using GENERATE_DATE_ARRAY and assigns it to the column feature_date
+      DI.event_date as feature_date,
+      -- Creates a series of columns (login_day_1) and initializes them with NULL values
+      -- These columns will be populated later with login data for specific days
+      NULL as login_day_1
+    FROM `{{mds_project_id}}.{{mds_dataset}}.event` Users
+    -- Performs a cross join with a subquery that generates a date array using GENERATE_DATE_ARRAY
+    -- The date array includes dates from min_date to max_date with a 1-day interval
+    CROSS JOIN (SELECT event_date FROM UNNEST(GENERATE_DATE_ARRAY(min_date, max_date, INTERVAL 1 DAY)) as event_date) as DI
+    -- Filters the data to include events where event_name is 'login'
+    WHERE LOWER(Users.event_name) IN ('login')
+      AND Users.ga_session_id IS NOT NULL
+  );
+
+-- Creates a temporary table called future_logins_per_user to store user login data in the future
+-- Future User metrics: 1-7-day future logins per user, 1-day future logins per user
+CREATE OR REPLACE TEMP TABLE future_logins_per_user AS (
+  SELECT
+    -- Selects user_pseudo_id from the event table and assigns it to column user_pseudo_id
+    user_pseudo_id,
+    -- Selects input_date from the dates_interval table and assigns it to column feature_date
+    input_date as feature_date,
+    -- This calculation is performed over a window partitioned by user_pseudo_id and input_date
+    -- Repeats the above logic for different day offsets (1) to calculate future login counts for different days
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(event_date, input_date, DAY) = 1 WHEN TRUE THEN ecommerce.transaction_id END)) OVER(PARTITION BY user_pseudo_id, input_date) AS login_day_1
+  FROM `{{mds_project_id}}.{{mds_dataset}}.event` as E
+  INNER JOIN `{{mds_project_id}}.{{mds_dataset}}.device` as D
+  ON E.device_type_id = D.device_type_id
+  CROSS JOIN dates_interval as DI
+     -- Filters events to be within the date range defined by input_date and end_date from dates_interval
+    WHERE E.event_date BETWEEN DI.input_date AND DI.end_date
+    AND LOWER(E.event_name) IN ('login')
+    AND E.ga_session_id IS NOT NULL
+    AND D.device_os IS NOT NULL
+    -- Groups the result by user_pseudo_id and feature_date
+    GROUP BY user_pseudo_id, feature_date
+);
+
+-- Inserts data into the target table
+INSERT INTO `{{project_id}}.{{dataset}}.{{insert_table}}` (
+  processed_timestamp,
+  feature_date,
+  user_pseudo_id,
+  login_day_1
+)
+SELECT DISTINCT
+  -- Selects the current timestamp and assigns it to the column processed_timestamp
+  CURRENT_TIMESTAMP() AS processed_timestamp,
+  -- Selects the feature_date from the all_users_possible_logins table and assigns it to the column feature_date
+  A.feature_date,
+  -- Selects the user_pseudo_id from the all_users_possible_logins table and assigns it to the column user_pseudo_id
+  A.user_pseudo_id,
+  -- Uses the LEAST function to get the minimum value between the coalesced value of login_day_1 and 1
+  -- COALESCE is used to handle null values, replacing them with 0
+  -- This pattern is repeated for login_day_1 to populate the respective columns
+  LEAST(COALESCE(B.login_day_1, 0), 1) AS login_day_1
+FROM all_users_possible_logins AS A
+-- Performs a left join with the future_logins_per_user table (aliased as B) using user_pseudo_id and feature_date
+LEFT JOIN future_logins_per_user AS B
+ON B.user_pseudo_id = A.user_pseudo_id AND B.feature_date = A.feature_date
+;
\ No newline at end of file
diff --git a/sql/query/invoke_backfill_user_rolling_window_lead_metrics.sqlx b/sql/query/invoke_backfill_user_rolling_window_lead_metrics.sqlx
new file mode 100644
index 00000000..d98fab78
--- /dev/null
+++ b/sql/query/invoke_backfill_user_rolling_window_lead_metrics.sqlx
@@ -0,0 +1,431 @@
+-- Copyright 2023 Google LLC
+--
+-- Licensed under the Apache License, Version 2.0 (the "License");
+-- you may not use this file except in compliance with the License.
+-- You may obtain a copy of the License at
+--
+--     http://www.apache.org/licenses/LICENSE-2.0
+--
+-- Unless required by applicable law or agreed to in writing, software
+-- distributed under the License is distributed on an "AS IS" BASIS,
+-- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+-- See the License for the specific language governing permissions and
+-- limitations under the License.
+
+-- This SQL code defines a series of temporary tables to calculate and store user engagement metrics based on
+-- rolling window aggregations. These tables are then used to populate a target table with daily user engagement features.
+
+DECLARE max_date DATE;
+DECLARE min_date DATE;
+-- Sets max_date to the latest event_date from the event table, minus an offset specified by the interval_max_date
+SET max_date = (SELECT DATE_SUB(MAX(event_date), INTERVAL {{interval_max_date}} DAY) FROM `{{mds_project_id}}.{{mds_dataset}}.event`); 
+-- Sets min_date to the earliest event_date from the event table, plus an offset specified by the interval_min_date
+SET min_date = (SELECT DATE_ADD(MIN(event_date), INTERVAL {{interval_min_date}} DAY) FROM `{{mds_project_id}}.{{mds_dataset}}.event`); 
+
+-- If min_date > maximum event_date OR max_date < minimum event_date, then set min_date for the max event_date and set max_date for the min event_date
+IF min_date >= (SELECT MAX(event_date) FROM `{{mds_project_id}}.{{mds_dataset}}.event`) OR max_date <= (SELECT MIN(event_date) FROM `{{mds_project_id}}.{{mds_dataset}}.event`) OR min_date >= max_date THEN
+  SET min_date = (SELECT MIN(event_date) FROM `{{mds_project_id}}.{{mds_dataset}}.event`);
+  SET max_date = (SELECT MAX(event_date) FROM `{{mds_project_id}}.{{mds_dataset}}.event`);
+END IF;
+
+-- This code block acts as a safeguard to ensure that the min_date and max_date used for further analysis are always within the bounds of the actual data available in the table. 
+-- It prevents situations where calculations might mistakenly consider dates beyond the real data range, which could lead to errors or misleading results.
+IF max_date > (SELECT MAX(event_date) FROM `{{mds_project_id}}.{{mds_dataset}}.event`) OR min_date < (SELECT MIN(event_date) FROM `{{mds_project_id}}.{{mds_dataset}}.event`) THEN
+  SET min_date = (SELECT MIN(event_date) FROM `{{mds_project_id}}.{{mds_dataset}}.event`);
+  SET max_date = (SELECT MAX(event_date) FROM `{{mds_project_id}}.{{mds_dataset}}.event`);
+END IF;
+
+-- This section determines the date range for analysis and creates a temporary table dates_interval with distinct date intervals.
+CREATE OR REPLACE TEMP TABLE dates_interval as (
+  SELECT DISTINCT 
+    -- Select each distinct event_date as 'input_date', representing the current date in the analysis
+    event_date as input_date,
+     -- Calculate the 'end_date' by subtracting a specified interval from the 'input_date'
+    DATE_SUB(event_date, INTERVAL {{interval_end_date}} DAY) as end_date
+  FROM `{{mds_project_id}}.{{mds_dataset}}.event`
+  WHERE event_date BETWEEN min_date AND max_date
+    ORDER BY input_date DESC
+);
+
+-- Run these windows aggregations every day. For each date in training and inference date ranges.
+-- All users metrics: 1–5-day scroll_50 users
+CREATE OR REPLACE TEMP TABLE rolling_scroll_50_past_days AS (
+  SELECT
+    user_pseudo_id,
+    input_date as feature_date,
+    -- Number of times the user has scroll_50 in the past 1st day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY) = 1 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS scroll_50_past_1_day,
+    -- Number of times the user has scroll_50 in the past 2nd day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY) = 2 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS scroll_50_past_2_day,
+    -- Number of times the user has scroll_50 in the past 3rd day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY) = 3 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS scroll_50_past_3_day,
+    -- Number of times the user has scroll_50 in the past 4th day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY) = 4 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS scroll_50_past_4_day,
+    -- Number of times the user has scroll_50 in the past 5th day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY) = 5 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS scroll_50_past_5_day
+  FROM `{{mds_project_id}}.{{mds_dataset}}.event` as E
+  CROSS JOIN dates_interval as DI
+  -- Filter events to be within the defined date range
+  WHERE E.event_date BETWEEN DI.end_date AND DI.input_date
+  -- Filter for scroll_50 events
+  AND event_name='scroll_50'
+  -- Ensure valid session ID
+  AND ga_session_id IS NOT NULL
+  -- Group the results by user pseudo ID and feature date
+  GROUP BY user_pseudo_id, feature_date
+);
+
+-- Past User metrics: 1-day scroll_90 per user, 2-5-day scroll_90 per user
+CREATE OR REPLACE TEMP TABLE rolling_scroll_90_past_days AS (
+  SELECT
+    user_pseudo_id,
+    input_date as feature_date,
+    -- Number of scroll_90 made by the user in the past 1st day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY) = 1 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS scroll_90_past_1_day,
+    -- Number of scroll_90 made by the user in the past 2nd day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY) = 2 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS scroll_90_past_2_day,
+    -- Number of scroll_90 made by the user in the past 3th day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY) = 3 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS scroll_90_past_3_day,
+    -- Number of scroll_90 made by the user in the past 4th day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY) = 4 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS scroll_90_past_4_day,
+    -- Number of scroll_90 made by the user in the past 5th day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY) = 5 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS scroll_90_past_5_day
+  FROM `{{mds_project_id}}.{{mds_dataset}}.event` as E
+  CROSS JOIN dates_interval as DI
+  -- Filter events to be within the defined date range
+  WHERE E.event_date BETWEEN DI.end_date AND DI.input_date
+  -- Consider only events with event name 'scroll_90'
+  AND event_name='scroll_90'
+  AND ga_session_id IS NOT NULL
+  -- Group the results by user pseudo ID and feature date
+  GROUP BY user_pseudo_id, feature_date
+);
+
+-- Past User metrics: 1-day view_search_results per user, 2-5-day visits per user
+CREATE OR REPLACE TEMP TABLE rolling_view_search_results_past_days AS (
+  SELECT
+    user_pseudo_id,
+    input_date as feature_date,
+    -- Calculate the number of times the user view_search_results in the past 1st day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY)  = 1 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS view_search_results_past_1_day,
+    -- Calculate the number of times the user view_search_results in the past 2nd day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY)  = 2 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS view_search_results_past_2_day,
+    -- Calculate the number of times the user view_search_results in the past 3rd day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY)  = 3 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS view_search_results_past_3_day,
+    -- Calculate the number of times the user view_search_results in the past 4th day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY)  = 4 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS view_search_results_past_4_day,
+    -- Calculate the number of times the user view_search_results in the past 5th day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY)  = 5 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS view_search_results_past_5_day
