Predicting Customer Lifetime Value Using Machine Learning

Introduction

Predicting Customer Lifetime Value (CLV) is crucial for businesses aiming to optimize marketing strategies, allocate resources efficiently, and enhance customer relationship management. This project proposal explores the development of a machine learning model to accurately predict CLV, leveraging recent advancements in predictive modeling and data analytics.

Background

Recent research has demonstrated that machine learning techniques can significantly improve the accuracy of CLV predictions. Traditional methods often rely on historical data and simple statistical models, which may not fully capture the complexities of customer behavior. Machine learning models, on the other hand, can process large volumes of data and identify intricate patterns that influence CLV.

Project Objective

The primary objective of this project is to develop a robust machine learning model capable of predicting CLV with high accuracy. The model will utilize diverse customer data points, including transaction history, demographic information, and engagement metrics, to provide actionable insights for business decision-making.

Methodology

1. Data Collection and Preprocessing

• Datasets: Utilize publicly available datasets such as the Online Retail dataset from the UCI Machine Learning Repository for model training and evaluation.
• Data Cleaning: Handle missing values, outliers, and data inconsistencies to ensure data quality.
• Feature Engineering: Extract relevant features such as purchase frequency, average order value, and customer segmentation.

2. Model Development

• Algorithm Selection: Evaluate various machine learning algorithms including regression models, decision trees, and ensemble methods like Random Forests and Gradient Boosting Machines.
• Feature Selection: Use techniques like Recursive Feature Elimination (RFE) to identify the most predictive features.
• Model Training: Split the dataset into training and testing subsets to train the model using cross-validation techniques.

3. Evaluation and Optimization

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

Expected Outcomes

The proposed machine learning model is expected to deliver more accurate CLV predictions compared to traditional statistical approaches. By leveraging advanced algorithms and comprehensive datasets, businesses can gain deeper insights into customer value trajectories and tailor their strategies accordingly.

Conclusion

This project aims to advance the field of customer analytics by developing a state-of-the-art machine learning model for CLV prediction. The integration of sophisticated algorithms and extensive data analysis is anticipated to provide significant improvements in predictive accuracy.

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

Dataset link: UCI Machine Learning Repository - Online Retail Dataset