A Meta-learning based Stacked Regression Approach for Customer Lifetime Value Prediction

TL;DR

This paper introduces a meta-learning stacked regression method for predicting Customer Lifetime Value, combining multiple models to improve accuracy while maintaining simplicity and interpretability, tested on real retail data.

Contribution

The paper presents a novel meta-learning based stacked regression approach that effectively integrates bagging and boosting models for CLV prediction, addressing limitations of existing models.

Findings

The proposed model outperforms traditional CLV prediction methods.

Empirical results demonstrate improved accuracy on online retail data.

The approach balances effectiveness with interpretability.

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/purchases made by a customer with the business over an intended period of time and is used as 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 & monetary) based models face a limitation 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 which is able to qualify both as effective, and comprehensive yet simple and interpretable. With that in mind, we develop a meta-learning-based stacked regression model which combines the predictions from bagging and boosting models that each is 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.