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Telecommunications operators (telcos) traditional sources of income, voiceand SMS, are shrinking due to customers using over-the-top (OTT) applicationssuch as WhatsApp or Viber. In this challenging environment it is critical fortelcos to maintain or grow their market share, by providing users with as goodan experience as possible on their network.  But the task of extracting customer insights from the vast amounts of datacollected by telcos is growing in complexity and scale everey day. How can wemeasure and predict the quality of a user's experience on a telco network inreal-time? That is the problem that we address in this paper.  We present an approach to capture, in (near) real-time, the mobile customerexperience in order to assess which conditions lead the user to place a call toa telco's customer care center. To this end, we follow a supervised learningapproach for prediction and train our 'Restricted Random Forest' model using,as a proxy for bad experience, the observed customer transactions in the telcodata feed before the user places a call to a customer care center.  We evaluate our approach using a rich dataset provided by a major Africantelecommunication's company and a novel big data architecture for both thetraining and scoring of predictive models. Our empirical study shows oursolution to be effective at predicting user experience by inferring if acustomer will place a call based on his current context.  These promising results open new possibilities for improved customer service,which will help telcos to reduce churn rates and improve customer experience,both factors that directly impact their revenue growth.

Towards Real-time Customer Experience Prediction for Telecommunication Operators