Empirical validation and comparison of models for customer base analysis

Abstract

The benefits of retaining customers lead companies to search for means to profile their customers individually and track their retention and defection behaviors. To this end, the main issues addressed in customer base analysis are identification of customer active/inactive status and prediction of future purchase levels. We compare the predictive performance of Pareto/NBD and BG/NBD models from the customer base analysis literature — in terms of repeat purchase levels and active status — using grocery retail transaction data. We also modify the BG/NBD model to incorporate zero repeat purchasers. All models capture the main characteristics of the purchase and dropout process of individual customers and produce similar forecasts. There are some deviations in the cumulative purchase estimates of the models, which may be due to the characteristics of grocery purchasing.