A meta-learning based stacked regression approach for customer lifetime value prediction

Abstract

Companies across the globe are keen on targeting potential high-value customers in an attempt to expand revenue, and this could be achieved only by understanding the customers more. Customer lifetime value (CLV) is the total monetary value of transactions or purchases made by a customer with the business over an intended period of time and is used as a means to estimate future customer interactions. CLV finds application in a number of distinct business domains, such as banking, insurance, online entertainment, gaming, and e-commerce. The existing distribution-based and basic (recency, frequency, and monetary)-based models face limitations in terms of handling a wide variety of input features. Moreover, the more advanced deep learning approaches could be superfluous and add an undesirable element of complexity in certain application areas. We, therefore, propose a system that is able to qualify as both effective and comprehensive, yet simple and interpretable. With that in mind, we develop a meta-learning-based stacked regression model that combines the predictions from bagging and boosting models that are found to perform well individually. Empirical tests have been carried out on an openly available online retail dataset to evaluate various models and show the efficacy of the proposed approach.

Keywords

• Customer lifetime value
• Gradient boosting machines
• Extreme gradient boosting