The Algorithmic Augmentation of Customer Lifetime Value Prediction: A Comparative Analysis of Machine Learning Models in the Retail Sector

Abstract

This study examines the effectiveness of machine learning models in predicting Customer Lifetime Value (CLV) in the dynamic retail environment. Precise CLV prediction facilitates targeted marketing, resource optimization, and improved customer relationship management. We evaluate the performance of various machine learning models, such as Linear Regression, Support Vector Regression (SVR), Random Forest Regression, and Gradient Boosting Regression, on a rich dataset of customer transactions and demographic data from a big retail chain. The research employs feature engineering methods to enhance model performance and mitigates possible biases in the data and algorithms. In addition, we examine the effect of different evaluation metrics, such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared, on model choice. The results offer useful insights for retail practitioners who want to apply machine learning for CLV prediction and guide future research in this field. This research adds to the expanding literature on algorithmic marketing and highlights the need for responsible and ethical use of predictive models in business.