Customer Lifetime Value Prediction – Retail

This project focuses on predicting Customer Lifetime Value (CLV) for a retail company, a critical metric that estimates the total revenue a customer is expected to generate over their relationship with the business. By analyzing historical transaction data, the project aims to identify customer behavior patterns, segment customers based on their value, and enable data-driven decisions for marketing, promotions, and customer retention strategies. The goal is to enhance customer relationship management, improve retention rates, and drive long-term revenue growth in the retail industry.

Importance of CLV

Customer Lifetime Value (CLV) is a vital indicator of a customer’s loyalty and profitability. It accounts for factors such as purchase frequency, average order value, and retention rates. A higher CLV signifies a loyal customer who is likely to make repeat purchases and recommend the company to others, thereby increasing profitability. By accurately predicting CLV, retailers can:

• Tailor marketing strategies to target high-value customers.

• Optimize promotions and discounts to maximize profitability.

• Improve customer retention through personalized engagement.

Project Objectives

The primary objective of this project is to develop a predictive model that estimates CLV for retail customers. The model leverages historical transaction data to:

• Predict future revenue potential for individual customers.

• Segment customers into high, mid, and low-value groups.

• Support data-driven decisions to enhance marketing efficiency and customer retention.

Methodology

Several machine learning models were evaluated for CLV, including:

• Linear Regression

• Decision Trees

• XGBoost

The models were assessed using metrics such as Mean Squared Error (MSE), R-squared, and accuracy. The XGBoost model outperformed others, achieving an accuracy of 94% on the test set. It demonstrated strong performance in predicting CLV across high, mid, and low-value customer segments, as evidenced by precision, recall, and F1-score metrics in the classification report.

Impact and Applications

This project has the potential to revolutionize how retailers approach customer relationship management. By accurately predicting CLV, companies can:

• Allocate resources effectively to acquire and retain high-value customers.

• Identify and address low-value customers to improve overall profitability.

• Drive sustainable growth by focusing on long-term customer engagement.

This project aligns with the broader goal of utilizing data science to address real-world problems in the retail industry, providing actionable insights to enhance business strategies and customer experiences.