Predicting partial customer churn using Markov for discrimination for modeling first purchase sequences |
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Authors: | Vera L Miguéis Dirk Van den Poel Ana S Camanho Jo?o Falc?o e Cunha |
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Institution: | 1. Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, 4200-465, Oporto, Portugal 2. Faculty of Economics and Business Administration, Ghent University, Tweekerkenstraat 2, 9000, Ghent, Belgium
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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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