Randomized Agent-Based Model for Mobile Customer Retention Behaviour Prediction

Abstract

Due to the development of technology, mobile phones have a crucial role in human life. Multiple sim card phones and a single person using multiple mobile phones are common nowadays. Telecommunication is a major area where big data technologies are needed. Competition among the telecommunication companies is high due to customer churn. Customer retention in telecom companies is one of the major problems. In this paper, we propose a Randomized Method (RM) using Map and Reduce big data functions to avoid data duplication in the customer call data of telecommunication application. We use agent-based model (ABM) to predict the complex customer behaviour for the retention of customers with a particular telecommunication service. Agent-based model increases the prediction accuracy due to its dynamic nature of agents. ABM suggests rules based on mobile user variable features using multiple agents. This paper shows the effectiveness RM with MapReduce along with agent-based model to predict customer retention behaviour. The benefit of this proposed system is simple, cost-effective and flexible prediction model with high business value.