Enhanced Churn Prediction in theTelecommunication Industry

Awodele Oludele, Adeniyi Ben, Ogbonna A.C, Kuyoro S.O, Ebiesuwa Seun
2020 International Journal of Innovative Research in Computer Science & Technology  
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
more » ... n 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.
doi:10.21276/ijircst.2020.8.2.1 fatcat:q6kmxsaaknci7cjvu3p5f64yuq