Study of Users’ Data Volume as Function of Quality of Experience for Churn Prediction

Abstract: Customer churn has always been a problem to be addressed by the telecommunication service providers. So far, work done in this regard was based on analyzing historical data of the customers by using different data mining techniques. Investigations based on individual user behavior with a motive of churn prediction are expected to give an idea about the user’s point view towards churn. Data volumes/data usage of the users is seen as parameter to assess the satisfaction of the users with the service. The subjective and objective behavior of the mobile phone users has been captured by collecting data about the data volumes/data usage for both Wi-Fi and mobile services along with their ratings of Quality of Experience (QoE).   The Experience Sampling Method has been deployed to collect the user data. Android tool was used to collect weekly data volumes of the users. A questionnaire was prepared with questions regarding quality, annoyance and churn risk of the users. The questionnaire was used to collect the weekly opinions of the users on the service. A total of 22 users participated in the study, of which 3 persons churned to other service provider during the study. The data collected in the study was analyzed using averages, correlations and decision trees. Comparisons were made between Wi-Fi and mobile services, churners and non-churners/active users. A 2-fold churn prediction model was proposed based on conclusions of the study.