+  FROM `{{mds_project_id}}.{{mds_dataset}}.event` as E
+  CROSS JOIN dates_interval as DI
+  -- Filter events to be within the defined date range
+  WHERE E.event_date BETWEEN DI.end_date AND DI.input_date
+  -- Filter for view_search_results events
+  AND event_name='view_search_results'
+  -- Ensure valid session ID
+  AND ga_session_id IS NOT NULL
+  -- Group the results by user pseudo ID and feature date
+  GROUP BY user_pseudo_id, feature_date
+);
+
+-- Past User metrics: 1-day view_item per user, 2-5-day view_item per user
+CREATE OR REPLACE TEMP TABLE rolling_file_download_past_days AS (
+  SELECT
+    user_pseudo_id,
+    input_date as feature_date,
+    -- Number of times the user has file_download in the past 1st day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY) = 1 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS file_download_past_1_day,
+    -- Number of times the user has file_download in the past 2nd day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY) = 2 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS file_download_past_2_day,
+    -- Number of times the user has file_download in the past 3rd day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY) = 3 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS file_download_past_3_day,
+    -- Number of times the user has file_download in the past 4th day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY) = 4 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS file_download_past_4_day,
+    -- Number of times the user has file_download in the past 5th day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY) = 5 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS file_download_past_5_day
+  FROM `{{mds_project_id}}.{{mds_dataset}}.event` as E
+  CROSS JOIN dates_interval as DI
+  -- Filter events to be within the defined date range
+  WHERE E.event_date BETWEEN DI.end_date AND DI.input_date
+  -- Consider only events with event name 'file_download'
+  AND event_name='file_download'
+  AND ga_session_id IS NOT NULL
+  -- Group the results by user pseudo ID and feature date
+  GROUP BY user_pseudo_id, feature_date
+);
+
+## Past User metrics: 1-day recipe_add_to_list per user, 2-5-day recipe_add_to_list per user
+CREATE OR REPLACE TEMP TABLE rolling_recipe_add_to_list_past_days AS (
+  SELECT
+    user_pseudo_id,
+    input_date as feature_date,
+    -- Number of times the user has added items to cart in the past 1st day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY) = 1 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS recipe_add_to_list_past_1_day,
+    -- Number of times the user has added items to cart in the past 2nd day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY) = 2 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS recipe_add_to_list_past_2_day,
+    -- Number of times the user has added items to cart in the past 3rd day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY) = 3 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS recipe_add_to_list_past_3_day,
+    -- Number of times the user has added items to cart in the past 4th day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY) = 4 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS recipe_add_to_list_past_4_day,
+    -- Number of times the user has added items to cart in the past 5th day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY) = 5 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS recipe_add_to_list_past_5_day
+  FROM `{{mds_project_id}}.{{mds_dataset}}.event` as E
+  CROSS JOIN dates_interval as DI
+  -- Filter events to be within the defined date range
+  WHERE E.event_date BETWEEN DI.end_date AND DI.input_date
+  -- Consider only events with event name 'recipe_add_to_list'
+  AND event_name='recipe_add_to_list'
+  AND ga_session_id IS NOT NULL
+  -- Group the results by user pseudo ID and feature date
+  GROUP BY user_pseudo_id, feature_date
+);
+
+-- Past User metrics: 1-day recipe_print per user, 2-5-day recipe_print per user
+CREATE OR REPLACE TEMP TABLE rolling_recipe_print_past_days AS (
+  SELECT
+    user_pseudo_id,
+    input_date as feature_date,
+    -- Number of times the user has recipe_print in the past 1st day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY) = 1 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS recipe_print_past_1_day,
+    -- Number of times the user has recipe_print in the past 2nd day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY) = 2 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS recipe_print_past_2_day,
+    -- Number of times the user has recipe_print in the past 3rd day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY) = 3 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS recipe_print_past_3_day,
+    -- Number of times the user has recipe_print in the past 4th day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY) = 4 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS recipe_print_past_4_day,
+    -- Number of times the user has recipe_print in the past 5th day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY) = 5 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS recipe_print_past_5_day
+  FROM `{{mds_project_id}}.{{mds_dataset}}.event` as E
+  CROSS JOIN dates_interval as DI
+  -- Filter events to be within the defined date range
+  WHERE E.event_date BETWEEN DI.end_date AND DI.input_date
+  -- Consider only events with event name 'recipe_print'
+  AND event_name='recipe_print'
+  AND ga_session_id IS NOT NULL
+  -- Group the results by user pseudo ID and feature date
+  GROUP BY user_pseudo_id, feature_date
+);
+
+-- Past User metrics: 1-day sign_up per user, 2-5-day sign_up per user
+CREATE OR REPLACE TEMP TABLE rolling_sign_up_past_days AS (
+  SELECT
+    user_pseudo_id,
+    input_date as feature_date,
+    -- Number of times the user has sign_up in the past 1st day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY) = 1 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS sign_up_past_1_day,
+    -- Number of times the user has sign_up in the past 2nd day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY) = 2 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS sign_up_past_2_day,
+    -- Number of times the user has sign_up in the past 3rd day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY) = 3 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS sign_up_past_3_day,
+    -- Number of times the user has sign_up in the past 4th day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY) = 4 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS sign_up_past_4_day,
+    -- Number of times the user has sign_up in the past 5th day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY) = 5 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS sign_up_past_5_day
+  FROM `{{mds_project_id}}.{{mds_dataset}}.event` as E
+  CROSS JOIN dates_interval as DI
+  -- Filter events to be within the defined date range
+  WHERE E.event_date BETWEEN DI.end_date AND DI.input_date
+  -- Consider only events with event name 'sign_up'
+  AND event_name='sign_up'
+  AND ga_session_id IS NOT NULL
+  -- Group the results by user pseudo ID and feature date
+  GROUP BY user_pseudo_id, feature_date
+);
+
+-- Past User metrics: 1-day recipe_favorite per user, 2-5-day recipe_favorite per user
+CREATE OR REPLACE TEMP TABLE rolling_recipe_favorite_past_days AS (
+  SELECT
+    user_pseudo_id,
+    input_date as feature_date,
+    -- Number of times the user has recipe_favorite in the past 1st day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY) = 1 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS recipe_favorite_past_1_day,
+    -- Number of times the user has recipe_favorite in the past 2nd day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY) = 2 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS recipe_favorite_past_2_day,
+    -- Number of times the user has recipe_favorite in the past 3rd day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY) = 3 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS recipe_favorite_past_3_day,
+    -- Number of times the user has recipe_favorite in the past 4th day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY) = 4 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS recipe_favorite_past_4_day,
+    -- Number of times the user has recipe_favorite in the past 5th day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY) = 5 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS recipe_favorite_past_5_day
+  FROM `{{mds_project_id}}.{{mds_dataset}}.event` as E
+  CROSS JOIN dates_interval as DI
+  -- Filter events to be within the defined date range
+  WHERE E.event_date BETWEEN DI.end_date AND DI.input_date
+  -- Consider only events with event name 'recipe_favorite'
+  AND event_name='recipe_favorite'
+  AND ga_session_id IS NOT NULL
+  -- Group the results by user pseudo ID and feature date
+  GROUP BY user_pseudo_id, feature_date
+);
+
+-- Past User metrics: 1-day recipe_add_to_menu per user, 2-5-day recipe_add_to_menu per user
+CREATE OR REPLACE TEMP TABLE rolling_recipe_add_to_menu_past_days AS (
+  SELECT
+    user_pseudo_id,
+    input_date as feature_date,
+    -- Number of times the user has recipe_add_to_menu in the past 1st day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY) = 1 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS recipe_add_to_menu_past_1_day,
+    -- Number of times the user has recipe_add_to_menu in the past 2nd day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY) = 2 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS recipe_add_to_menu_past_2_day,
+    -- Number of times the user has recipe_add_to_menu in the past 3rd day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY) = 3 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS recipe_add_to_menu_past_3_day,
+    -- Number of times the user has recipe_add_to_menu in the past 4th day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY) = 4 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS recipe_add_to_menu_past_4_day,
+    -- Number of times the user has recipe_add_to_menu in the past 5th day
+    MAX(COUNT(DISTINCT CASE DATE_DIFF(input_date, event_date, DAY) = 5 WHEN TRUE THEN event_timestamp END)) OVER(PARTITION BY user_pseudo_id, input_date) AS recipe_add_to_menu_past_5_day
+  FROM `{{mds_project_id}}.{{mds_dataset}}.event` as E
+  CROSS JOIN dates_interval as DI
+  -- Filter events to be within the defined date range
+  WHERE E.event_date BETWEEN DI.end_date AND DI.input_date
+  -- Consider only events with event name 'recipe_add_to_menu'
+  AND event_name='recipe_add_to_menu'
+  AND ga_session_id IS NOT NULL
+  -- Group the results by user pseudo ID and feature date
+  GROUP BY user_pseudo_id, feature_date
+);
+
+-- All users in the platform
+-- This code creates a temporary table that contains a distinct list of user pseudo IDs 
+-- and their corresponding feature dates, filtering for events with valid session IDs, 
+-- device operating systems, and falling within the specified date range.
+CREATE OR REPLACE TEMP TABLE events_users as (
+  SELECT DISTINCT
+    Users.user_pseudo_id,
+    DI.input_date as feature_date
+  FROM `{{mds_project_id}}.{{mds_dataset}}.event` Users
+  INNER JOIN `{{mds_project_id}}.{{mds_dataset}}.device` as D
+  ON Users.device_type_id = D.device_type_id 
+  CROSS JOIN dates_interval as DI
+  WHERE Users.ga_session_id IS NOT NULL
+  AND Users.event_date BETWEEN DI.end_date AND DI.input_date
+  AND D.device_os IS NOT NULL
+);
+
+-- This code block inserts data into a table, combining information from the events_users 
+-- table and several temporary tables containing rolling window features. The resulting data 
+-- represents user-level features for each user and date, capturing their past activity within 
+-- different time windows.
