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Incorporating neighborhood effects in customer relationship management models

Philippe Baecke (UGent) and Dirk Van den Poel (UGent)
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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.
Keywords
AUTOLOGISTIC MODEL, SPATIAL MODELS, VARIABLES, BEHAVIOR, Customer Intelligence, Predictive Analytics, Marketing, Data Augmentation, Autoregressive Model, Automobile Industry

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MLA
Baecke, Philippe, and Dirk Van den Poel. “Incorporating Neighborhood Effects in Customer Relationship Management Models.” Lecture Notes in Computer Science. Ed. Marzena Kryszkiewicz et al. Vol. 6804. Heidelberg, Germany: Springer Verlag, 2011. 90–95. Print.
APA
Baecke, P., & Van den Poel, D. (2011). Incorporating neighborhood effects in customer relationship management models. In M. Kryszkiewicz, H. Rybinski, A. Skowron, & Z. W. Raś (Eds.), LECTURE NOTES IN COMPUTER SCIENCE (Vol. 6804, pp. 90–95). Presented at the 19th International Symposium on Methodologies for Intelligent Systems (ISMIS), Heidelberg, Germany: Springer Verlag.
Chicago author-date
Baecke, Philippe, and Dirk Van den Poel. 2011. “Incorporating Neighborhood Effects in Customer Relationship Management Models.” In Lecture Notes in Computer Science, ed. Marzena Kryszkiewicz, Henryk Rybinski, Andrzej Skowron, and Zbigniew W Raś, 6804:90–95. Heidelberg, Germany: Springer Verlag.
Chicago author-date (all authors)
Baecke, Philippe, and Dirk Van den Poel. 2011. “Incorporating Neighborhood Effects in Customer Relationship Management Models.” In Lecture Notes in Computer Science, ed. Marzena Kryszkiewicz, Henryk Rybinski, Andrzej Skowron, and Zbigniew W Raś, 6804:90–95. Heidelberg, Germany: Springer Verlag.
Vancouver
1.
Baecke P, Van den Poel D. Incorporating neighborhood effects in customer relationship management models. In: Kryszkiewicz M, Rybinski H, Skowron A, Raś ZW, editors. LECTURE NOTES IN COMPUTER SCIENCE. Heidelberg, Germany: Springer Verlag; 2011. p. 90–5.
IEEE
[1]
P. Baecke and D. Van den Poel, “Incorporating neighborhood effects in customer relationship management models,” in LECTURE NOTES IN COMPUTER SCIENCE, Warsaw, Poland, 2011, vol. 6804, pp. 90–95.
@inproceedings{2039055,
  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.},
  author       = {Baecke, Philippe and Van den Poel, Dirk},
  booktitle    = {LECTURE NOTES IN COMPUTER SCIENCE},
  editor       = {Kryszkiewicz, Marzena and Rybinski, Henryk and Skowron, Andrzej and Raś, Zbigniew W},
  isbn         = {9783642219153},
  issn         = {0302-9743},
  keywords     = {AUTOLOGISTIC MODEL,SPATIAL MODELS,VARIABLES,BEHAVIOR,Customer Intelligence,Predictive Analytics,Marketing,Data Augmentation,Autoregressive Model,Automobile Industry},
  language     = {eng},
  location     = {Warsaw, Poland},
  pages        = {90--95},
  publisher    = {Springer Verlag},
  title        = {Incorporating neighborhood effects in customer relationship management models},
  url          = {http://dx.doi.org/10.1007/978-3-642-21916-0_10},
  volume       = {6804},
  year         = {2011},
}

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