Customer Lifetime Value Prediction & Segmentation

This project analyzes e-commerce transaction data to understand customer behavior and predict future customer value using machine learning techniques.

The project combines RFM analysis, customer segmentation, and predictive modeling to help businesses identify high-value customers and design targeted marketing strategies.

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Project Objectives

• Analyze customer purchasing behavior
• Segment customers using RFM analysis and K-Means clustering
• Predict future customer revenue using machine learning models
• Provide business insights for customer retention and marketing strategies

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Dataset

Dataset used: Online Retail Dataset

The dataset contains transactional records of an online retail store including:

• Invoice Number
• Stock Code
• Product Description
• Quantity
• Invoice Date
• Unit Price
• Customer ID
• Country

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Project Workflow

1. Exploratory Data Analysis

Understanding dataset structure, missing values, and purchase patterns.

2. Data Preprocessing

Cleaning invalid transactions and preparing the dataset for analysis.

3. RFM Feature Engineering

Calculating customer behavior metrics:

Recency – Days since last purchase
Frequency – Number of transactions
Monetary – Total spending amount

4. Customer Segmentation

Customers are grouped into segments using K-Means clustering based on RFM features.

5. CLV Estimation

Customer Lifetime Value is estimated using behavioral metrics.

6. Future CLV Prediction

Machine learning models are trained to predict future customer revenue.

Models used:

• Linear Regression
• Random Forest
• Gradient Boosting
• XGBoost

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Key Findings

Feature importance analysis shows the following ranking:

Monetary > Recency > Frequency

Interpretation:

• Customers who historically spend more tend to generate higher revenue in the future.
• Recently active customers have a higher probability of making new purchases.
• Purchase frequency contributes to predictions but is less influential compared to spending behavior.

These insights help businesses focus on high-value and recently active customers for targeted marketing campaigns.

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Technologies Used

• Python
• Pandas
• NumPy
• Scikit-Learn
• XGBoost
• Matplotlib
• Jupyter Notebook
• Power BI

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Project Structure

customer-lifetime-value-prediction/
│
├── data/
│   ├── raw/
│   │   └── Online Retail.csv
│   │  
│   ├── processed/
│   │   ├── processed_online_retail.csv  
│   │   ├── rfm_data.csv
│   │   ├── rfm_with_clv.csv
│
├── notebooks/
│   ├── 01_eda.ipynb
│   ├── 02_data_preprocess.ipynb
│   ├── 03_rfm_feature_engineering.ipynb
│   ├── 04_customer_segmentation_kmeans.ipynb
│   ├── 05_clv_estimation.ipynb
│   ├── 06_future_clv_prediction.ipynb
│   ├── 07_business_insights_strategy.ipynb
│
├── src/
│   ├── rfm.py
│   ├── clustering.py
│
├── dashboards/
│   └── clv_powerbi_dashboard.pbix
│
├── results/
│   ├── customer_distribution_segment.png
│   ├── feature_importance.png
│   ├── model_comparison_results.csv
│   ├── future_revenue_distribution.png
│   ├── revenue_by_segment.png
│
├── README.md
└── requirements.txt

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Dashboard

A Power BI dashboard is included to visualize customer segments and revenue insights.

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Future Improvements

• Improve prediction accuracy with additional behavioral features
• Deploy model as a web application
• Build automated customer segmentation pipeline

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