+INSERT INTO `{{project_id}}.{{dataset}}.{{insert_table}}` (
+  processed_timestamp,
+  feature_date,
+  user_pseudo_id,
+  scroll_50_past_1_day,
+  scroll_50_past_2_day,
+  scroll_50_past_3_day,
+  scroll_50_past_4_day,
+  scroll_50_past_5_day,
+  scroll_90_past_1_day,
+  scroll_90_past_2_day,
+  scroll_90_past_3_day,
+  scroll_90_past_4_day,
+  scroll_90_past_5_day,
+  view_search_results_past_1_day,
+  view_search_results_past_2_day,
+  view_search_results_past_3_day,
+  view_search_results_past_4_day,
+  view_search_results_past_5_day,
+  file_download_past_1_day,
+  file_download_past_2_day,
+  file_download_past_3_day,
+  file_download_past_4_day,
+  file_download_past_5_day,
+  recipe_add_to_list_past_1_day,
+  recipe_add_to_list_past_2_day,
+  recipe_add_to_list_past_3_day,
+  recipe_add_to_list_past_4_day,
+  recipe_add_to_list_past_5_day,
+  recipe_print_past_1_day,
+  recipe_print_past_2_day,
+  recipe_print_past_3_day,
+  recipe_print_past_4_day,
+  recipe_print_past_5_day,
+  sign_up_past_1_day,
+  sign_up_past_2_day,
+  sign_up_past_3_day,
+  sign_up_past_4_day,
+  sign_up_past_5_day,
+  recipe_favorite_past_1_day,
+  recipe_favorite_past_2_day,
+  recipe_favorite_past_3_day,
+  recipe_favorite_past_4_day,
+  recipe_favorite_past_5_day,
+  recipe_add_to_menu_past_1_day,
+  recipe_add_to_menu_past_2_day,
+  recipe_add_to_menu_past_3_day,
+  recipe_add_to_menu_past_4_day,
+  recipe_add_to_menu_past_5_day
+)
+  SELECT DISTINCT
+    -- This selects the current timestamp and assigns it to the column processed_timestamp.
+    CURRENT_TIMESTAMP() AS processed_timestamp,
+    EUD.feature_date,
+    EUD.user_pseudo_id,
+    COALESCE(scroll_50_past_1_day,0) AS scroll_50_past_1_day,
+    COALESCE(scroll_50_past_2_day,0) AS scroll_50_past_2_day,
+    COALESCE(scroll_50_past_3_day,0) AS scroll_50_past_3_day,
+    COALESCE(scroll_50_past_4_day,0) AS scroll_50_past_4_day,
+    COALESCE(scroll_50_past_5_day,0) AS scroll_50_past_5_day,
+    COALESCE(scroll_90_past_1_day,0) AS scroll_90_past_1_day,
+    COALESCE(scroll_90_past_2_day,0) AS scroll_90_past_2_day,
+    COALESCE(scroll_90_past_3_day,0) AS scroll_90_past_3_day,
+    COALESCE(scroll_90_past_4_day,0) AS scroll_90_past_4_day,
+    COALESCE(scroll_90_past_5_day,0) AS scroll_90_past_5_day,
+    COALESCE(view_search_results_past_1_day,0) AS view_search_results_past_1_day,
+    COALESCE(view_search_results_past_2_day,0) AS view_search_results_past_2_day,
+    COALESCE(view_search_results_past_3_day,0) AS view_search_results_past_3_day,
+    COALESCE(view_search_results_past_4_day,0) AS view_search_results_past_4_day,
+    COALESCE(view_search_results_past_5_day,0) AS view_search_results_past_5_day,
+    COALESCE(file_download_past_1_day,0) AS file_download_past_1_day,
+    COALESCE(file_download_past_2_day,0) AS file_download_past_2_day,
+    COALESCE(file_download_past_3_day,0) AS file_download_past_3_day,
+    COALESCE(file_download_past_4_day,0) AS file_download_past_4_day,
+    COALESCE(file_download_past_5_day,0) AS file_download_past_5_day,
+    COALESCE(recipe_add_to_list_past_1_day,0) AS recipe_add_to_list_past_1_day,
+    COALESCE(recipe_add_to_list_past_2_day,0) AS recipe_add_to_list_past_2_day,
+    COALESCE(recipe_add_to_list_past_3_day,0) AS recipe_add_to_list_past_3_day,
+    COALESCE(recipe_add_to_list_past_4_day,0) AS recipe_add_to_list_past_4_day,
+    COALESCE(recipe_add_to_list_past_5_day,0) AS recipe_add_to_list_past_5_day,
+    COALESCE(recipe_print_past_1_day,0) AS recipe_print_past_1_day,
+    COALESCE(recipe_print_past_2_day,0) AS recipe_print_past_2_day,
+    COALESCE(recipe_print_past_3_day,0) AS recipe_print_past_3_day,
+    COALESCE(recipe_print_past_4_day,0) AS recipe_print_past_4_day,
+    COALESCE(recipe_print_past_5_day,0) AS recipe_print_past_5_day,
+    COALESCE(sign_up_past_1_day,0) AS sign_up_past_1_day,
+    COALESCE(sign_up_past_2_day,0) AS sign_up_past_2_day,
+    COALESCE(sign_up_past_3_day,0) AS sign_up_past_3_day,
+    COALESCE(sign_up_past_4_day,0) AS sign_up_past_4_day,
+    COALESCE(sign_up_past_5_day,0) AS sign_up_past_5_day,
+    COALESCE(recipe_favorite_past_1_day,0) AS recipe_favorite_past_1_day,
+    COALESCE(recipe_favorite_past_2_day,0) AS recipe_favorite_past_2_day,
+    COALESCE(recipe_favorite_past_3_day,0) AS recipe_favorite_past_3_day,
+    COALESCE(recipe_favorite_past_4_day,0) AS recipe_favorite_past_4_day,
+    COALESCE(recipe_favorite_past_5_day,0) AS recipe_favorite_past_5_day,
+    COALESCE(recipe_add_to_menu_past_1_day,0) AS recipe_add_to_menu_past_1_day,
+    COALESCE(recipe_add_to_menu_past_2_day,0) AS recipe_add_to_menu_past_2_day,
+    COALESCE(recipe_add_to_menu_past_3_day,0) AS recipe_add_to_menu_past_3_day,
+    COALESCE(recipe_add_to_menu_past_4_day,0) AS recipe_add_to_menu_past_4_day,
+    COALESCE(recipe_add_to_menu_past_5_day,0) AS recipe_add_to_menu_past_5_day
+    FROM events_users AS EUD
+    -- This performs a full outer join, which combines all rows from both tables, 
+    -- including those that don't have matching values.
+    FULL OUTER JOIN rolling_scroll_50_past_days AS A
+    ON EUD.user_pseudo_id = A.user_pseudo_id
+    FULL OUTER JOIN rolling_scroll_90_past_days AS B
+    ON EUD.user_pseudo_id = B.user_pseudo_id
+    FULL OUTER JOIN rolling_view_search_results_past_days AS C
+    ON EUD.user_pseudo_id = C.user_pseudo_id
+    FULL OUTER JOIN rolling_file_download_past_days AS D
+    ON EUD.user_pseudo_id = D.user_pseudo_id
+    FULL OUTER JOIN rolling_recipe_add_to_list_past_days AS E
+    ON EUD.user_pseudo_id = E.user_pseudo_id
+    FULL OUTER JOIN rolling_recipe_print_past_days AS F
+    ON EUD.user_pseudo_id = F.user_pseudo_id
+    FULL OUTER JOIN rolling_sign_up_past_days AS G
+    ON EUD.user_pseudo_id = G.user_pseudo_id
+    FULL OUTER JOIN rolling_recipe_favorite_past_days AS H
+    ON EUD.user_pseudo_id = H.user_pseudo_id
+    FULL OUTER JOIN rolling_recipe_add_to_menu_past_days AS I
+    ON EUD.user_pseudo_id = I.user_pseudo_id
+    -- This filters the results to include only rows where the user_pseudo_id is not null.
+    WHERE EUD.user_pseudo_id IS NOT NULL
+    ; 
\ No newline at end of file
diff --git a/sql/query/invoke_lead_score_propensity_inference_preparation.sqlx b/sql/query/invoke_lead_score_propensity_inference_preparation.sqlx
new file mode 100644
index 00000000..54e937d7
--- /dev/null
+++ b/sql/query/invoke_lead_score_propensity_inference_preparation.sqlx
@@ -0,0 +1,23 @@
+-- Copyright 2023 Google LLC
+--
+-- Licensed under the Apache License, Version 2.0 (the "License");
+-- you may not use this file except in compliance with the License.
+-- You may obtain a copy of the License at
+--
+--     http://www.apache.org/licenses/LICENSE-2.0
+--
+-- Unless required by applicable law or agreed to in writing, software
+-- distributed under the License is distributed on an "AS IS" BASIS,
+-- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+-- See the License for the specific language governing permissions and
+-- limitations under the License.
+
+-- This script determines the current date and then passes it as an argument to a 
+-- stored procedure in your BigQuery project. This pattern is commonly used when 
+-- you want a stored procedure to perform operations or calculations that are 
+-- relevant to the current date, such as data processing, analysis, or reporting tasks.
+
+DECLARE inference_date DATE DEFAULT NULL;
+SET inference_date = CURRENT_DATE();
+
+CALL `{{project_id}}.{{dataset}}.{{stored_procedure}}`(inference_date);
diff --git a/sql/query/invoke_lead_score_propensity_label.sqlx b/sql/query/invoke_lead_score_propensity_label.sqlx
new file mode 100644
index 00000000..f4288278
--- /dev/null
+++ b/sql/query/invoke_lead_score_propensity_label.sqlx
@@ -0,0 +1,39 @@
+-- Copyright 2023 Google LLC
+--
+-- Licensed under the Apache License, Version 2.0 (the "License");
+-- you may not use this file except in compliance with the License.
+-- You may obtain a copy of the License at
+--
+--     http://www.apache.org/licenses/LICENSE-2.0
+--
+-- Unless required by applicable law or agreed to in writing, software
+-- distributed under the License is distributed on an "AS IS" BASIS,
+-- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+-- See the License for the specific language governing permissions and
+-- limitations under the License.
+
+-- This script sets up a date range, calls a stored procedure with this range and a variable to 
+-- store a result, and then returns the result of the stored procedure. This pattern is common 
+-- for orchestrating data processing tasks within BigQuery using stored procedures.
+
+DECLARE input_date DATE;
+DECLARE end_date DATE;
+DECLARE users_added INT64 DEFAULT NULL;
+
+SET end_date= CURRENT_DATE();
+SET input_date= (SELECT DATE_SUB(end_date, INTERVAL {{interval_input_date}} DAY));
+
+-- This code block ensures that the end_date used in subsequent operations is not later than one day after the latest available data in 
+-- the specified events table. This prevents potential attempts to process data for a date range that extends beyond the actual data availability.
+IF (SELECT DATE_SUB(end_date, INTERVAL 1 DAY)) > (SELECT MAX(event_date) FROM `{{mds_project_id}}.{{mds_dataset}}.event`) THEN
+  SET end_date = (SELECT DATE_ADD(MAX(event_date), INTERVAL 1 DAY) FROM `{{mds_project_id}}.{{mds_dataset}}.event`);
+END IF;
+
+-- This code block ensures that the input_date used in subsequent operations is not before the earliest available data in the 
+-- specified events table. This prevents potential errors or unexpected behavior that might occur when trying to process data 
+-- for a date range that precedes the actual data availability.
