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Extended Cox Modeling of Customer Retention in Mobile Telecommunication Sector of Rwanda

Received: 25 August 2015     Accepted: 15 September 2015     Published: 13 October 2015
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Abstract

Retaining customers improves profitability, importantly reduces the cost incurred in acquiring new customers and moreover a firm can increase profits by 25-95 percent if it could improve its customer retention rates by 5 percent. As markets mature and competitive pressure intensifies, companies can no longer ignore the importance of customer retention as their existing customer bases have become their precious assets. This research aims to model customer retention in Rwandan telecom sector using survival analysis technique in order to inform the concerned institutions and companies about telecom customer retention in Rwanda. The Cox regression model and extended Cox model were developed using simulation approach in order to assess which model is the best for customer retention. It was found that the customer’s socio-economic, demographic and behavioral characteristics have an effect on churn rate. The extended Cox model was the best description of how customer retention is achieved. These findings hold implications for industry operators on key areas to pay attention to in order to achieve customer retention.

Published in American Journal of Theoretical and Applied Statistics (Volume 4, Issue 6)
DOI 10.11648/j.ajtas.20150406.17
Page(s) 471-479
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2015. Published by Science Publishing Group

Keywords

Customer retention, Cox model, Extended Cox Model

References
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[2] Ali, T. J. (2009), Predicting customer churn in the telecommunications service providers, Master’s thesis, LuLea University of Technology.
[3] Collett, D. (2003), Modelling survival data in medical research, CRC press.
[4] Dudde, P. (2014), ‘Rwanda-telecoms, mobile and broadband-market insights and statistics’, research and markets.
[5] Fox, J. (2002), ‘Cox proportional-hazards regression for survival data’, An R and S-PLUS companion to applied regression pp. 1–18.
[6] Fox, J. & Weisberg, S. (2011), ‘Cox proportional-hazards regression for survival data in r. an appendix to an r companion to applied regression’.
[7] Kleinbaum, D. G. (1998), ‘Survival analysis, a self-learning text’, Biometrical Journal 40(1), 107–108.
[8] Lu, J. (2002), ‘Predicting customer churn in the telecommunications industry:an application of survival analysis modeling using sas’, SAS User Group International (SUGI27) Online Proceedings pp. 114–27.
[9] Martinussen, T. & Scheike, T. H. (2007), Dynamic regression models for survival data, Springer Science & Business Media.
[10] Ocloo, C. E. & Tsetse, E. K. (2013), ‘Customer retention in the ghanaian mobile telecommunication industry’, European Journal of Business and Social Sciences 2(7), 136–160.
[11] Portela, S. & Menezes, R. (2011), ‘detecting customer defections: an application of continuous duration models’, Journal of Global Strategic Management (09), 22–30.
[12] RDB (2013), “The national customer satisfaction survey”, Rwanda Development Board.
[13] Reichheld, F. F. & Schefter, P. (2000), ‘E-loyalty’, Harvard business review 78(4), 105–113.
[14] RURA (2015), ‘Communication and media’, Rwanda Utilities Regulatory Authority .
[15] Sharmeela-Banu, S. A., Gengeswari, K. & Padmashantini, P. (2012), ‘Customer retention practices among the major retailers in malaysia’, International Journal of Academic Research in Business and Social Sciences 2(6), 157–166.
[16] Timothy, K. L., Cooil, B., Aksoy, L., Andreassen, T. W. & Weiner, J. (2007), ‘The value of different customer satisfaction and loyalty metrics in predicting customer retention, recommendation, and shareof-wallet’, Managing Service Quality: An International Journal 17(4), 361–384.
[17] Van Den Poel, D. & Lariviere, B. (2004), ‘Customer attrition analysis for financial services using proportional hazard models’, European journal of operational research 157(1), 196–217.
[18] Williams, C., Solomon, G. & Pepper, R. (2012), ‘The impact iof mobile telephony on economic growth. a report for groupe speciale mobile association (gsma)’. A report for GSM Assocition.
[19] Wong, K. K.-K. (2011), ‘Using Cox regression to model customer time to churn in the wireless telecommunications industry’, Journal of Targeting, Measurement and Analysis for Marketing 19(1), 37–43.
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  • APA Style

    Diane Ingabire, Samuel Musili Mwalili, George Otieno Orwa. (2015). Extended Cox Modeling of Customer Retention in Mobile Telecommunication Sector of Rwanda. American Journal of Theoretical and Applied Statistics, 4(6), 471-479. https://doi.org/10.11648/j.ajtas.20150406.17

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    ACS Style

    Diane Ingabire; Samuel Musili Mwalili; George Otieno Orwa. Extended Cox Modeling of Customer Retention in Mobile Telecommunication Sector of Rwanda. Am. J. Theor. Appl. Stat. 2015, 4(6), 471-479. doi: 10.11648/j.ajtas.20150406.17

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    AMA Style

    Diane Ingabire, Samuel Musili Mwalili, George Otieno Orwa. Extended Cox Modeling of Customer Retention in Mobile Telecommunication Sector of Rwanda. Am J Theor Appl Stat. 2015;4(6):471-479. doi: 10.11648/j.ajtas.20150406.17

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  • @article{10.11648/j.ajtas.20150406.17,
      author = {Diane Ingabire and Samuel Musili Mwalili and George Otieno Orwa},
      title = {Extended Cox Modeling of Customer Retention in Mobile Telecommunication Sector of Rwanda},
      journal = {American Journal of Theoretical and Applied Statistics},
      volume = {4},
      number = {6},
      pages = {471-479},
      doi = {10.11648/j.ajtas.20150406.17},
      url = {https://doi.org/10.11648/j.ajtas.20150406.17},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20150406.17},
      abstract = {Retaining customers improves profitability, importantly reduces the cost incurred in acquiring new customers and moreover a firm can increase profits by 25-95 percent if it could improve its customer retention rates by 5 percent. As markets mature and competitive pressure intensifies, companies can no longer ignore the importance of customer retention as their existing customer bases have become their precious assets. This research aims to model customer retention in Rwandan telecom sector using survival analysis technique in order to inform the concerned institutions and companies about telecom customer retention in Rwanda. The Cox regression model and extended Cox model were developed using simulation approach in order to assess which model is the best for customer retention. It was found that the customer’s socio-economic, demographic and behavioral characteristics have an effect on churn rate. The extended Cox model was the best description of how customer retention is achieved. These findings hold implications for industry operators on key areas to pay attention to in order to achieve customer retention.},
     year = {2015}
    }
    

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    AU  - Diane Ingabire
    AU  - Samuel Musili Mwalili
    AU  - George Otieno Orwa
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    JF  - American Journal of Theoretical and Applied Statistics
    JO  - American Journal of Theoretical and Applied Statistics
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    AB  - Retaining customers improves profitability, importantly reduces the cost incurred in acquiring new customers and moreover a firm can increase profits by 25-95 percent if it could improve its customer retention rates by 5 percent. As markets mature and competitive pressure intensifies, companies can no longer ignore the importance of customer retention as their existing customer bases have become their precious assets. This research aims to model customer retention in Rwandan telecom sector using survival analysis technique in order to inform the concerned institutions and companies about telecom customer retention in Rwanda. The Cox regression model and extended Cox model were developed using simulation approach in order to assess which model is the best for customer retention. It was found that the customer’s socio-economic, demographic and behavioral characteristics have an effect on churn rate. The extended Cox model was the best description of how customer retention is achieved. These findings hold implications for industry operators on key areas to pay attention to in order to achieve customer retention.
    VL  - 4
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Author Information
  • Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

  • Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

  • Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

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