The Value of Neighborhood Information in Prospect Selection Models: Investigating the Optimal Level of Granularity

Within analytical customer relationship management (CRM), customer acquisition models suffer the most from a lack of data quality because the information of potential customers is mostly limited to socio-demographic and lifestyle variables obtained from external data vendors. Particularly in this situation, taking advantage of the spatial correlation between customers can improve the predictive performance of these models. This study compares the predictive performance of an autoregressive and hierarchical technique in an application that identifies potential new customers for 25 products and brands. In addition, this study shows that the predictive improvement can vary significantly depending on the granularity level on which the neighborhoods are composed. Therefore, a model is introduced that simultaneously incorporates multiple levels of granularity resulting in even more accurate predictions.