Training and scoring of predictive models. Telecommunication's company and a novel big data architecture for both the We evaluate our approach using a rich dataset provided by a major African To this end, we follow a supervised learningĪpproach for prediction and train our 'Restricted Random Forest' model using,Īs a proxy for bad experience, the observed customer transactions in the telcoĭata feed before the user places a call to a customer care center. We present an approach to capture, in (near) real-time, the mobile customerĮxperience in order to assess which conditions lead the user to place a call toĪ telco's customer care center. Real-time? That is the problem that we address in this paper. Measure and predict the quality of a user's experience on a telco network in Telcos to maintain or grow their market share, by providing users with as goodĪn experience as possible on their network.īut the task of extracting customer insights from the vast amounts of dataĬollected by telcos is growing in complexity and scale everey day. In this challenging environment it is critical for Telecommunications operators (telcos) traditional sources of income, voiceĪnd SMS, are shrinking due to customers using over-the-top (OTT) applications Towards Real-time Customer Experience Prediction for Telecommunication Operators
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |