This project implements a Customer Lifetime Value (CLV) prediction model using Python, leveraging advanced statistical methods and machine learning techniques.
Project Overview:
The CLV prediction model aims to estimate the total value a customer will bring to a business over their entire relationship. This project utilizes historical customer data to predict future purchasing behavior and monetary value.
Key Features
- Data preprocessing and feature engineering
- Implementation of Gamma-Gamma and Beta-Geometric/NBD models
- Random Forest Regressor for CLV prediction
- Feature importance analysis
- Data visualization for customer segmentation and CLV distribution
Technologies Used
- Python 3.x
- pandas
- numpy
- scikit-learn
- matplotlib
- seaborn
Model Components
- Data Preprocessing: Handling missing values and feature engineering
- Gamma-Gamma Model: Estimating customer monetary value
- Beta-Geometric/NBD Model: Predicting customer purchase behavior
- Random Forest Regressor: Predicting overall Customer Lifetime Value
- Feature Importance Analysis: Identifying key factors influencing CLV
Results
The model successfully predicts Customer Lifetime Value, enabling:
- Targeted marketing strategies
- Improved customer retention efforts
- Efficient allocation of marketing resources
- Personalized customer engagement
Future Improvements
- Incorporate additional data sources for more accurate predictions
- Experiment with other machine learning algorithms for comparison
- Develop a web application for easy CLV prediction by non-technical users