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Trends in Applied Sciences Research
  Year: 2010 | Volume: 5 | Issue: 2 | Page No.: 107-119
DOI: 10.3923/tasr.2010.107.119
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Using Process Mining for Customer Retention

N. Abedzadeh, N. Nematbakhsh and M.A. Nematbakhsh

Recognizing churn customers and provoke them to stay in our store, is a way for customer churn management. This study does so not only through predicting churn probability but also by proposing a method to keep them. The historical information of customers and CLV, are used to form a churn model. We use the attributes identified in the churn model to divide all churners into distinct groups. Then, a churn predictor uses the churn model to predict the churn probability of a given customer. When the churn model finds that the customer has a churn probability, process mining is used to suggest specific retention policies to keep them. The Refah departments store dataset is used to analyze this method. This dataset consists of customer's data and one year sells of Refah department stores. This study’s experiments show that our method has high evaluation accuracy as compared with other methods.
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How to cite this article:

N. Abedzadeh, N. Nematbakhsh and M.A. Nematbakhsh, 2010. Using Process Mining for Customer Retention. Trends in Applied Sciences Research, 5: 107-119.

DOI: 10.3923/tasr.2010.107.119






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