https://github.com/1401dev/customer-lifetime-value-prediction

A data science project leveraging Python and Scikit-Learn to build predictive models that estimate customer lifetime value (CLV). Includes data cleaning, feature engineering, and model selection to identify key drivers of CLV, supporting strategic decision-making in customer retention and marketing.
https://github.com/1401dev/customer-lifetime-value-prediction

clv clv-analysis customer-retention data-analysis dataprocessing feature-engineering machine-learning marketing-analytics predictive-modeling python regression-analysis scikit-learn

Last synced: 6 days ago
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A data science project leveraging Python and Scikit-Learn to build predictive models that estimate customer lifetime value (CLV). Includes data cleaning, feature engineering, and model selection to identify key drivers of CLV, supporting strategic decision-making in customer retention and marketing.

• Host: GitHub
• URL: https://github.com/1401dev/customer-lifetime-value-prediction
• Owner: 1401Dev
• Created: 2024-03-27T22:32:38.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-10-22T15:10:18.000Z (3 months ago)
• Last Synced: 2024-11-11T19:13:47.125Z (2 months ago)
• Topics: clv, clv-analysis, customer-retention, data-analysis, dataprocessing, feature-engineering, machine-learning, marketing-analytics, predictive-modeling, python, regression-analysis, scikit-learn
• Language: Jupyter Notebook
• Homepage:
• Size: 173 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Customer Lifetime Value (CLV) Prediction Model

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**




1. Data Preprocessing: Handling missing values and feature engineering

2. Gamma-Gamma Model: Estimating customer monetary value

3. Beta-Geometric/NBD Model: Predicting customer purchase behavior

4. Random Forest Regressor: Predicting overall Customer Lifetime Value

5. 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