Next-product-to-buy models for cross-selling applications

Abstract We present and evaluate next-product-to-buy (NPTB) models for improving the effectiveness of cross-selling. The NPTB model reduces the waste of poorly targeted cross-selling activities by predicting the product each customer would be most likely to buy next. We describe the model-building process and discuss theoretical and practical issues in developing a NPTB model. We then illustrate the effectiveness of the NPTB approach with a field test. The field test shows that the NPTB model increases profits compared to a heuristic approach, and that profits are incremental over and above sales that would have occurred through other channels. We then conduct an empirical test of methodological issues. We find that incorporating current product ownership as a predictor enhances predictive accuracy the most, followed by customer monetary value to the company, and demographics. We find that statistical method makes little difference in predictive accuracy, with neural nets having a slight edge. A simple random sample to create the calibration database increases predictive accuracy more than a stratified random sample, although the stratified sample may be preferred to avoid underpredicting unpopular products. We explore the potential for incorporating purchase incidence models in the NPTB approach, and find that this potentially enhances the effectiveness of the NPTB model. We close with recommendations for practitioners and for future academic research.