Including spatial interdependence in customer acquisition models: a cross-category comparison
Baecke, Philippe Van den Poel, Dirk
Baecke, Philippe
Van den Poel, Dirk
Citations
Altmetric:
Publication Type
Journal article with impact factor
Editor
Supervisor
Publication Year
2012
Journal
Expert Systems with Applications
Book
Publication Volume
39
Publication Issue
15
Publication Begin page
12105
Publication End page
12133
Publication Number of pages
Collections
Abstract
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 an autoregressive and hierarchical technique that both are able to incorporate spatial information in a model that can be applied on large datasets, which is typical for CRM. Predictive performances of these models are compared in an application that identifies potential new customers for 25 products and brands. The results show that when a discrete spatial variable is used to group customers into mutually exclusive neighborhoods, a multilevel model performs at least as well as, and for a large number of durable goods even significantly better than a frequently used autologistic model. Further, this application provides interesting insights for marketing decision makers. It indicates that especially for publicly consumed durable goods neighborhood effects can be identified. However, for more exclusive brands, incorporating spatial information will not always result in major predictive improvements. For these luxury products, the high spatial interdependence is mainly caused by homophily in which the spatial variable is a substitute for absent socio-demographic and lifestyle variables. As a result, these neighborhood variables lose a lot of predictive value on top of a traditional acquisition model that typically is based on such non-transactional variables.
Research Projects
Organizational Units
Journal Issue
Keywords
Marketing & Sales