Predicting Customer Lifetime Value Using Support Vector Machines (SVM) In Business

Abstract

In this study, the authors investigate applying Support Vector Machines (SVM) to predict Customer Lifetime Value (CLV) for improved customer segmentation and retention for businesses. The goal is to overcome the drawbacks of the traditional CLV prediction model, which does not consider non-linear and dynamic customer behavior. This study uses a dataset that has e-commerce transaction information, including some of the customers' attributes like their recency, frequency, monetary, and demographic data. The data was preprocessed using key techniques such as normalization, dealing with missing data, and feature engineering. The basic method applied is Support Vector Regression (SVR), which is appropriate for regression problems with non-linear, high dimensional and complex data. Evaluation of the model includes the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R²). The results achieve a superior accuracy, error metrics and R squared value, as compared to the traditional models like linear regression and random forest. In conclusion, according to the analysis, it can be established that the modeling of non-linear behavior of customers through SVR is an efficient method of forecasting CLV, which may assist companies in optimizing their marketing policies, customer targeting, and resource allocation. This research highlights the practical implications of applying advanced machine learning algorithms like SVR to decision-making processes in organizations.