An analysis of customer profitability using gradient boosting and neural network

What is it about?

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 is used for data manipulation and SAS 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.

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