Predicting partial customer churn using Markov for discrimination for modeling first purchase sequences

Vera Miguéis (), Dirk Van den Poel, Ana Camanho () and João Falcão e Cunha ()

Advances in Data Analysis and Classification, 2012, vol. 6, issue 4, 337-353

Abstract: Currently, in order to remain competitive companies are adopting customer centered strategies and consequently customer relationship management 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. Copyright Springer-Verlag Berlin Heidelberg 2012

Keywords: Customer relationship management; Churn analysis; Retailing; Classification; Logistic regression; Random forests; 91 (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (5)

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Working Paper: Predicting Partial Customer Churn Using Markov for Discrimination for Modeling First Purchase Sequences (2012)
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DOI: 10.1007/s11634-012-0121-3

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