The ensemble of neural network and gradient boosting for the prediction of customer profitability: A two-stage modeling approach

Abstract

This paper illustrates a two-stage approach for predicting customer profitability. The first stage is to build a dichotomous model to predict the customer’s likelihood of future purchase. The second stage is to build a model, with continuous target variable, to predict the conditional future profit generated by the customer given he would make a purchase. Both stages involve the utilization of the gradient boosting and neural network data-mining techniques. In each stage, various ensemble combinations are tried and the one resulting in the lowest validation average squared error is chosen to be the stage model winner. The two model winners are subsequently used jointly for the prediction of future profit. In this analysis, Base SAS ${}^{\mathrm{\circledR }}$ is used for data manipulation and SAS ${}^{\mathrm{\circledR }}$ Enterprise Miner™ 13.2 is used for predictive modeling. It is evident that this two-stage modeling approach is robust in predicting customer profitability. Managerial and research implications will be highlighted.