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Incorporating Neighborhood Effects in Customer Relationship Management Models

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Foundations of Intelligent Systems (ISMIS 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6804))

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Abstract

Traditional customer relationship management (CRM) models often ignore the correlation that could exist in the purchase behavior of neighboring customers. Instead of treating this correlation as nuisance in the error term, a generalized linear autologistic regression can be used to take these neighborhood effects into account and improve the predictive performance of a customer identification model for a Japanese automobile brand. In addition, this study shows that the level on which neighborhoods are composed has an important influence on the extra value that results from the incorporation of spatial autocorrelation.

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Author information

Authors and Affiliations

  1. Faculty of Economics and Business Administration, Department of Marketing, Ghent University, Tweekerkenstraat 2, B-9000, Ghent, Belgium

    Philippe Baecke & Dirk Van den Poel

Authors
  1. Philippe Baecke
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  2. Dirk Van den Poel
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Editor information

Editors and Affiliations

  1. Faculty of Electronics and Information Technology, Institute of Computer Science, Warsaw University of Technology,, Nowowiejska 15/19, 00-665, Warsaw, Poland

    Marzena Kryszkiewicz

  2. Faculty of Electronics and Information Technology, Institute of Computer Science, Warsaw University of Technology, Nowowiejska 15/19, 00-665, Warsaw, Poland

    Henryk Rybinski

  3. University of Warsaw, 02-097, Warsaw, Poland

    Andrzej Skowron

  4. Faculty of Electronics and Information Technology, Institute of Computer Science, Warsaw University of Technology, Nowowiejska 15/19,, 00-665, Warsaw, Poland

    Zbigniew W. Raś

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© 2011 Springer-Verlag Berlin Heidelberg

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Baecke, P., Van den Poel, D. (2011). Incorporating Neighborhood Effects in Customer Relationship Management Models. In: Kryszkiewicz, M., Rybinski, H., Skowron, A., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2011. Lecture Notes in Computer Science(), vol 6804. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21916-0_10

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  • DOI: https://doi.org/10.1007/978-3-642-21916-0_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21915-3

  • Online ISBN: 978-3-642-21916-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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