+IF input_date < (SELECT MIN(event_date) FROM `{{mds_project_id}}.{{mds_dataset}}.event`) THEN
+  SET input_date = (SELECT DATE_ADD(MIN(event_date), INTERVAL 1 DAY) FROM `{{mds_project_id}}.{{mds_dataset}}.event`);
+END IF;
+
+CALL `{{project_id}}.{{dataset}}.{{stored_procedure}}`(input_date, end_date, users_added);
\ No newline at end of file
diff --git a/sql/query/invoke_lead_score_propensity_training_preparation.sqlx b/sql/query/invoke_lead_score_propensity_training_preparation.sqlx
new file mode 100644
index 00000000..cf93a2d8
--- /dev/null
+++ b/sql/query/invoke_lead_score_propensity_training_preparation.sqlx
@@ -0,0 +1,73 @@
+-- Copyright 2023 Google LLC
+--
+-- Licensed under the Apache License, Version 2.0 (the "License");
+-- you may not use this file except in compliance with the License.
+-- You may obtain a copy of the License at
+--
+--     http://www.apache.org/licenses/LICENSE-2.0
+--
+-- Unless required by applicable law or agreed to in writing, software
+-- distributed under the License is distributed on an "AS IS" BASIS,
+-- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+-- See the License for the specific language governing permissions and
+-- limitations under the License.
+
+-- This script intelligently determines the optimal date range for training a purchase 
+-- propensity model by considering user-defined parameters and the availability of purchase 
+-- events within the dataset. It ensures that the training data includes purchase events if 
+-- they exist within the specified bounds.
+
+-- Intended start and end dates for training data
+-- Initializing Training Dates
+DECLARE train_start_date DATE DEFAULT NULL;
+DECLARE train_end_date DATE DEFAULT NULL;
+
+-- Control data splitting for training and validation (likely used in a subsequent process).
+DECLARE train_split_end_number INT64 DEFAULT NULL;
+DECLARE validation_split_end_number INT64 DEFAULT NULL;
+
+-- Will store the count of distinct users who made a login within a given period.
+DECLARE logged_users INT64 DEFAULT NULL;
+
+-- Used to store the maximum and minimum event dates from the source data.
+DECLARE max_date DATE;
+DECLARE min_date DATE;
+
+-- Determining Maximum and Minimum Dates
+SET max_date = (SELECT DATE_SUB(MAX(event_date), INTERVAL {{interval_max_date}} DAY) FROM `{{mds_project_id}}.{{mds_dataset}}.event`); 
+SET min_date = (SELECT DATE_ADD(MIN(event_date), INTERVAL {{interval_min_date}} DAY) FROM `{{mds_project_id}}.{{mds_dataset}}.event`); 
+
+-- If min_date > maximum event_date OR max_date < minimum event_date, then set min_date for the min event_date and set max_date for the max event_date
+IF min_date >= (SELECT MAX(event_date) FROM `{{mds_project_id}}.{{mds_dataset}}.event`) OR max_date <= (SELECT MIN(event_date) FROM `{{mds_project_id}}.{{mds_dataset}}.event`)  OR min_date >= max_date THEN
+  SET min_date = (SELECT MIN(event_date) FROM `{{mds_project_id}}.{{mds_dataset}}.event`);
+  SET max_date = (SELECT MAX(event_date) FROM `{{mds_project_id}}.{{mds_dataset}}.event`);
+END IF;
+
+-- Setting Split Numbers
+-- Sets the train_split_end_number to a user-defined value. This value likely determines the proportion of data used for training.
+SET train_split_end_number = {{train_split_end_number}};    -- If you want 60% for training use number 5. If you want 80% use number 7.
+-- Sets the validation_split_end_number to a user-defined value, controlling the proportion of data used for validation.
+SET validation_split_end_number = {{validation_split_end_number}};
+
+-- This crucial step counts distinct users who have an event named 'login' within the initially set training date range.
+-- IF there are no logged_users in the time interval selected, then set "train_start_date" and "train_end_date" as "max_date" and "min_date".
+SET logged_users = (SELECT COUNT(DISTINCT user_pseudo_id) 
+                FROM `{{mds_project_id}}.{{mds_dataset}}.event`
+                WHERE event_name = 'login' AND
+                event_date BETWEEN min_date AND max_date
+                );
+
+-- Setting Training Dates
+-- If there are logged_users in the training set, then keep the calculated dates, or else set
+-- the start and end dates to a fixed interval preventing `train_start_date` and `train_end_date` from being NULL.
+IF logged_users > 0 THEN
+    SET train_start_date =  min_date;
+    SET train_end_date = max_date;
+ELSE
+    SET train_start_date =  DATE_SUB(CURRENT_DATE(), INTERVAL 3 YEAR);
+    SET train_end_date = DATE_SUB(CURRENT_DATE(), INTERVAL 5 DAY);
+END IF;
+
+-- Finally, the script calls a stored procedure, passing the adjusted training dates and split numbers as arguments. This stored procedure 
+-- handles the actual data preparation for the lead score propensity model.
+CALL `{{project_id}}.{{dataset}}.{{stored_procedure}}`(train_start_date, train_end_date, train_split_end_number, validation_split_end_number);
diff --git a/sql/query/invoke_user_rolling_window_lead_metrics.sqlx b/sql/query/invoke_user_rolling_window_lead_metrics.sqlx
new file mode 100644
index 00000000..e469a2d7
--- /dev/null
+++ b/sql/query/invoke_user_rolling_window_lead_metrics.sqlx
@@ -0,0 +1,28 @@
+-- Copyright 2023 Google LLC
+--
+-- Licensed under the Apache License, Version 2.0 (the "License");
+-- you may not use this file except in compliance with the License.
+-- You may obtain a copy of the License at
+--
+--     http://www.apache.org/licenses/LICENSE-2.0
+--
+-- Unless required by applicable law or agreed to in writing, software
+-- distributed under the License is distributed on an "AS IS" BASIS,
+-- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+-- See the License for the specific language governing permissions and
+-- limitations under the License.
+
+-- This script sets up a date range, calls a stored procedure with this range and a variable to 
+-- store a result, and then returns the result of the stored procedure. This pattern is common 
+-- for orchestrating data processing tasks within BigQuery using stored procedures.
+
+DECLARE input_date DATE;
+DECLARE end_date DATE;
+DECLARE users_added INT64 DEFAULT NULL;
+
+SET input_date= CURRENT_DATE();
+SET end_date= (SELECT DATE_SUB(input_date, INTERVAL {{interval_end_date}} DAY));
+
+CALL `{{project_id}}.{{dataset}}.{{stored_procedure}}`(input_date, end_date, users_added);
+
+SELECT users_added;
\ No newline at end of file
diff --git a/sql/schema/table/lead_score_propensity_inference_preparation.json b/sql/schema/table/lead_score_propensity_inference_preparation.json
new file mode 100644
index 00000000..5fc9e6ec
--- /dev/null
+++ b/sql/schema/table/lead_score_propensity_inference_preparation.json
@@ -0,0 +1,337 @@
+[
+  {
+    "name": "user_pseudo_id",
+    "type": "STRING",
+    "description": "The user pseudo identifier"
+  },
+  {
+    "name": "user_id",
+    "type": "STRING",
+    "description": "The user identifier when the user is logged in"
+  },
+  {
+    "name": "feature_date",
+    "type": "DATE",
+    "description": "Date that serves as the basis for the calculation of the features"
+  },
+  {
+    "name": "user_ltv_revenue",
+    "type": "FLOAT",
+    "description": "The current customer lifetime value revenue of the user"
+  },
+  {
+    "name": "device_category",
+    "type": "STRING",
+    "description": "The device category the user last accessed"
+  },
+  {
+    "name": "device_mobile_brand_name",
+    "type": "STRING",
+    "description": "The device mobile brand name the user last accessed"
+  },
+  {
+    "name": "device_mobile_model_name",
+    "type": "STRING",
+    "description": "The device mobile model name the user last accessed"
+  },
+  {
+    "name": "device_os",
+    "type": "STRING",
+    "description": "The device operating system the user last accessed"
+  },
+  {
+    "name": "device_language",
+    "type": "STRING",
+    "description": "The device language the user last accessed"
+  },
+  {
+    "name": "device_web_browser",
+    "type": "STRING",
+    "description": "The device web browser the user last accessed"
+  },
+  {
+    "name": "geo_sub_continent",
+    "type": "STRING",
+    "description": "The geographic subcontinent the user last accessed from"
+  },
+  {
+    "name": "geo_country",
+    "type": "STRING",
+    "description": "The geographic country the user last accessed from"
+  },
+  {
+    "name": "geo_region",
+    "type": "STRING",
+    "description": "The geographic region the user last accessed from"
+  },
+  {
+    "name": "geo_city",
+    "type": "STRING",
+    "description": "The geographic city the user last accessed from"
+  },
+  {
+    "name": "geo_metro",
+    "type": "STRING",
+    "description": "The geographic metropolitan area the user last accessed from"
+  },
+  {
+    "name": "last_traffic_source_medium",
+    "type": "STRING",
+    "description": "The last traffic source medium the user has been acquired"
+  },
+  {
+    "name": "last_traffic_source_name",
+    "type": "STRING",
+    "description": "The last traffic source name the user has been acquired"
+  },
+  {
+    "name": "last_traffic_source_source",
+    "type": "STRING",
+    "description": "The last traffic source source the user has been acquired"
+  },
+  {
+    "name": "first_traffic_source_medium",
+    "type": "STRING",
+    "description": "The first traffic source medium the user has been acquired"
+  },
+  {
+    "name": "first_traffic_source_name",
+    "type": "STRING",
+    "description": "The first traffic source name the user has been acquired"
+  },
+  {
+    "name": "first_traffic_source_source",
+    "type": "STRING",
+    "description": "The first traffic source source the user has been acquired"
+  },
+  {
+    "name": "has_signed_in_with_user_id",
+    "type": "BOOLEAN",
+    "description": "A boolean indicating whether the user has signed in with an user id"
+  },
+  {
+    "name": "scroll_50_past_1_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has been active in the past 1 day"
+  },
+  {
+    "name": "scroll_50_past_2_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has been active in the past 2nd day"
+  },
+  {
+    "name": "scroll_50_past_3_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has been active in the past 3rd day"
+  },
+  {
+    "name": "scroll_50_past_4_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has been active in the past 4th day"
+  },
+  {
+    "name": "scroll_50_past_5_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has been active in the past 5th day"
+  },
+  {
+    "name": "scroll_90_past_1_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has scrolled 90p pages in the past day"
+  },
+  {
+    "name": "scroll_90_past_2_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has scrolled 90p pages in the past 2nd day"
+  },
+  {
+    "name": "scroll_90_past_3_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has scrolled 90p pages in the past 3rd day"
+  },
+  {
+    "name": "scroll_90_past_4_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has scrolled 90p pages in the past 4th day"
+  },
+  {
+    "name": "scroll_90_past_5_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has scrolled 90p pages in the past 5th day"
+  },
+  {
+    "name": "view_search_results_past_1_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has visited in the past day"
+  },
+  {
+    "name": "view_search_results_past_2_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has visited in the past 2nd day"
+  },
+  {
+    "name": "view_search_results_past_3_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has visited in the past 3rd day"
+  },
+  {
+    "name": "view_search_results_past_4_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has visited in the past 4th day"
+  },
+  {
+    "name": "view_search_results_past_5_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has visited in the past 5th day"
