Variable Selection of Customers for Churn Analysis in Telecommunication Industry

Variable Selection of Customers for Churn Analysis in Telecommunication Industry

Vishal Mahajan (AGM, HCL Technologies, Noida, India) and Renuka Mahajan (Assistant Professor, Jaipuria Institute of Management, Noida, India)
Copyright: © 2018 |Pages: 16
DOI: 10.4018/IJVCSN.2018010102

Abstract

The telecommunication industry considers customer relationship management as a significant issue for organizational adaptation. Mobile service providers have enforced CRM with the objective to reduce the number of customers that churn. The objective of this article is to detect high impact factors leading to customer churn in the mobile industry over the present-day market situation in Delhi-NCR by using a questionnaire survey and examine their importance. The study is done to understand usage patterns of customers using mobile data services. The data collected was analyzed using descriptive statistics to identify the most common issues to identify attributes of selecting a service provider, cellular usage, and service quality. Thus, the authors have selected possible variables for modeling the decision tree to build a churn prediction model. A renewed customer service, after analyzing this experience, could predict those customers who are at risk of switching to a different provider.
Article Preview
Top

Various studies have been done on factors pertaining to churn (Kotler and Keller, 2006), (Reichheld and Sasser, 1990); (Cronin et al., 2001); (Kang and James, 2004); (Yoon and Suh, 2004); (Omotayo and Joachim, 2008);(Khan, Jamwal and Sepehri, 2010); (Bitner and Hubbert, 1994); (Cronin and Taylor, 1992); (Zeithaml, Berry and Parasuraman, 1996); (Lee and Murphy 2008); (Al-Rousan et al., 2010); (Cardozo, 1965); (Parasuman, Zeithaml and Berry, 1991); Loya & Bhatt (2013); Hussain et al. (2016); Bhatia & Chanda (2016).

The subscribers use their cell phones as cameras, address and contact lists, as web browsers, navigation devices and many other functions. There has been very scarce research on analyzing the impact of data usage on customer churn. Further, due to the cost involved, most of the studies involving surveys are using small data sample of customer records (Keaveney, 1995; Bolton et al., 2000; Gerpottet al., 2001; Lee et al., 2003; Kim et al., 2004), which may undermine the reliability and validity of analysis results. All these studies are based on subscribers belonging to some specific area and no study was conducted on subscribers in Delhi/NCR region.

Complete Article List

Search this Journal:
Reset
Open Access Articles: Forthcoming
Volume 10: 4 Issues (2018)
Volume 9: 4 Issues (2017)
Volume 8: 4 Issues (2016)
Volume 7: 4 Issues (2015)
Volume 6: 4 Issues (2014)
Volume 5: 4 Issues (2013)
Volume 4: 4 Issues (2012)
Volume 3: 4 Issues (2011)
Volume 2: 4 Issues (2010)
Volume 1: 4 Issues (2009)
View Complete Journal Contents Listing