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Including Spatial Interdependence in Customer Acquisition Models: a Cross-Category Comparison

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Author Info

  • P. BAECKE
  • D. VAN DEN POEL

    ()

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 a large dataset, 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 more often 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. Though, for the 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.

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Bibliographic Info

Paper 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 12/788.

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Length: 32 pages
Date of creation: May 2012
Date of revision:
Handle: RePEc:rug:rugwps:12/788

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Related research

Keywords: Customer Intelligence • Data Mining • Autologistic Model • Multilevel Model • Neighborhood effects • Spatial Interdependence;

This paper has been announced in the following NEP Reports:

References

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  1. K. Coussement & D. F. Benoit & D. Van Den Poel, 2009. "Improved Marketing Decision Making in a Customer Churn Prediction Context Using Generalized Additive Models," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 09/603, Ghent University, Faculty of Economics and Business Administration.
  2. Anselin, Luc, 2002. "Under the hood : Issues in the specification and interpretation of spatial regression models," Agricultural Economics, Blackwell, vol. 27(3), pages 247-267, November.
  3. Bart J. Bronnenberg & Vijay Mahajan, 2001. "Unobserved Retailer Behavior in Multimarket Data: Joint Spatial Dependence in Market Shares and Promotion Variables," Marketing Science, INFORMS, vol. 20(3), pages 284-299, October.
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  5. Bearden, William O & Etzel, Michael J, 1982. " Reference Group Influence on Product and Brand Purchase Decisions," Journal of Consumer Research, University of Chicago Press, vol. 9(2), pages 183-94, September.
  6. P. Baecke & D. Van Den Poel, 2009. "Data Augmentation by Predicting Spending Pleasure Using Commercially Available External Data," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 09/596, Ghent University, Faculty of Economics and Business Administration.
  7. David Bell & Sangyoung Song, 2007. "Neighborhood effects and trial on the internet: Evidence from online grocery retailing," Quantitative Marketing and Economics, Springer, vol. 5(4), pages 361-400, December.
  8. Wagner Kamakura & Carl Mela & Asim Ansari & Anand Bodapati & Pete Fader & Raghuram Iyengar & Prasad Naik & Scott Neslin & Baohong Sun & Peter Verhoef & Michel Wedel & Ron Wilcox, 2005. "Choice Models and Customer Relationship Management," Marketing Letters, Springer, vol. 16(3), pages 279-291, December.
  9. Bronnenberg, B.J.J.A.M. & Mahajan, V., 2001. "Unobserved retailer behavior in multimarket data: Joint spatial dependence in market shares and promotion variables," Open Access publications from Tilburg University urn:nbn:nl:ui:12-332642, Tilburg University.
  10. Sangkil Moon & Gary J. Russell, 2008. "Predicting Product Purchase from Inferred Customer Similarity: An Autologistic Model Approach," Management Science, INFORMS, vol. 54(1), pages 71-82, January.
  11. P. Baecke & D. Van Den Poel, 2010. "Improving purchasing behavior predictions by data augmentation with situational variables," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 10/658, Ghent University, Faculty of Economics and Business Administration.
  12. Bradlow, E. & Bronnenberg, B.J.J.A.M. & Russell, G.J. & Arora, N. & Bell, D. & Deepak, S.D. & Hofstede, F. ter & Sismeiro, C. & Thomadsen, R. & Yang, S., 2005. "Spatial models in marketing," Open Access publications from Tilburg University urn:nbn:nl:ui:12-332173, Tilburg University.
  13. Kuenzel, Johanna & Musters, Pieter, 2007. "Social interaction and low involvement products," Journal of Business Research, Elsevier, vol. 60(8), pages 876-883, August.
  14. Eric Bradlow & Bart Bronnenberg & Gary Russell & Neeraj Arora & David Bell & Sri Duvvuri & Frankel Hofstede & Catarina Sismeiro & Raphael Thomadsen & Sha Yang, 2005. "Spatial Models in Marketing," Marketing Letters, Springer, vol. 16(3), pages 267-278, December.
  15. D. Thorleuchter & D. Van Den Poel & A. Prinzie, 2011. "Analyzing existing customers’ websites to improve the customer acquisition process as well as the profitability prediction in B-to-B marketing," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 11/733, Ghent University, Faculty of Economics and Business Administration.
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Cited by:
  1. P. Baecke & D. Van Den Poel, 2012. "Improving Customer Acquisition Models by Incorporating Spatial Autocorrelation at Different Levels of Granularity," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/819, Ghent University, Faculty of Economics and Business Administration.

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