Lead scoring and segmentation for an online education company

• Introduction
• Objectives
• Project results
  • Actionable initiatives
  • Lead scoring model + web application
  • Lead segmentation model
• Project structure
• Instructions
• License

Introduction

The client is an online education company which sells an online course to industry professionals.

The company markets their course on different websites and search engines. Once professionals who are interested in the course land on the website, they might browse the course or fill up a form for the course or watch some videos. When these people fill up a form providing their email address or phone number, they are classified to be a lead. Moreover, the company also gets leads through past referrals.

Once these leads are acquired, employees from the sales team start making calls, writing emails, etc. Through this process, some of the leads get converted while most do not, with the inefficiency of this process impacting company’s benefits.

• See a detailed technical explanation of the project here.

Objectives

1. Analysing historical leads information to propose potential actions that increase overall company turnover.
2. Creating advanced analytical assets such as a predictive lead scoring and customer segmentation algorithms that helps sales team to identify both potential customers who are most likely to convert into paying customers and leads who are not economically profitable to manage.

Project results

Actionable initiatives derived from exploratory data analysis

Actions to improve company's customer knowledge

1. Improve quality of the survey or form questions to receive more user inputs and reduce NaN/default ('Select') values.
2. Improve algorithm for the activity and profile score/index to produce complete and more accurate results.
3. Colect time stamp visiting the website for seasonality analysis and implement cookies to identity and track users as they navigate different pages on the website.
4. Implement a new lead segmentation algorithm that identifies the company's different leads profiles and makes it possible to identify which group best fit for each new lead, in order to be able to carry out more personalised commercial actions.

Actions to improve lead-to-customer conversion rate

1. Implement a predictive lead scoring algorithm that identifies people who are most likely to convert into paying customers through the most efficient channel for each lead and relieves the sales team of the workload of manually filling in features such as lead_quality, lead_profile or tags so they will be able to spend more time on contacting the most promising customers.

Actions to improve commercial and marketing channels performance

1. Improve the content strategy of the website, lead magnet and emails to attract traffic and increase the time people spend on the website by creating tailored content mainly for working professionals in the Indian management sector.
2. Create a referral program to encourage existing customers to recommend the course to their friends, family, and colleagues.
3. Invest more resources into acquiring leads from 'Welingak website'.
4. Increase investments in SMS campaigns as they are performing well.
5. Check whether the default value for advertisement features is set to 'No' in the web form, which could explain the high percentage of 'No' for all of them. If this is not the case, then the advertising investment strategy should be completely revised as it is not generating almost any lead (0,24%).
6. Check that the sales team is only contacting people who have given their consent to do so.

Lead scoring model and web application

By applying the developed lead scoring predictive model, the company has been able to:

1. Increase its sales profit by 2.7%.
2. Save 36.8% of the amount of money lost due to the management of low quality leads.
3. Increase lead-to-customer conversion rate from 38% to 50%.
4. Save 23% of time spent by employees on managing leads.

	As is	To be	Improvements
Conversion Rate	0.3896	0.4985	Increased by 0.11
Workload (nº leads to be managed)	1789 leads	1376 leads	Reduced by 23.09%
Lost investments ($)	3549	2242.50	Reduced by 36.81%
Sales profit ($)	29028.78	29821.14	Increased by 2.73%

In order to get the most value out of the developed machine learning model, a prototype web application has been designed so employees can start using them to make practical decisions. This web app collect, on the one hand, the internal data that the company has for each lead (time spent on the website, number of visits, origin…) and on the other hand, the information provided by the lead itself through a web form.

Launch Lead Score Analyzer Web App!

Lead segmentation model

Six different potential customer profiles have been identified. Through the analysis of these customer segments, a series of insights have been derived that lead to the following actionable initiatives:

1. The company's most valuable leads are those that come from referrals or from Welingak website, and even more so if they are working professionals. As proposed in the exploratory data analysis section, the company should seriously consider creating a referral programme to encourage existing customers to recommend the course to their close circle.
2. SMS campaigns are performing quite well. However, these campaigns should focus on:
   • Working professionals comming from API or landing page who spend above-average time on the website.
   • Leads comming from References or Welingak website regardless of their occupation and time spend on the website.
   • Avoid sending sms to leads who come from API and have spent a short time on the site.
3. Olark chat is not performing well. The company should consider withdrawing investment in this service and for leads coming from API replace it with:
   • Email marketing campaigns in case of working professionals who spent a short time on the website or in case of unemployed leads.
   • SMS campaigns in case of working professionals who spend above-average time on the website, as discussed in the previous point.

Project structure

• 📁 01_Documents
  • Contains basic project files:
    • Leads Data Dictionary.pdf: feature-level metadata.
    • pf_leadscoring.yml: project environment file.
    • Development stage_Data Transformation Design.xlsx: support file for designing feature transformation processes.
    • Production stage_Processes Design: support file for designing final production script.
• 📁 02_Data
  • 📁 01_Originals
    • Leads.csv: Original dataset.
  • 📁 02_Validation
    • validation.csv: Sample extracted from the original dataset at the beginning of the project in order to be used to check the correct performance of the model once it is put into production.
  • 📁 03_Work
    • This folder contains the datasets resulting from each of the stages of the project (data quality, exploratory data analysis, feature transformation...).
• 📁 03_Notebooks
  • 📁 02_Development
    • 00_Project Design.ipynb: Notebook compiling the initial design of the project.
    • 01_Set Up.ipynb: Notebook used for the initial set up of the project.
    • 02_Data Quality.ipynb: Notebook detailing and executing all data quality processes.
    • 03_EDA.ipynb: Notebook used for the execution of the exploratory data analysis and which collects the business insights found.
    • 04_Feature Transformation.ipynb: Notebook that details and executes the data transformation processes necessary to prepare the features for input into the models.
    • 05_Unsupervised Modelling.ipynb: Notebook for modelling the unsupervised Kmeans algorithm used to perform lead segmentation. It also includes the segment profiling from a business point of view as well as the insights and conclusions obtained from this process.
    • 06_Feature Selection.ipynb: Notebook used to make a selection of the final variables to be entered into the models.
    • 07_Supervised Classification Modelling.ipynb: Notebook for modelling the predictive lead scoring model. Model selection, hyperparameterisation, selection of the optimal discrimination threshold and evaluation of results.
    • 08_Production Code Preparation.ipynb: Notebook used to compile all the quality, transformation and variable selection processes, as well as the final model and execution and retraining processes, with the aim of creating the final retraining and execution pipes that condense all the aforementioned processes.
    • 09_Retraining script.ipynb: Notebook to retrain the model with new data when necessary.
    • 10_Execution script.ipynb: Notebook to execute the final model and obtain the results.
  • 📁 03_System/App_lead_scoring
    • This folder contains the files (app script, production script, models, ...) used in the deployment of the web application Lead scoring analyzer.
• 📁 04_Models
  • pipe_execution.pickle: pipe that condenses the final trained model as well as all necessary prior data transformations.
  • pipe_training.pickle: pipe that condenses the entire training process. It can be used to retrain the model with new data when necessary.
  • optimal_disc_threshold.pickle: Contains the value of the optimal discrimination threshold of the model that maximises the company's roi. It is used by pipe_training.pickle and is updated when re-training the model with pipe_training.pickle.
• 📁 05_Results
  • Project Results.ipynb: Notebook summarising the insights and KPIs improvements achieved from the exploratory data analysis as well as from the execution of the scoring and lead segmentation machine learning models.
  • Execution script.py: Python script to execute the model and obtain the results.
  • Retraining script.py: Python script to retrain the model with new data when necessary.
  • final features.pickle: Names of the final features pre-selected for input to the model.

Instructions

The project should be run using exactly the same environment in which it was created.

• Project environment can be replicated using 'pf_leadscoring.yml' file which was created during the set up phase of the project. It can be found in the folder '01_Documents'.
• Copy 'pf_leadscoring.yml' file to the directory and using the terminal or anaconda prompt execute:

  conda env create --file pf_leadscoring.yml --name project_name

By other hand, remember to update the project_path variable of the notebooks to the path where you have replicated the project.

Licensing

The data set, licensing, and other descriptive information is available here.