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Improving Customer Acquisition Models by Incorporating Spatial Autocorrelation at Different Levels of Granularity

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  • P. BAECKE
  • D. VAN DEN POEL

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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 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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    File URL: http://www.feb.ugent.be/nl/Ondz/wp/Papers/wp_12_819.pdf
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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 12/819.

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    Length: 25 pages
    Date of creation: Oct 2012
    Date of revision:
    Handle: RePEc:rug:rugwps:12/819

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    Related research

    Keywords: Customer Relationship Management (CRM); Predictive Analytics; Customer Intelligence; Marketing; Data Augmentation; Autoregressive Model; Automobile Industry;

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    References

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    1. Puneet Manchanda & Ying Xie & Nara Youn, 2008. "The Role of Targeted Communication and Contagion in Product Adoption," Marketing Science, INFORMS, vol. 27(6), pages 961-976, 11-12.
    2. Thomas J. Steenburgh & Andrew Ainslie & Peder Hans Engebretson, 2003. "Massively Categorical Variables: Revealing the Information in Zip Codes," Marketing Science, INFORMS, vol. 22(1), pages 40-57, August.
    3. 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.
    4. Wesley Hartmann & Puneet Manchanda & Harikesh Nair & Matthew Bothner & Peter Dodds & David Godes & Kartik Hosanagar & Catherine Tucker, 2008. "Modeling social interactions: Identification, empirical methods and policy implications," Marketing Letters, Springer, vol. 19(3), pages 287-304, December.
    5. 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.
    6. 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.
    7. 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.
    8. Rod McCrea, 2009. "Explaining sociospatial patterns in South East Queensland, Australia: social homophily versus structural homophily," Environment and Planning A, Pion Ltd, London, vol. 41(9), pages 2201-2214, September.
    9. Mark Grinblatt & Matti Keloharju & Seppo Ikäheimo, 2008. "Social Influence and Consumption: Evidence from the Automobile Purchases of Neighbors," The Review of Economics and Statistics, MIT Press, vol. 90(4), pages 735-753, November.
    10. P. Baecke & D. Van Den Poel, 2012. "Including Spatial Interdependence in Customer Acquisition Models: a Cross-Category Comparison," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/788, Ghent University, Faculty of Economics and Business Administration.
    11. Philippe Baecke & Dirk Van Den Poel, 2010. "Improving Purchasing Behavior Predictions By Data Augmentation With Situational Variables," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 9(06), pages 853-872.
    12. Sushil Bikhchandani & David Hirshleifer & Ivo Welch, 2010. "A theory of Fads, Fashion, Custom and cultural change as informational Cascades," Levine's Working Paper Archive 1193, David K. Levine.
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