Improved Customer Churn and Retention Decision Management Using Operations Research Approach

Authors

  • Sulaimon Olanrewaju Adebiyi Department of Business Administration, University of Lagos, Akoka, Lagos. Nigeria
  • Emmanuel Olateju Oyatoye Department of Business Administration, University of Lagos, Akoka, Lagos. Nigeria.
  • Bilqis Bolanle Amole Accounting and Business Administration Department, Distance Learning Institute, University of Lagos, Akoka, Lagos. Nigeria

DOI:

https://doi.org/10.5195/emaj.2016.101

Keywords:

AHP, Markov chain, customer churn, retention, decisions and strategies

Abstract

The relevance of operations research cannot be overemphasized, as it provides the best possible results in any given circumstance, through analysis of operations and the use of scientific method thus, this paper explore the combination of two operations research models (analytic hierarchy process and Markov chain) for solving subscribers’ churn and retention problem peculiar to most service firms. A conceptual model for unraveling the problem customer churn and retention decision management was proposed and tested with data on third level analysis of AHP for determining appropriate strategies for customer churn and retention in the Nigeria telecommunication industries. A survey was conducted with 408 subscribers; the sample for the study was selected through multi-stage sampling. Two analytical tools were proposed for the analysis of data. These include: Expert Choice/Excel Solver (using Microsoft Excel) and Windows based Quantitative System for Business (WinQSB). This paper plays important role in understanding various strategies for effective churn and retention management and the ranking of churn and retention drivers in order of importance to stakeholders` decision-making. The study provided a framework for understanding the application of AHP and Markov chain for modeling, analysing and proffering solution to problem of churn and retention. The study recommends organizational strategies (corporate, business and functional) that reverse the churn alternatives with high priority and equally strengthen service delivery on high priority retention alternatives in order to ensure firms sustainable competitive advantage.

 

An erratum to this article has been published as https://doi.org/10.5195/emaj.2017.131.

Author Biography

Sulaimon Olanrewaju Adebiyi, Department of Business Administration, University of Lagos, Akoka, Lagos. Nigeria

Department of Business Administration, University of Lagos, Akoka, Lagos. Nigeria

References

Adebiyi S. O., Oyatoye, E. O & Kuye, O. L (2015). An analytic hierarchy process analysis: Application to subscriber retention decisions in the Nigerian mobile telecommunications industry, International Journal of Management and Economics, 48, 63-83.

Adebiyi S. O., Oyatoye E. O & Mojekwu, J. N. (2015). Predicting customer churn and retention rates in Nigeria's mobile telecommunication industry using Markov chain modeling, Acta Universities Sapientia Economics and Business, 3, 74-80.

Adebiyi S. O., Oyatoye E. O & Amole, B. B. (2015). Determinants of customers` churn decision in the Nigeria telecommunication industry: An analytic hierarchy process approach, International Journal of Economic Behaviour, 5, 81-104.

Adeleke, A & Aminu S. A, (2012). The determinants of customer loyalty in Nigeria’s GSM market, International Journal of Business and Social Science, 3(14), 209-222.

Au, W., Chan, C. C. & Yao, X. (2003). A novel evolutionary data mining algorithm with applications to churn prediction. IEEE transactions on evolutionary computation, 7(6), 532-545.

Berson, A., Simith S, & Thearling, K., (2000). Building data mining applications for CRM, New York: McGraw-Hill.

Blau, P. (1994). Structural contexts of opportunities. Chicago: University of Chicago Press.

Blau, P. (1964). Exchange and power in social life. New York: John Wiley & Sons.

Buckinx, W., & Van den Poel, D. (2005). Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting. European Journal of Operational Research, 164(1), 252-268.

Burez, J., & Van den Poel, D. (2007). CRM at a Pay-TV company: Using analytical models to reduce customer attrition by targeted marketing for subscription services. Expert Systems with Applications, 32(2), 277-288.

Chiang, D. A., Wang, Y. F., Lee, S. L., & Lin, C. J. (2003). Goal-oriented sequential pattern for network banking churn analysis. Expert Systems with Applications, 25, 293-302.

Chu, B. H, Tsai, M. S, & Ho, C. S., (2007). Toward a hybrid data mining model for customer retention, Knowledge Based System, 20, 703-718.

Coleman, J. S. (1990). Foundations of social theory. Cambridge, Mass, Harvard University Press.

Coussement, K., & Van den Poel, D. (2008). Churn prediction in subscription services: An application of support vector machines while comparing two parameter selection techniques. Expert Systems with Applications, 34, 313-327.