+  },
+  {
+    "name": "file_download_past_1_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has visited in the past day"
+  },
+  {
+      "name": "file_download_past_2_day",
+      "type": "INTEGER",
+      "description": "The number of times the user has visited in the past 2nd day"
+  },
+  {
+      "name": "file_download_past_3_day",
+      "type": "INTEGER",
+      "description": "The number of times the user has visited in the past 3rd day"
+  },
+  {
+      "name": "file_download_past_4_day",
+      "type": "INTEGER",
+      "description": "The number of times the user has visited in the past 4th day"
+  },
+  {
+      "name": "file_download_past_5_day",
+      "type": "INTEGER",
+      "description": "The number of times the user has visited in the past 5th day"
+  },
+  {
+    "name": "recipe_add_to_list_past_1_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has viewed items in the past day"
+  },
+  {
+    "name": "recipe_add_to_list_past_2_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has viewed items in the past 2nd day"
+  },
+  {
+    "name": "recipe_add_to_list_past_3_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has viewed items in the past 3rd day"
+  },
+  {
+    "name": "recipe_add_to_list_past_4_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has viewed items in the past 4th day"
+  },
+  {
+    "name": "recipe_add_to_list_past_5_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has viewed items in the past 5th day"
+  },
+  {
+    "name": "recipe_print_past_1_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has added items to cart in the past day"
+  },
+  {
+    "name": "recipe_print_past_2_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has added items to cart in the past 2nd day"
+  },
+  {
+    "name": "recipe_print_past_3_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has added items to cart in the past 3rd day"
+  },
+  {
+    "name": "recipe_print_past_4_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has added items to cart in the past 4th day"
+  },
+  {
+    "name": "recipe_print_past_5_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has added items to cart in the past 5th day"
+  },
+  {
+    "name": "sign_up_past_1_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has checked out in the past day"
+  },
+  {
+    "name": "sign_up_past_2_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has checked out in the past 2nd day"
+  },
+  {
+    "name": "sign_up_past_3_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has checked out in the past 3rd day"
+  },
+  {
+    "name": "sign_up_past_4_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has checked out in the past 4th day"
+  },
+  {
+    "name": "sign_up_past_5_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has checked out in the past 5th day"
+  },
+  {
+    "name": "recipe_favorite_past_1_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has checked out in the past day"
+  },
+  {
+    "name": "recipe_favorite_past_2_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has checked out in the past 2nd day"
+  },
+  {
+    "name": "recipe_favorite_past_3_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has checked out in the past 3rd day"
+  },
+  {
+    "name": "recipe_favorite_past_4_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has checked out in the past 4th day"
+  },
+  {
+    "name": "recipe_favorite_past_5_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has checked out in the past 5th day"
+  },
+  {
+    "name": "recipe_add_to_menu_past_1_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has checked out in the past day"
+  },
+  {
+    "name": "recipe_add_to_menu_past_2_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has checked out in the past 2nd day"
+  },
+  {
+    "name": "recipe_add_to_menu_past_3_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has checked out in the past 3rd day"
+  },
+  {
+    "name": "recipe_add_to_menu_past_4_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has checked out in the past 4th day"
+  },
+  {
+    "name": "recipe_add_to_menu_past_5_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has checked out in the past 5th day"
+  }
+]
\ No newline at end of file
diff --git a/sql/schema/table/lead_score_propensity_label.json b/sql/schema/table/lead_score_propensity_label.json
new file mode 100644
index 00000000..8b63bc6f
--- /dev/null
+++ b/sql/schema/table/lead_score_propensity_label.json
@@ -0,0 +1,22 @@
+[
+  {
+      "name": "processed_timestamp",
+      "type": "TIMESTAMP",
+      "description": "Timestamp of when the data was processed"
+  },
+  {
+      "name": "feature_date",
+      "type": "DATE",
+      "description": "The date serving as basis for the features calculation"
+  },
+  {
+      "name": "user_pseudo_id",
+      "type": "STRING",
+      "description": "The user pseudo identifier"
+  },
+  {
+      "name": "login_day_1",
+      "type": "INTEGER",
+      "description": "Predicted number of logins by the user in the next 1st day from the feature date"
+  }
+]
\ No newline at end of file
diff --git a/sql/schema/table/lead_score_propensity_training_preparation.json b/sql/schema/table/lead_score_propensity_training_preparation.json
new file mode 100644
index 00000000..f5647417
--- /dev/null
+++ b/sql/schema/table/lead_score_propensity_training_preparation.json
@@ -0,0 +1,352 @@
+[
+  {
+      "name": "processed_timestamp",
+      "type": "TIMESTAMP",
+      "description": "Timestamp of when the data was processed"
+  },
+  {
+      "name": "data_split",
+      "type": "STRING",
+      "description": "The indication of whether the row should be used for TRAINING, VALIDATION or TESTING"
+  },
+  {
+      "name": "feature_date",
+      "type": "DATE",
+      "description": "The date serving as basis for the features calculation"
+  },
+  {
+      "name": "user_pseudo_id",
+      "type": "STRING",
+      "description": "The user pseudo identifier"
+  },
+  {
+      "name": "user_id",
+      "type": "STRING",
+      "description": "The user identifier of when the user has logged in"
+  },
+  {
+      "name": "user_ltv_revenue",
+      "type": "FLOAT",
+      "description": "The current user lifetime value"
+  },
+  {
+      "name": "device_category",
+      "type": "STRING",
+      "description": "The device category of the user last used to access"
+  },
+  {
+      "name": "device_mobile_brand_name",
+      "type": "STRING",
+      "description": "The device mobile brand name last used by the user"
+  },
+  {
+      "name": "device_mobile_model_name",
+      "type": "STRING",
+      "description": "The device mobile model name last used by the user"
+  },
+  {
+      "name": "device_os",
+      "type": "STRING",
+      "description": "The device operating system last used by the user"
+  },
+  {
+      "name": "device_language",
+      "type": "STRING",
+      "description": "The device language last used by the user"
+  },
+  {
+      "name": "device_web_browser",
+      "type": "STRING",
+      "description": "The device web browser last used by the user"
+  },
+  {
+      "name": "geo_sub_continent",
+      "type": "STRING",
+      "description": "The geographic subcontinent from the user last access"
+  },
+  {
+      "name": "geo_country",
+      "type": "STRING",
+      "description": "The geographic country from the user last access"
+  },
+  {
+      "name": "geo_region",
+      "type": "STRING",
+      "description": "The geographic region from the user last access"
+  },
+  {
+      "name": "geo_city",
+      "type": "STRING",
+      "description": "The geographic city from the user last access"
+  },
+  {
+      "name": "geo_metro",
+      "type": "STRING",
+      "description": "The geographic metropolitan area from the user user last access"
+  },
+  {
+      "name": "last_traffic_source_medium",
+      "type": "STRING",
+      "description": "The last traffic source medium from where the user was acquired"
+  },
+  {
+      "name": "last_traffic_source_name",
+      "type": "STRING",
+      "description": "The last traffic source name from where the user was acquired"
+  },
+  {
+      "name": "last_traffic_source_source",
+      "type": "STRING",
+      "description": "The last traffic source soure from where the user was acquired"
+  },
+  {
+      "name": "first_traffic_source_medium",
+      "type": "STRING",
+      "description": "The first traffic source medium from where the user was acquired"
+  },
+  {
+      "name": "first_traffic_source_name",
+      "type": "STRING",
+      "description": "The first traffic source name from where the user was acquired"
+  },
+  {
+      "name": "first_traffic_source_source",
+      "type": "STRING",
+      "description": "The first traffic source source from where the user was acquired"
+  },
+  {
+      "name": "has_signed_in_with_user_id",
+      "type": "BOOLEAN",
+      "description": "A boolean indicating whether the user has signed in with the user id"
+  },
+  {
+    "name": "scroll_50_past_1_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has been active in the past 1 day"
+  },
+  {
+    "name": "scroll_50_past_2_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has been active in the past 2nd day"
+  },
+  {
+    "name": "scroll_50_past_3_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has been active in the past 3rd day"
+  },
+  {
+    "name": "scroll_50_past_4_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has been active in the past 4th day"
+  },
+  {
+    "name": "scroll_50_past_5_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has been active in the past 5th day"
+  },
+  {
+    "name": "scroll_90_past_1_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has scrolled 90p pages in the past day"
+  },
+  {
+    "name": "scroll_90_past_2_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has scrolled 90p pages in the past 2nd day"
+  },
+  {
+    "name": "scroll_90_past_3_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has scrolled 90p pages in the past 3rd day"
+  },
+  {
+    "name": "scroll_90_past_4_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has scrolled 90p pages in the past 4th day"
+  },
+  {
+    "name": "scroll_90_past_5_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has scrolled 90p pages in the past 5th day"
+  },
+  {
+    "name": "view_search_results_past_1_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has visited in the past day"
+  },
+  {
+    "name": "view_search_results_past_2_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has visited in the past 2nd day"
+  },
+  {
+    "name": "view_search_results_past_3_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has visited in the past 3rd day"
+  },
+  {
+    "name": "view_search_results_past_4_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has visited in the past 4th day"
+  },
+  {
+    "name": "view_search_results_past_5_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has visited in the past 5th day"
+  },
+  {
+    "name": "file_download_past_1_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has visited in the past day"
+  },
+  {
+      "name": "file_download_past_2_day",
+      "type": "INTEGER",
+      "description": "The number of times the user has visited in the past 2nd day"
+  },
+  {
+      "name": "file_download_past_3_day",
+      "type": "INTEGER",
+      "description": "The number of times the user has visited in the past 3rd day"
+  },
+  {
+      "name": "file_download_past_4_day",
+      "type": "INTEGER",
+      "description": "The number of times the user has visited in the past 4th day"
+  },
+  {
+      "name": "file_download_past_5_day",
+      "type": "INTEGER",
+      "description": "The number of times the user has visited in the past 5th day"
+  },
+  {
