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Customer Lifetime Value Prediction using Python's Lifetimes Package

This repository demonstrates how to use Python's lifetimes package to predict Customer Lifetime Value (CLV) and identify high-value customers. By applying the BG/NBD and Gamma-Gamma models, businesses can predict future purchasing behavior and estimate transaction value, enabling targeted marketing strategies that enhance customer retention and maximize revenue.

Table of Contents

Introduction

In a competitive e-commerce landscape, understanding Customer Lifetime Value (CLV) is essential for retaining customers and optimizing marketing efforts. This project uses the lifetimes package to estimate CLV, helping businesses focus on high-value customers.

Usage

  1. Data Preparation: Load and preprocess your customer transaction data.
  2. Modeling: Apply the BG/NBD model to predict purchase frequency and the Gamma-Gamma model to estimate transaction value.
  3. CLV Calculation: Calculate CLV by combining the model outputs.
  4. Analysis: Use the results to inform targeted marketing strategies.

Model Overview

BG/NBD Model

Predicts the frequency of future purchases based on historical transaction data.

Gamma-Gamma Model

Estimates the average transaction value, providing a comprehensive view of customer value when combined with the BG/NBD model.

Results

By using these models, businesses can:

  • Identify customers with high potential value.
  • Predict future purchasing patterns.
  • Enhance marketing strategies based on customer value predictions.

Contributing

Contributions are welcome! Feel free to fork the repository and submit a pull request. For significant changes, please open an issue to discuss your ideas.