Enhanced Churn Prediction in theTelecommunication Industry (International Journal of Innovative Research in Computer Science & Technology, 2020)
Prediction models are usually built by  applying a supervised learning algorithm to historical data.  This involves the use of data analytics system that uses  real-time integration and dynamic real time responses data  to detect churn risks. Subscribe are increasingly  terminating their membership agreement with  telecommunication companies through mobile number  portability (MNP) in order to subscribe to another  competitor companies.  To model the Customer prediction, a Markov Chain Model  will be used. The Markov model allows for more flexibility  than most other potential models, and can incorporate  variables such as non-constant retention rate, which is not  possible in the simpler models. The model allows looking at  individual customer relationships as well as averages, and  its probabilistic nature makes the uncertainty  apprehensible. The Markov Decision Process is also  appealing, but since dynamic decisions along the lifetime of  the customer will not be evaluated the Markov Chain is the  simplest model that still meets the requirements. Each state  in the Markov Chain will represent a person being a  customer for one month, with an infinite number of states.  The transition probability to move from one state to the next  is equivalent to a customer retaining with the operator to  the next month. A customer that has churned will be  considered lost forever. Once the retention and churn rates are determined, the  reference churn value for each customer will be computed.  The churn rate will be calculated using MATLAB Monte  Carlo simulations, running a large number of fictitious  customer-company relationship processes, and extracting  the results of the average customer.
Keywords: Prediction Models; mobile number portability,; Markov Decision Process; churn rate