Customer Churn in Mobile Markets: A Comparison of Techniques

What is it about?

Customer retention is an important issue for any business, especially in mature markets where new customers can only be acquired from competitors. Different methods and techniques have been used to investigate customer retention including statistical methods and data mining. Due to the increasing complexity of the mobile market, however, the effectiveness of these techniques is questionable. Through empirical experimentation, this paper compares two techniques of analysing customer churn - decision tree and logistic regression models. Experimental results are presented that show decision trees as the superior technique in the case presented. The paper also highlights issues of significance, causality, data evolution, model complexity and aggregation that indicate a need for more sophisticated approaches to churn modelling.

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Why is it important?

This paper compares two techniques of analysing customer churn - decision tree and logistic regression models. The paper also highlights issues of significance, causality, data evolution, model complexity and aggregation that indicate a need for more sophisticated approaches to churn modelling.