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

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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.

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Acknowledgments

The funding of this research through the scholarship SFRH/BD/60970/2009 from the Portuguese Foundation of Science and Technology (FCT) is gratefully acknowledged by the first author.

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Authors and Affiliations

  1. Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 , Oporto, Portugal

    Vera L. Miguéis, Ana S. Camanho & João Falcão e Cunha

  2. Faculty of Economics and Business Administration, Ghent University, Tweekerkenstraat 2, 9000 , Ghent, Belgium

    Dirk Van den Poel

Authors
  1. Vera L. Miguéis
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  2. Dirk Van den Poel
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  3. Ana S. Camanho
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  4. João Falcão e Cunha
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Corresponding author

Correspondence to Vera L. Miguéis.

Appendix

Appendix

See Tables 2 and 3.

Table 2 Non-churners transition matrix
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Table 3 Churners transition matrix
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Miguéis, V.L., Van den Poel, D., Camanho, A.S. et al. Predicting partial customer churn using Markov for discrimination for modeling first purchase sequences. Adv Data Anal Classif 6, 337–353 (2012). https://doi.org/10.1007/s11634-012-0121-3

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  • DOI: https://doi.org/10.1007/s11634-012-0121-3

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