Abstract

This research investigates the efficacy of machine learning algorithms in predicting Customer Lifetime Value (CLV) within the dynamic retail landscape. Accurate CLV prediction enables targeted marketing strategies, optimized resource allocation, and enhanced customer relationship management. We compare the performance of several machine learning models, including Linear Regression, Support Vector Regression (SVR), Random Forest Regression, and Gradient Boosting Regression, using a comprehensive dataset of customer transactions and demographic information from a large retail chain. The study incorporates feature engineering techniques to improve model accuracy and addresses potential biases in the data and algorithms. Furthermore, we analyze the impact of various evaluation metrics, such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared, on model selection. The findings provide valuable insights for retail practitioners seeking to leverage machine learning for CLV prediction and inform future research directions in this area. This study contributes to the growing body of knowledge on algorithmic marketing and emphasizes the importance of responsible and ethical implementation of predictive models in business.