Abstract
AbstractTraditional 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.KeywordsCustomer IntelligencePredictive AnalyticsMarketingData AugmentationAutoregressive ModelAutomobile Industry
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