Customer Propensity to RFM Purchase Modelling

Business Objective

Our Client is an early-stage e-commerce company selling various products from daily essentials to high-end electronics and home appliances. They aim to increase purchases by sending discounts or coupons to users based on a predictive model that estimates purchase probability.

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Objective

This project focuses on building a Propensity to Purchase Model using Python, with a primary objective of improving user engagement and ROI. We employ Propensity Modeling and RFM (Recency, Frequency, Monetary) Analysis to predict users' likelihood of making a purchase and to identify high-value customer segments.

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Data Description

The dataset contains purchase history data for an e-commerce company over a period of time.

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Aim

1. Understand Propensity Modeling.
2. Understand RFM Analysis.
3. Build a model to predict the purchase probability of each user in an e-commerce company using the Propensity Model.

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Tech Stack

• Language: Python
• Libraries: pandas, scikit-learn, numpy, seaborn, datetime, matplotlib, missingno

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Approach

1. Import the required libraries and packages.
2. Read the CSV file.
3. Perform data preprocessing.
4. Conduct exploratory data analysis.
   • Univariate analysis
   • Multivariate analysis
5. Perform RFM Analysis.
6. Perform feature engineering.
7. Create modeling data.
8. Build the predictive model.
9. Make predictions.

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Code Structure

• input: Contains data and configuration files.
  • config.yaml: Configuration parameters.
  • final_customer_data.xlsx: Customer transaction data.
  • final_customer_data_with_RFM_features.csv: Merged dataset with RFM values.
  • ecom_product_data.csv: Transaction data for RFM modeling.
• src: Contains modularized code for different project steps.
  • engine.py: Main execution script.
  • ml_pipeline: Modular functions.
  • requirements.txt: List of required packages.
• output: Stores the trained model for future use.
• lib: Reference notebooks from the project.

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