Extended Cox Modeling of Customer Retention in Mobile Telecommunication Sector of Rwanda
American Journal of Theoretical and Applied Statistics
Volume 4, Issue 6, November 2015, Pages: 471-479
Received: Aug. 25, 2015; Accepted: Sep. 15, 2015; Published: Oct. 13, 2015
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Authors
Diane Ingabire, Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Samuel Musili Mwalili, Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
George Otieno Orwa, Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
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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.
Keywords
Customer retention, Cox model, Extended Cox Model
To cite this article
Diane Ingabire, Samuel Musili Mwalili, George Otieno Orwa, Extended Cox Modeling of Customer Retention in Mobile Telecommunication Sector of Rwanda, American Journal of Theoretical and Applied Statistics. Vol. 4, No. 6, 2015, pp. 471-479. doi: 10.11648/j.ajtas.20150406.17
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