+    "name": "recipe_add_to_list_past_1_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has viewed items in the past day"
+  },
+  {
+    "name": "recipe_add_to_list_past_2_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has viewed items in the past 2nd day"
+  },
+  {
+    "name": "recipe_add_to_list_past_3_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has viewed items in the past 3rd day"
+  },
+  {
+    "name": "recipe_add_to_list_past_4_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has viewed items in the past 4th day"
+  },
+  {
+    "name": "recipe_add_to_list_past_5_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has viewed items in the past 5th day"
+  },
+  {
+    "name": "recipe_print_past_1_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has added items to cart in the past day"
+  },
+  {
+    "name": "recipe_print_past_2_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has added items to cart in the past 2nd day"
+  },
+  {
+    "name": "recipe_print_past_3_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has added items to cart in the past 3rd day"
+  },
+  {
+    "name": "recipe_print_past_4_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has added items to cart in the past 4th day"
+  },
+  {
+    "name": "recipe_print_past_5_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has added items to cart in the past 5th day"
+  },
+  {
+    "name": "sign_up_past_1_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has checked out in the past day"
+  },
+  {
+    "name": "sign_up_past_2_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has checked out in the past 2nd day"
+  },
+  {
+    "name": "sign_up_past_3_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has checked out in the past 3rd day"
+  },
+  {
+    "name": "sign_up_past_4_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has checked out in the past 4th day"
+  },
+  {
+    "name": "sign_up_past_5_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has checked out in the past 5th day"
+  },
+  {
+    "name": "recipe_favorite_past_1_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has checked out in the past day"
+  },
+  {
+    "name": "recipe_favorite_past_2_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has checked out in the past 2nd day"
+  },
+  {
+    "name": "recipe_favorite_past_3_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has checked out in the past 3rd day"
+  },
+  {
+    "name": "recipe_favorite_past_4_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has checked out in the past 4th day"
+  },
+  {
+    "name": "recipe_favorite_past_5_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has checked out in the past 5th day"
+  },
+  {
+    "name": "recipe_add_to_menu_past_1_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has checked out in the past day"
+  },
+  {
+    "name": "recipe_add_to_menu_past_2_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has checked out in the past 2nd day"
+  },
+  {
+    "name": "recipe_add_to_menu_past_3_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has checked out in the past 3rd day"
+  },
+  {
+    "name": "recipe_add_to_menu_past_4_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has checked out in the past 4th day"
+  },
+  {
+    "name": "recipe_add_to_menu_past_5_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has checked out in the past 5th day"
+  },
+  {
+      "name": "will_login",
+      "type": "INTEGER",
+      "description": "A boolean indicating whether the user will login in the next period"
+  }
+]
\ No newline at end of file
diff --git a/sql/schema/table/purchase_propensity_inference_preparation.json b/sql/schema/table/purchase_propensity_inference_preparation.json
index 0fe328b2..2f8b1256 100644
--- a/sql/schema/table/purchase_propensity_inference_preparation.json
+++ b/sql/schema/table/purchase_propensity_inference_preparation.json
@@ -109,161 +109,6 @@
     "type": "BOOLEAN",
     "description": "A boolean indicating whether the user has signed in with an user id"
   },
-  {
-    "name": "engagement_rate",
-    "type": "FLOAT",
-    "description": "The percentage of sessions that were engaged sessions. Engagement rate = engaged sessions / total sessions Engagement rate is the inverse of bounce rate"
-  },
-  {
-    "name": "engaged_sessions_per_user",
-    "type": "INTEGER",
-    "description": "The number of engaged sessions per user"
-  },
-  {
-    "name": "session_conversion_rate",
-    "type": "FLOAT",
-    "description": "The session conversion rate is calculated by dividing the number of sessions with a conversion event by the total number of sessions"
-  },
-  {
-    "name": "bounces",
-    "type": "INTEGER",
-    "description": "The number of not engaged sessions"
-  },
-  {
-    "name": "bounce_rate_per_user",
-    "type": "FLOAT",
-    "description": "The percentage of sessions that were not engaged sessions per user. Bounce rate = not engaged sessions / total sessions Bounce rate is the inverse of engagement rate"
-  },
-  {
-    "name": "sessions_per_user",
-    "type": "INTEGER",
-    "description": "The number of sessions per user"
-  },
-  {
-    "name": "avg_views_per_session",
-    "type": "FLOAT",
-    "description": "The average number of views per sessions"
-  },
-  {
-    "name": "sum_engagement_time_seconds",
-    "type": "FLOAT",
-    "description": "The sum of time that your website was in focus in a user's browser or an app was in the foreground of a user's device in seconds per user"
-  },
-  {
-    "name": "avg_engagement_time_seconds",
-    "type": "FLOAT",
-    "description": "The average time that your website was in focus in a user's browser or an app was in the foreground of a user's device. Average engagement time = total user engagement durations / number of active users"
-  },
-  {
-    "name": "new_visits",
-    "type": "INTEGER",
-    "description": "The number of times your users opened your website for the first time"
-  },
-  {
-    "name": "returning_visits",
-    "type": "INTEGER",
-    "description": "The number of users who have initiated at least one previous session, regardless of whether or not the previous sessions were engaged sessions"
-  },
-  {
-    "name": "add_to_carts",
-    "type": "INTEGER",
-    "description": "The number of times users added items to their shopping carts"
-  },
-  {
-    "name": "cart_to_view_rate",
-    "type": "FLOAT",
-    "description": "The number of times users added items to their shopping carts divided by the the number of mobile app screens or web pages your users saw. Repeated views of a single screen or page are counted"
-  },
-  {
-    "name": "checkouts",
-    "type": "INTEGER",
-    "description": "The number of times users started the checkout process"
-  },
-  {
-    "name": "ecommerce_purchases",
-    "type": "INTEGER",
-    "description": "The number of purchases on your website or app"
-  },
-  {
-    "name": "ecommerce_quantity",
-    "type": "INTEGER",
-    "description": "The number of units for an ecommerce event"
-  },
-  {
-    "name": "ecommerce_revenue",
-    "type": "FLOAT",
-    "description": "The sum of revenue from purchases made on your website or app, minus any refunds given. Purchase revenue = purchases + in-app purchases + subscriptions - refund"
-  },
-  {
-    "name": "item_revenue",
-    "type": "FLOAT",
-    "description": "The total revenue from items only minus refunds, excluding tax and shipping"
-  },
-  {
-    "name": "item_quantity",
-    "type": "INTEGER",
-    "description": "The number of units for a single item included in ecommerce events"
-  },
-  {
-    "name": "item_view_events",
-    "type": "INTEGER",
-    "description": "The number of times an item was viewed"
-  },
-  {
-    "name": "items_clicked_in_promotion",
-    "type": "INTEGER",
-    "description": "The number of items that the customer clicked in a promotion"
-  },
-  {
-    "name": "items_clicked_in_list",
-    "type": "INTEGER",
-    "description": "The number of items that the customer clicked in a list of items"
-  },
-  {
-    "name": "items_checked_out",
-    "type": "INTEGER",
-    "description": "The number of times the user has checked out"
-  },
-  {
-    "name": "items_added_to_cart",
-    "type": "INTEGER",
-    "description": "The number of times the user has added items to cart"
-  },
-  {
-    "name": "item_list_view_events",
-    "type": "INTEGER",
-    "description": "The number of times the user has viewed items in list"
-  },
-  {
-    "name": "purchase_revenue",
-    "type": "FLOAT",
-    "description": "The total revenue from purchases, in-app purchases, subscriptions, and ad revenue. Total revenue = purchases + in-app purchases + subscriptions + ad revenue - refunds"
-  },
-  {
-    "name": "purchase_to_view_rate",
-    "type": "FLOAT",
-    "description": "The number of purchases on your website or app divided by the number of mobile app screens or web pages your users saw"
-  },
-  {
-    "name": "transactions_per_purchaser",
-    "type": "FLOAT",
-    "description": "The average number of purchases per buyer for the selected time frame"
-  },
-  {
-    "name": "user_conversion_rate",
-    "type": "FLOAT",
-    "description": "The number of users who performed a conversion action divided by the total number of users"
-  },
-  {
-    "name": "how_many_purchased_before",
-    "type": "INTEGER",
-    "description": "The number of times the user have purchased before"
-  },
-  {
-    "name": "has_abandoned_cart",
-    "type": "BOOLEAN",
-    "description": "a boolean indicating whether the user has abandoned a cart in the past"
-  },
   {
     "name": "active_users_past_1_day",
     "type": "INTEGER",
diff --git a/sql/schema/table/purchase_propensity_training_preparation.json b/sql/schema/table/purchase_propensity_training_preparation.json
index e5d284d5..f984f42e 100644
--- a/sql/schema/table/purchase_propensity_training_preparation.json
+++ b/sql/schema/table/purchase_propensity_training_preparation.json
@@ -119,161 +119,6 @@
       "type": "BOOLEAN",
       "description": "A boolean indicating whether the user has signed in with the user id"
   },
-  {
-      "name": "engagement_rate",
-      "type": "FLOAT",
-      "description": "The percentage of sessions that were engaged sessions. Engagement rate = engaged sessions / total sessions Engagement rate is the inverse of bounce rate"
-  },
-  {
-      "name": "engaged_sessions_per_user",
-      "type": "INTEGER",
-      "description": "The number of engaged sessions per user"
-  },
-  {
-      "name": "session_conversion_rate",
-      "type": "FLOAT",
-      "description": "The session conversion rate is calculated by dividing the number of sessions with a conversion event by the total number of sessions"
-  },
-  {
-      "name": "bounces",
-      "type": "INTEGER",
-      "description": "The number of not engaged sessions"
-  },
-  {
-      "name": "bounce_rate_per_user",
-      "type": "FLOAT",
-      "description": "The percentage of sessions that were not engaged sessions per user. Bounce rate = not engaged sessions / total sessions Bounce rate is the inverse of engagement rate"
-  },
-  {
-      "name": "sessions_per_user",
-      "type": "INTEGER",
-      "description": "The number of sessions per user"
-  },
-  {
-      "name": "avg_views_per_session",
-      "type": "FLOAT",
-      "description": "The average number of views per sessions"
-  },
-  {
-      "name": "sum_engagement_time_seconds",
-      "type": "FLOAT",
-      "description": "The sum of time that your website was in focus in a user's browser or an app was in the foreground of a user's device in seconds per user"
-  },
-  {
-      "name": "avg_engagement_time_seconds",
-      "type": "FLOAT",
