Improving customer acquisition models by incorporating spatial autocorrelation at different levels of granularity
Baecke, Philippe Van den Poel, Dirk
Baecke, Philippe
Van den Poel, Dirk
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Publication Type
Journal article with impact factor
Editor
Supervisor
Publication Year
2013
Journal
Journal of Intelligent Information Systems
Book
Publication Volume
41
Publication Issue
1
Publication Begin page
73
Publication End page
90
Publication Number of pages
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Abstract
Traditional CRM models often ignore the correlation that could exist among the purchasing behavior of surrounding prospects. Hence, a generalized linear autologistic regression model can be used to capture this interdependence and improve the predictive performance of the model. In particular, customer acquisition models can benefit from this. These models often suffer from a lack of data quality due to the limited amount of information available about potential new customers. Based on a customer acquisition model of a Japanese automobile brand, this study shows that the extra value resulting from incorporating neighborhood effects can vary significantly depending on the granularity level on which the neighborhoods are composed. A model based on a granularity level that is too coarse or too fine will incorporate too much or too little interdependence resulting in a less than optimal predictive improvement. Since neighborhood effects can have several sources (i.e. social influence, homophily and exogeneous shocks), this study suggests that the autocorrelation can be divided into several parts, each optimally measured at a different level of granularity. Therefore, a model is introduced that simultaneously incorporates multiple levels of granularity resulting in even more accurate predictions. Further, the effect of the sample size is examined. This shows that including spatial interdependence using finer levels of granularity is only useful when enough data is available to construct stable spatial lag effects. As a result, extending a spatial model with multiple granularity levels becomes increasingly valuable when the data sample becomes larger.
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Keywords
Customer Relationship Management (CRM), Predictive Analytics, Customer Intelligence, Marketing, Data Augmentation, Autoregressive Model, Automobile Industry