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.
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.
- Data Preparation: Load and preprocess your customer transaction data.
- Modeling: Apply the BG/NBD model to predict purchase frequency and the Gamma-Gamma model to estimate transaction value.
- CLV Calculation: Calculate CLV by combining the model outputs.
- Analysis: Use the results to inform targeted marketing strategies.
Predicts the frequency of future purchases based on historical transaction data.
Estimates the average transaction value, providing a comprehensive view of customer value when combined with the BG/NBD model.
By using these models, businesses can:
- Identify customers with high potential value.
- Predict future purchasing patterns.
- Enhance marketing strategies based on customer value predictions.
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.