-      "description": "The average time that your website was in focus in a user's browser or an app was in the foreground of a user's device. Average engagement time = total user engagement durations / number of active users"
-  },
-  {
-      "name": "new_visits",
-      "type": "INTEGER",
-      "description": "The number of times your users opened your website for the first time"
-  },
-  {
-      "name": "returning_visits",
-      "type": "INTEGER",
-      "description": "The number of users who have initiated at least one previous session, regardless of whether or not the previous sessions were engaged sessions"
-  },
-  {
-      "name": "add_to_carts",
-      "type": "INTEGER",
-      "description": "The number of times users added items to their shopping carts"
-  },
-  {
-      "name": "cart_to_view_rate",
-      "type": "FLOAT",
-      "description": "The number of times users added items to their shopping carts divided by the the number of mobile app screens or web pages your users saw. Repeated views of a single screen or page are counted"
-  },
-  {
-      "name": "checkouts",
-      "type": "INTEGER",
-      "description": "The number of times users started the checkout process"
-  },
-  {
-      "name": "ecommerce_purchases",
-      "type": "INTEGER",
-      "description": "The number of purchases on your website or app"
-  },
-  {
-      "name": "ecommerce_quantity",
-      "type": "INTEGER",
-      "description": "The number of units for an ecommerce event"
-  },
-  {
-      "name": "ecommerce_revenue",
-      "type": "FLOAT",
-      "description": "The sum of revenue from purchases made on your website or app, minus any refunds given. Purchase revenue = purchases + in-app purchases + subscriptions - refund"
-  },
-  {
-      "name": "item_revenue",
-      "type": "FLOAT",
-      "description": "The total revenue from items only minus refunds, excluding tax and shipping"
-  },
-  {
-      "name": "item_quantity",
-      "type": "INTEGER",
-      "description": "The number of units for a single item included in ecommerce events"
-  },
-  {
-      "name": "item_view_events",
-      "type": "INTEGER",
-      "description": "The number of times an item was viewed"
-  },
-  {
-      "name": "items_clicked_in_promotion",
-      "type": "INTEGER",
-      "description": "The number of items that the customer clicked in a promotion"
-  },
-  {
-      "name": "items_clicked_in_list",
-      "type": "INTEGER",
-      "description": "The number of items that the customer clicked in a list of items"
-  },
-  {
-      "name": "items_checked_out",
-      "type": "INTEGER",
-      "description": "The number of times the user has checked out"
-  },
-  {
-      "name": "items_added_to_cart",
-      "type": "INTEGER",
-      "description": "The number of times the user has added items to cart"
-  },
-  {
-      "name": "item_list_view_events",
-      "type": "INTEGER",
-      "description": "The number of times the user has viewed items in list"
-  },
-  {
-      "name": "purchase_revenue",
-      "type": "FLOAT",
-      "description": "The total revenue from purchases, in-app purchases, subscriptions, and ad revenue. Total revenue = purchases + in-app purchases + subscriptions + ad revenue - refunds"
-  },
-  {
-      "name": "purchase_to_view_rate",
-      "type": "FLOAT",
-      "description": "The number of purchases on your website or app divided by the number of mobile app screens or web pages your users saw"
-  },
-  {
-      "name": "transactions_per_purchaser",
-      "type": "FLOAT",
-      "description": "The average number of purchases per buyer for the selected time frame"
-  },
-  {
-      "name": "user_conversion_rate",
-      "type": "FLOAT",
-      "description": "The number of users who performed a conversion action divided by the total number of users"
-  },
-  {
-      "name": "how_many_purchased_before",
-      "type": "INTEGER",
-      "description": "The number of times the user have purchased before"
-  },
-  {
-      "name": "has_abandoned_cart",
-      "type": "BOOLEAN",
-      "description": "a boolean indicating whether the user has abandoned a cart in the past"
-  },
   {
       "name": "active_users_past_1_day",
       "type": "INTEGER",
@@ -544,131 +389,6 @@
       "type": "INTEGER",
       "description": "The number of times the user has checked out in the past 15 to 30 days"
   },
-  {
-      "name": "purchasers_users",
-      "type": "INTEGER",
-      "description": "The number of distinct users who have purchases in the past"
-  },
-  {
-      "name": "average_daily_purchasers",
-      "type": "FLOAT",
-      "description": "The average number of purchasers across all the days in the selected time frame"
-  },
-  {
-      "name": "active_users",
-      "type": "INTEGER",
-      "description": "The number of distinct users who visited your website or application. An active user is any user who has an engaged session or when Analytics collects: the first_visit event or engagement_time_msec parameter from a website the first_open event or engagement_time_msec parameter from an Android app the first_open or user_engagement event from an iOS app"
-  },
-  {
-      "name": "DAU",
-      "type": "FLOAT",
-      "description": "The number of users who engaged for the calendar day"
-  },
-  {
-      "name": "MAU",
-      "type": "FLOAT",
-      "description": "The number of users who engaged in the last 30 days"
-  },
-  {
-      "name": "WAU",
-      "type": "FLOAT",
-      "description": "The number of users who engaged in the last week"
-  },
-  {
-      "name": "dau_per_mau",
-      "type": "FLOAT",
-      "description": "Daily Active Users (DAU) / Monthly Active Users (MAU) shows the percentage of users who engaged for the calendar day out of the users who engaged in the last 30 days. A higher ratio suggests good engagement and user retention"
-  },
-  {
-      "name": "dau_per_wau",
-      "type": "FLOAT",
-      "description": "Daily Active Users (DAU) / Weekly Active Users (WAU) shows the percentage of users who engaged in the last 24 hours out of the users who engaged in the last 7 days. A higher ratio suggests good engagement and user retention"
-  },
-  {
-      "name": "wau_per_mau",
-      "type": "FLOAT",
-      "description": "Weekly Active Users (DAU) / Monthly Active Users (MAU) shows the percentage of users who engaged in the last 7 days out of the users who engaged in the last 30 days. A higher ratio suggests good engagement and user retention"
-  },
-  {
-      "name": "users_engagement_duration_seconds",
-      "type": "FLOAT",
-      "description": "The length of time that your app screen was in the foreground or your web page was in focus in seconds"
-  },
-  {
-      "name": "average_engagement_time",
-      "type": "FLOAT",
-      "description": "The average time that your website was in focus in a user's browser or an app was in the foreground of a user's device. Average engagement time = total user engagement durations / number of active users"
-  },
-  {
-      "name": "average_engagement_time_per_session",
-      "type": "FLOAT",
-      "description": "The average engagement time per session"
-  },
-  {
-      "name": "average_sessions_per_user",
-      "type": "FLOAT",
-      "description": "The average number of sessions per user"
-  },
-  {
-      "name": "ARPPU",
-      "type": "FLOAT",
-      "description": "Average revenue per paying user (ARPPU) is the total purchase revenue per active user who made a purchase"
-  },
-  {
-      "name": "ARPU",
-      "type": "FLOAT",
-      "description": "Average revenue per active user (ARPU) is the total revenue generated on average from each active user, whether they made a purchase or not. ARPU = (Total ad revenue + purchase revenue + in-app purchase revenue + subscriptions) / Active users"
-  },
-  {
-      "name": "average_daily_revenue",
-      "type": "FLOAT",
-      "description": "Average daily revenue The average total revenue for a day over the selected time frame"
-  },
-  {
-      "name": "max_daily_revenue",
-      "type": "FLOAT",
-      "description": "The maximum total revenue for a day over the selected time frame"
-  },
-  {
-      "name": "min_daily_revenue",
-      "type": "FLOAT",
-      "description": "The minimum total revenue for a day over the selected time frame"
-  },
-  {
-      "name": "new_users",
-      "type": "INTEGER",
-      "description": "The number of new unique user IDs that logged the first_open or first_visit event. The metric allows you to measure the number of users who interacted with your site or launched your app for the first time"
-  },
-  {
-      "name": "returning_users",
-      "type": "INTEGER",
-      "description": "The number of users who have initiated at least one previous session, regardless of whether or not the previous sessions were engaged sessions"
-  },
-  {
-      "name": "first_time_purchasers",
-      "type": "INTEGER",
-      "description": "The number of users who made their first purchase in the selected time frame."
-  },
-  {
-      "name": "first_time_purchaser_conversion",
-      "type": "FLOAT",
-      "description": "The percentage of active users who made their first purchase. This metric is returned as a fraction; for example, 0.092 means 9.2% of active users were first-time purchasers"
-  },
-  {
-      "name": "first_time_purchasers_per_new_user",
-      "type": "FLOAT",
-      "description": "The average number of first-time purchasers per new user"
-  },
-  {
-      "name": "avg_user_conversion_rate",
-      "type": "FLOAT",
-      "description": "The average number of converting user per total users"
-  },
-  {
-      "name": "avg_session_conversion_rate",
-      "type": "FLOAT",
-      "description": "The average number of converting session per total sessions"
-  },
   {
       "name": "will_purchase",
       "type": "INTEGER",
diff --git a/sql/schema/table/user_rolling_window_lead_metrics.json b/sql/schema/table/user_rolling_window_lead_metrics.json
new file mode 100644
index 00000000..e22d0ceb
--- /dev/null
+++ b/sql/schema/table/user_rolling_window_lead_metrics.json
@@ -0,0 +1,242 @@
+[
+  {
+      "name": "processed_timestamp",
+      "type": "TIMESTAMP",
+      "description": "Timestamp of when the data was processed"
+  },
+  {
+      "name": "feature_date",
+      "type": "DATE",
+      "description": "The date serving as basis for the features calculation"
+  },
+  {
+      "name": "user_pseudo_id",
+      "type": "STRING",
+      "description": "The user pseudo identifier"
+  },
+  {
+      "name": "scroll_50_past_1_day",
+      "type": "INTEGER",
+      "description": "The number of times the user has been active in the past 1 day"
+  },
+  {
+      "name": "scroll_50_past_2_day",
+      "type": "INTEGER",
+      "description": "The number of times the user has been active in the past 2nd day"
+  },
+  {
+      "name": "scroll_50_past_3_day",
+      "type": "INTEGER",
+      "description": "The number of times the user has been active in the past 3rd day"
+  },
+  {
+      "name": "scroll_50_past_4_day",
+      "type": "INTEGER",
+      "description": "The number of times the user has been active in the past 4th day"
+  },
+  {
+      "name": "scroll_50_past_5_day",
+      "type": "INTEGER",
+      "description": "The number of times the user has been active in the past 5th day"
+  },
+  {
+      "name": "scroll_90_past_1_day",
+      "type": "INTEGER",
+      "description": "The number of times the user has purchased in the past day"
+  },
+  {
+      "name": "scroll_90_past_2_day",
+      "type": "INTEGER",
+      "description": "The number of times the user has purchased in the past 2nd day"
+  },
+  {
+      "name": "scroll_90_past_3_day",
+      "type": "INTEGER",
+      "description": "The number of times the user has purchased in the past 3rd day"
+  },
+  {
+      "name": "scroll_90_past_4_day",
+      "type": "INTEGER",
