This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)
Awodele Oludele , Adeniyi Ben*, Ogbonna A.C., Kuyoro S.O., Ebiesuwa Seun
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.
Using simulation approach gives better result than analytical methods, since an indefinite number of states make matrix algebra complicated. It also allows visualizing the distribution of the results more easily than with algebraic calculation.
To the telecom companies the result of this analysis would improve the level at which they can predict customer churn, because, it will give insight on why a customer would choose to leave one telecommunication industry for another telecommunication industry. In other words, the cost of advertisement and loyalty programs as well as challenges face in retaining loyal customers would be identified. The researcher believes that the enhanced model for churn prediction developed from this study will lead to better retention strategy, improved telecom quality service, enhanced customer loyalty due to the improvement service from applying the information from this model. The study is also significant to researchers, behavioral scientist, business analysts as well as professionals in the computer science domain. The study will serve as a good reference material for research and contribute to the growing objective of developing enhanced model built on data mining techniques that can explain the churn behavior with more accuracy than using single methods.
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