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Predicting Partial Customer Churn Using Markov for Discrimination for Modeling First Purchase Sequences

V. L. Miguéis (), Dirk Van den Poel (), A.S. Camanho and Joao Falcao E Cunha
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Joao Falcao E Cunha: -

Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium from Ghent University, Faculty of Economics and Business Administration

Abstract: Currently, in order to remain competitive companies are adopting customer centered strategies and consequently customer relationship management (CRM) is gaining increasing importance. In this context, customer retention deserves particular attention. This paper proposes a model for partial churn detection in the retail grocery sector that includes as a predictor the similarity of the products' first purchase sequence with churner and non-churner sequences. The sequence of first purchase events is modeled using Markov for discrimination. Two classification techniques are used in the empirical study: logistic regression and random forests. A real sample of approximately 95.000 new customers is analyzed taken from the data warehouse of a European retailing company. The empirical results reveal the relevance of the inclusion of a products' sequence likelihood in partial churn prediction models, as well as the supremacy of logistic regression when compared with random forests.

Keywords: Credit Scoring; Quantile regression; Classification; Bayesian estimation; Markov Chain Monte Carlo Customer relationship management; Churn analysis; Retailing; Classification; Logistic regression; Random forests (search for similar items in EconPapers)
Pages: 19 pages
Date: 2012-08
New Economics Papers: this item is included in nep-mkt
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Journal Article: Predicting partial customer churn using Markov for discrimination for modeling first purchase sequences (2012) Downloads
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