Coussement, K., Benoit, D. F & Van den Poel, D (2010). Improved marketing decision making in a customer churn prediction context using generalized additive models, Expert Systems with Applications, 37, 2132-2143.

Drucker, P. F. (1973). Management: Tasks, Responsibilities, practices. NY: Harper & Row.

Dura’n, O., & Aguilo, J. (2008). Computeraided machine-tool selection based on a fuzzy-AHP approach. Expert Systems with Applications, 34, 1787–1794.

Emerson, R. (1962). Power-Dependence relations. American Sociological Review 27, 31-41.

Homans, G. (1961). Social behaviour. New York, Harcourt, Brace & World.

Hung, S. Y., Yen, D. C., & Wang, H. Y. (2006). Applying data mining to telecomm churn management. Expert Systems with Applications, 31(3), 515-524.

IBM Corporation, (2010). Working with telecommunications, Minimizing churn in the telecommunications industry, United States of America.

Kim, H. S., & Yoon, C. H. (2004). Determinants of subscriber churn and customer loyalty in the Korean mobile telephony market. Telecommunications Policy, 28, 751-765.

Kolajo, T & A. B. Adeyemo (2012). Data Mining technique for predicting telecommunications industry customer churn using both descriptive and predictive algorithms. Computing Information Systems & Development Informatics Journal. 3(2). 27-34.

Larivie`re, B, & Van den Poel, D. (2005). Predicting customer retention and profitability by using random forests and regression forests techniques. Expert System Application, 29(2), 472-484.

Mubea, K. W, Ngigi, T. G & Mundia, C. N. (2010). Assessing application of Markov Chain analysis in predicting land cover change: A case study of Nakuru municipality, Jomo Kenyatta University of Agriculture and Technology (JAGST), 12(2), 126-144.

Ngai, E. W. T, Xiu, L, & Chau, D. C. K., (2009). Application of data mining techniques in customer relationship management: A literature review and classification. Expert System Application, 36, 2592-2602.

Oyatoye E. O., Adebiyi S. O and Amole, B. B. (2015). Modeling switching behaviour of Nigeria global system for mobile communication multiple SIMs subscribers` using Markov chain analysis, The Indiana University Press (IUP) Journal of Operations Management, 14(1), 7-31.

Oyatoye E.O, Adebiyi S.O & Amole, B. B. (2013). An empirical study on consumers’ preference for mobile telecommunication attributes in Nigeria, British Journal of Economics, Management & Trade, 3(4), 419-428.

Pyramid Research (2010). The impact of mobile services in Nigeria: How mobile technologies are transforming economic and social activities; Abuja, Nigeria.

Reinartz, W.J. Kumar, V. (2003). The impact of customer relationship characteristics on profitable lifetime duration, Journal of Marketing, 67(1), 77-99.

Reynolds, T. & Jolly, J. (1980). Measuring personal values: an evaluation of alternative methods. Journal Marketing Science, 17, 531–536.

Rodpysh, K. V. (2013). Applying Data Mining to customer churn analysis: a case study on the insurance industry of Iran, Open Journal of Artificial Intelligence, 1(1), 8-12.

Saaty, T. L. (1980). The Analytic Hierarchy Process, McGraw- Hill, New York.

Sharma. A & Panigrahi, P. K. (2011). A Neural Network based approach for predicting customer churn in cellular network services, International Journal of Computer Applications, 27(11), 56-89.

Sharma J. K. (2009). Operations Research; theory and applications, third edition, Indian Macmillan Ltd.

Slăvescu E O, (2011). The implementation of uplift modeling to telecommunications marketing campaigns. The case of the Romanian mobile telecommunications market. Proceedings of The 7th International Conference Management of Technological Changes, Alexandroupolis, Greece, 2011.

Tsai, C. F. & Chen, M. Y. (2010). Variable selection by association rules for customer churn prediction of multimedia on demand, Expert Systems with Applications, 37, 2006-2015.

Van den Poel, D. & Lariviere, B. (2004). Customer attrition analysis for financial services using proportional hazard models. European Journal of Operational Research, 157, 196-217.

Wei, C. P., & Chiu, I. T. (2002). Turning telecommunications call details to churn prediction: A data mining approach. Expert Systems with Applications, 23, 103-112.

Yilmaz, H. (2009). “Optimization of the product design through Quality Function Deployment (QFD) and Analytical Hierarchy Process (AHP): A case study in a ceramic washbasin”, Thesis Submitted to The Graduate School of Engineering and Sciences of Izmir Institute of Technology in Partial Fulfillment of the Requirements for the Degree of Master of Science.

Published

2017-01-06

Issue

Section

Errata