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Miguéis, Vera L.; Van den Poel, Dirk; Camanho, Ana S.; Falcão e Cunha, João

Predicting partial customer churn using Markov for discrimination for modeling first purchase sequences. (English) Zbl 1282.62249

Adv. Data Anal. Classif., ADAC 6, No. 4, 337-353 (2012).
Summary: 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.

Cited in 3 Documents

MSC:

62P20 Applications of statistics to economics

Keywords:

customer relationship management; churn analysis; retailing

Software:

C4.5
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\textit{V. L. Miguéis} et al., Adv. Data Anal. Classif., ADAC 6, No. 4, 337--353 (2012; Zbl 1282.62249)
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