Big data architecture for predicting churn risk in mobile phone companies

Abstract

Nowadays in Peru, mobile phone companies have been affected by the problem of mobile number portability because since July 2014 customers can change their mobile operator in just 24 h. Companies look for solutions through the analysis of historical data of their customers in order to generate predictive models and to identify which customers would leave the company. However, the current way how this prediction is performed is too slow. In this paper, we show a Big Data architecture which solves the problems of the “classic architecture” using data from social networks in order to predict which customers may go to the competition company, according to their opinions. Data processing is performed by Hadoop, which implements MapReduce and can process large amounts of data in parallel way. After doing the tests and seeing the results, we got a high percentage of accuracy (90.03% of success).