The Customer Segmentation Tool is a data analysis project that aims to segment customers into distinct groups based on various factors such as demographics, purchase history, and behavior. This tool provides valuable insights for businesses to tailor their marketing strategies, product offerings, and customer engagement approaches to better meet the needs and preferences of each customer segment.
- RFM Analysis: Utilizes Recency, Frequency, and Monetary analysis to group customers based on their recent purchasing behavior.
- Behavioral Segmentation: Segments customers based on their online behavior, such as page views, email subscriptions, and last login date.
- Machine Learning-Based Clustering: Applies machine learning algorithms to identify hidden patterns and create customer personas for targeted marketing.
In this project, we have thoroughly tested the following scenarios:
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Data Generation: Ensured that the generated customer data shows clear statistical differences among personas, including High-Income Shoppers, Young Professionals, Frequent Shoppers, and Value-Conscious Consumers.
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RFM Analysis: Successfully performed RFM analysis to segment customers based on recency, frequency, and monetary factors.
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Behavioral Segmentation: Implemented behavioral segmentation using a suitable threshold for page views and validated the results for each persona.
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Machine Learning-Based Clustering: Utilized machine learning algorithms, such as K-Means clustering, to create customer personas and tested the clustering quality using silhouette analysis.
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Visualization: Created visualizations, including silhouette analysis plots and cluster personas charts, to provide insights into the customer segmentation results.
Please refer to the respective sections in the documentation for detailed information on each scenario.