+      "description": "The number of times the user has purchased in the past 4th day"
+  },
+  {
+      "name": "scroll_90_past_5_day",
+      "type": "INTEGER",
+      "description": "The number of times the user has purchased in the past 5th day"
+  },
+  {
+      "name": "view_search_results_past_1_day",
+      "type": "INTEGER",
+      "description": "The number of times the user has visited in the past day"
+  },
+  {
+      "name": "view_search_results_past_2_day",
+      "type": "INTEGER",
+      "description": "The number of times the user has visited in the past 2nd day"
+  },
+  {
+      "name": "view_search_results_past_3_day",
+      "type": "INTEGER",
+      "description": "The number of times the user has visited in the past 3rd day"
+  },
+  {
+      "name": "view_search_results_past_4_day",
+      "type": "INTEGER",
+      "description": "The number of times the user has visited in the past 4th day"
+  },
+  {
+      "name": "view_search_results_past_5_day",
+      "type": "INTEGER",
+      "description": "The number of times the user has visited in the past 5th day"
+  },
+  {
+      "name": "file_download_past_1_day",
+      "type": "INTEGER",
+      "description": "The number of times the user has visited in the past day"
+  },
+  {
+      "name": "file_download_past_2_day",
+      "type": "INTEGER",
+      "description": "The number of times the user has visited in the past 2nd day"
+  },
+  {
+      "name": "file_download_past_3_day",
+      "type": "INTEGER",
+      "description": "The number of times the user has visited in the past 3rd day"
+  },
+  {
+      "name": "file_download_past_4_day",
+      "type": "INTEGER",
+      "description": "The number of times the user has visited in the past 4th day"
+  },
+  {
+      "name": "file_download_past_5_day",
+      "type": "INTEGER",
+      "description": "The number of times the user has visited in the past 5th day"
+  },
+  {
+      "name": "recipe_add_to_list_past_1_day",
+      "type": "INTEGER",
+      "description": "The number of times the user has viewed items in the past day"
+  },
+  {
+      "name": "recipe_add_to_list_past_2_day",
+      "type": "INTEGER",
+      "description": "The number of times the user has viewed items in the past 2nd day"
+  },
+  {
+      "name": "recipe_add_to_list_past_3_day",
+      "type": "INTEGER",
+      "description": "The number of times the user has viewed items in the past 3rd day"
+  },
+  {
+      "name": "recipe_add_to_list_past_4_day",
+      "type": "INTEGER",
+      "description": "The number of times the user has viewed items in the past 4th day"
+  },
+  {
+      "name": "recipe_add_to_list_past_5_day",
+      "type": "INTEGER",
+      "description": "The number of times the user has viewed items in the past 5th day"
+  },
+  {
+      "name": "recipe_print_past_1_day",
+      "type": "INTEGER",
+      "description": "The number of times the user has added items to cart in the past day"
+  },
+  {
+      "name": "recipe_print_past_2_day",
+      "type": "INTEGER",
+      "description": "The number of times the user has added items to cart in the past 2nd day"
+  },
+  {
+      "name": "recipe_print_past_3_day",
+      "type": "INTEGER",
+      "description": "The number of times the user has added items to cart in the past 3rd day"
+  },
+  {
+      "name": "recipe_print_past_4_day",
+      "type": "INTEGER",
+      "description": "The number of times the user has added items to cart in the past 4th day"
+  },
+  {
+      "name": "recipe_print_past_5_day",
+      "type": "INTEGER",
+      "description": "The number of times the user has added items to cart in the past 5th day"
+  },
+  {
+      "name": "sign_up_past_1_day",
+      "type": "INTEGER",
+      "description": "The number of times the user has checked out in the past day"
+  },
+  {
+      "name": "sign_up_past_2_day",
+      "type": "INTEGER",
+      "description": "The number of times the user has checked out in the past 2nd day"
+  },
+  {
+      "name": "sign_up_past_3_day",
+      "type": "INTEGER",
+      "description": "The number of times the user has checked out in the past 3rd day"
+  },
+  {
+      "name": "sign_up_past_4_day",
+      "type": "INTEGER",
+      "description": "The number of times the user has checked out in the past 4th day"
+  },
+  {
+      "name": "sign_up_past_5_day",
+      "type": "INTEGER",
+      "description": "The number of times the user has checked out in the past 5th day"
+  },
+  {
+    "name": "recipe_favorite_past_1_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has checked out in the past day"
+  },
+  {
+    "name": "recipe_favorite_past_2_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has checked out in the past 2nd day"
+  },
+  {
+    "name": "recipe_favorite_past_3_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has checked out in the past 3rd day"
+  },
+  {
+    "name": "recipe_favorite_past_4_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has checked out in the past 4th day"
+  },
+  {
+    "name": "recipe_favorite_past_5_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has checked out in the past 5th day"
+  },
+  {
+    "name": "recipe_add_to_menu_past_1_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has checked out in the past day"
+  },
+  {
+    "name": "recipe_add_to_menu_past_2_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has checked out in the past 2nd day"
+  },
+  {
+    "name": "recipe_add_to_menu_past_3_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has checked out in the past 3rd day"
+  },
+  {
+    "name": "recipe_add_to_menu_past_4_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has checked out in the past 4th day"
+  },
+  {
+    "name": "recipe_add_to_menu_past_5_day",
+    "type": "INTEGER",
+    "description": "The number of times the user has checked out in the past 5th day"
+  }
+]
\ No newline at end of file
diff --git a/templates/activation_query/lead_score_propensity_query_template.sqlx b/templates/activation_query/lead_score_propensity_query_template.sqlx
new file mode 100644
index 00000000..5ad0b874
--- /dev/null
+++ b/templates/activation_query/lead_score_propensity_query_template.sqlx
@@ -0,0 +1,14 @@
+SELECT
+  a.prediction AS user_prop_l_s_p_prediction,
+  NTILE(10) OVER (ORDER BY a.prediction_prob DESC) AS user_prop_l_s_p_decile,
+  b.user_pseudo_id AS client_id,
+  b.user_id AS user_id,
+  b.ga_session_id AS event_param_session_id,
+  '100' AS event_param_engagement_time_msec,
+  CASE WHEN EXTRACT(MICROSECOND FROM b.event_timestamp) = 1 THEN b.event_timestamp ELSE TIMESTAMP_SUB(b.event_timestamp, INTERVAL 1 MICROSECOND) END AS inference_date
+FROM
+  `${mds_project_id}.marketing_ga4_v1_${mds_dataset_suffix}.latest_event_per_user_last_72_hours` b,
+  `{{source_table}}` a
+WHERE
+  COALESCE(a.user_id, "") = COALESCE(b.user_id, "")
+  AND a.user_pseudo_id = b.user_pseudo_id
diff --git a/templates/activation_query/lead_score_propensity_vbb_query_template.sqlx b/templates/activation_query/lead_score_propensity_vbb_query_template.sqlx
new file mode 100644
index 00000000..9be0e0a9
--- /dev/null
+++ b/templates/activation_query/lead_score_propensity_vbb_query_template.sqlx
@@ -0,0 +1,35 @@
+WITH user_prediction_decile AS (
+  SELECT
+    a.prediction AS l_s_p_prediction,
+    NTILE(10) OVER (ORDER BY a.prediction_prob DESC) AS l_s_p_decile,
+    b.user_pseudo_id AS client_id,
+    b.user_id AS user_id,
+    b.ga_session_id AS session_id,
+    CASE
+      WHEN EXTRACT(MICROSECOND FROM b.event_timestamp) = 1 THEN b.event_timestamp
+      ELSE TIMESTAMP_SUB(b.event_timestamp, INTERVAL 1 MICROSECOND)
+    END AS inference_date
+  FROM
+    `${mds_project_id}.marketing_ga4_v1_${mds_dataset_suffix}.latest_event_per_user_last_24_hours` b,
+    `{{source_table}}` a
+  WHERE
+    COALESCE(a.user_id, "") = COALESCE(b.user_id, "")
+    AND a.user_pseudo_id = b.user_pseudo_id)
+SELECT
+  a.l_s_p_prediction AS user_prop_l_s_p_prediction,
+  a.l_s_p_decile AS user_prop_l_s_p_decile,
+  b.value AS event_param_value,
+  'USD' AS event_param_currency,
+  a.client_id,
+  a.user_id,
+  a.session_id AS event_param_session_id,
+  a.inference_date
+FROM
+  user_prediction_decile AS a
+LEFT JOIN
+  `${activation_project_id}.${dataset}.vbb_activation_configuration` AS b
+ON
+  a.l_s_p_decile = b.decile
+WHERE
+  b.activation_type = 'lead-score-propensity'
+AND b.value > 0
\ No newline at end of file
diff --git a/templates/activation_type_configuration_template.tpl b/templates/activation_type_configuration_template.tpl
index d73206c7..913b70a2 100644
--- a/templates/activation_type_configuration_template.tpl
+++ b/templates/activation_type_configuration_template.tpl
@@ -46,5 +46,9 @@
     "churn-propensity-15-7": {
         "activation_event_name": "maj_churn_propensity_15_7",
         "source_query_template": "${churn_propensity_query_template_gcs_path}"
+    },
+    "lead-score-propensity-30-15": {
+        "activation_event_name": "maj_lead_score_propensity_30_15",
+        "source_query_template": "${lead_score_propensity_query_template_gcs_path}"
     }
 }
diff --git a/templates/activation_user_import/lead_score_propensity_csv_export.sqlx b/templates/activation_user_import/lead_score_propensity_csv_export.sqlx
new file mode 100644
index 00000000..376cea56
--- /dev/null
+++ b/templates/activation_user_import/lead_score_propensity_csv_export.sqlx
@@ -0,0 +1,27 @@
+DECLARE
+  select_query STRING;
+SET
+  select_query = FORMAT("""
+    CREATE TEMPORARY TABLE tmp_selection AS
+      SELECT
+        user_pseudo_id AS client_id,
+        '${ga4_stream_id}' AS stream_id,
+        prediction AS l_s_p_prediction,
+        NTILE(10) OVER (ORDER BY prediction_prob DESC) AS l_s_p_decile
+      FROM `%s`
+  """, prediction_table_name);
+EXECUTE IMMEDIATE
+  select_query;
+EXPORT DATA
+  OPTIONS ( uri = 'gs://${export_bucket}/csv-export/lead_score_propensity-*.csv',
+    format = 'CSV',
+    OVERWRITE = TRUE,
+    header = TRUE,
+    field_delimiter = ',' ) AS (
+  SELECT
+    client_id,
+    stream_id,
+    l_s_p_prediction,
+    l_s_p_decile
+  FROM
+    tmp_selection );
diff --git a/templates/vbb_activation_configuration.jsonl b/templates/vbb_activation_configuration.jsonl
index a0c64ad1..57b200e0 100644
--- a/templates/vbb_activation_configuration.jsonl
+++ b/templates/vbb_activation_configuration.jsonl
@@ -1,2 +1,3 @@
 {"activation_type":"purchase-propensity","value_norm":150,"decile_multiplier":[{"decile":1,"multiplier":5.5},{"decile":2,"multiplier":3},{"decile":3,"multiplier":2},{"decile":4,"multiplier":1},{"decile":5,"multiplier":0},{"decile":6,"multiplier":0},{"decile":7,"multiplier":0},{"decile":8,"multiplier":0},{"decile":9,"multiplier":0},{"decile":10,"multiplier":0}]}
-{"activation_type":"cltv","value_norm":500,"decile_multiplier":[{"decile":1,"multiplier":5.5},{"decile":2,"multiplier":3},{"decile":3,"multiplier":2},{"decile":4,"multiplier":1},{"decile":5,"multiplier":0},{"decile":6,"multiplier":0},{"decile":7,"multiplier":0},{"decile":8,"multiplier":0},{"decile":9,"multiplier":0},{"decile":10,"multiplier":0}]}
\ No newline at end of file
+{"activation_type":"cltv","value_norm":500,"decile_multiplier":[{"decile":1,"multiplier":5.5},{"decile":2,"multiplier":3},{"decile":3,"multiplier":2},{"decile":4,"multiplier":1},{"decile":5,"multiplier":0},{"decile":6,"multiplier":0},{"decile":7,"multiplier":0},{"decile":8,"multiplier":0},{"decile":9,"multiplier":0},{"decile":10,"multiplier":0}]}
+{"activation_type":"lead-score-propensity","value_norm":150,"decile_multiplier":[{"decile":1,"multiplier":5.5},{"decile":2,"multiplier":3},{"decile":3,"multiplier":2},{"decile":4,"multiplier":1},{"decile":5,"multiplier":0},{"decile":6,"multiplier":0},{"decile":7,"multiplier":0},{"decile":8,"multiplier":0},{"decile":9,"multiplier":0},{"decile":10,"multiplier":0}]}
\ No newline at end of file