Predicting Customer Lifetime Value in Retail

Introduction

Predicting Customer Lifetime Value (CLV) is crucial for retailers aiming to optimize marketing strategies and improve customer relationship management. This project proposal outlines a predictive modeling approach to estimate CLV, drawing inspiration from recent research advancements in the field.

Background

Customer Lifetime Value is a key metric that represents the total revenue a business can expect from a customer throughout their relationship. Accurate CLV predictions enable retailers to make informed decisions regarding customer acquisition, retention, and segmentation. Recent research has highlighted the effectiveness of machine learning techniques in enhancing the accuracy of CLV predictions by leveraging historical transaction data and customer behavior analytics.

Project Objective

The primary objective of this project is to develop a predictive model for CLV in the retail industry. The model will utilize advanced machine learning algorithms to analyze customer purchase history and other relevant data, aiming to provide actionable insights for strategic decision-making.

Methodology

1. Data Collection and Preprocessing

• Datasets: Utilize publicly available retail datasets such as the Online Retail II dataset from the UCI Machine Learning Repository.
• Data Cleaning: Process data to handle missing values, outliers, and ensure consistency across different data sources.
• Feature Engineering: Extract features such as purchase frequency, average order value, recency of purchase, and customer demographics.

2. Model Development

• Algorithm Selection: Experiment with various machine learning algorithms including regression models, decision trees, and ensemble methods like Random Forest and Gradient Boosting.
• Model Training: Split data into training and testing sets to evaluate model performance using cross-validation techniques.

3. Evaluation and Optimization

• Evaluation Metrics: Use metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared to assess model accuracy.
• Hyperparameter Tuning: Optimize model parameters using grid search or random search methods to enhance prediction accuracy.

Expected Outcomes

The proposed predictive model is expected to provide accurate CLV estimates that will help retailers tailor their marketing efforts and resource allocation more effectively. By leveraging machine learning techniques, the model should offer a significant improvement over traditional CLV estimation methods.

Conclusion

This project aims to advance retail analytics by developing a robust predictive model for Customer Lifetime Value. The integration of machine learning algorithms is anticipated to yield valuable insights that can drive strategic business decisions in the retail sector.

For further details on related research, please refer to the paper "Predicting Customer Lifetime Value in Retail," available at sciencedirect.com/science/article/pii/S1877050920316793.

Dataset link: UCI Machine Learning Repository - Online Retail II