The Impact of Experimental Setup in Prepaid Churn Prediction for Mobile Telecommunications: What to Predict, for Whom and Does t

Prepaid customers in mobile telecommunications are not bound by a contract and can therefore change operators (`churn') at their convenience and without notification. This makes the task of predicting prepaid churn both challenging and financially rewarding. This paper presents an explorative, real world study of prepaid churn modeling by varying the experimental setup on three dimensions: data, outcome definition and population sample. Firstly, we add Customer Experience Management (CEM) data to data traditionally used for prepaid churn prediction. Secondly, we vary the outcome definition of prepaid churn. Thirdly, we alter the sample of the customers included, based on their inactivity period at the time of recording. While adding CEM parameters did not influence the predictability of churn, one variation on the sample and especially a particular change in the outcome definition had a substantial influence.

• Type: Journal
• Year: 2010
• Where: MLDM
• Authors: Dejan Radosavljevik, Peter van der Putten, Kim Kyllesbech Larsen
• Keywords: Customers, Machine Learning, MLDM 2010, Outcome Definition, Prepaid Churn