Improving Customer Acquisition Models by Incorporating Spatial Autocorrelation at Different Levels of Granularity
AbstractTraditional 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 showed that including spatial interdependence using finer levels of granularity is only useful when enough data is available to construct reliable 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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Bibliographic InfoPaper provided by Ghent University, Faculty of Economics and Business Administration in its series Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium with number 12/819.
Length: 25 pages
Date of creation: Oct 2012
Date of revision:
Customer Relationship Management (CRM); Predictive Analytics; Customer Intelligence; Marketing; Data Augmentation; Autoregressive Model; Automobile Industry;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-02-03 (All new papers)
- NEP-FOR-2013-02-03 (Forecasting)
- NEP-GEO-2013-02-03 (Economic Geography)
- NEP-MKT-2013-02-03 (Marketing)
- NEP-URE-2013-02-03 (Urban & Real Estate Economics)
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