Identifying Dissatisfied 4G Customers from Network Indicators: A Comparison between Complaint and Survey Data

Identifying Dissatisfied 4G Customers from Network Indicators: A Comparison between Complaint and Survey Data

Alexis Huet (Nanjing Howso Technology, Nanjing, China), Ye Ouyang (Columbia University, New York City, NY, USA), Mantian (Mandy) Hu (Chinese University of Hong Kong, Hong Kong, China) and Xinling Dai (Nanjing Howso Technology, Nanjing, China)
DOI: 10.4018/IJITN.2016010102
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Abstract

Feedback data directly collected from users are a great source of information for telecom operators. They are usually retrieved as complaints and survey data. For the mobile telecoms sector, one purpose is to manage those data to identify network problems leading to customer dissatisfaction. In this paper, a quantitative methodology is used to predict dissatisfied users. It focuses on extraction and selection of predictive features, followed by a classification model. Two sets of data are used for experiments: one is related to complaints, the other to survey data. Since the methodology is similar for those two sets, prediction efficiency and influence of features are compared. Specific influence of user loyalty in survey data is highlighted. Thus, the methodology presented in this article provides a reference for the mobile operators to improve procedures for collecting feedback answers.
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2. Research Background

The customer satisfaction evaluation through customer data has been an extended field of research for industrial companies. The concept of customer loyalty has been intensively studied in (Alan & Basu, 1994; Olever, 1999), as well as the customers’ general behavior (Olever, 1997). From those concepts, the idea of a unique Net Promoter Score (NPS) emerged (Reichheld, 2003), which is a one-question survey focusing on how likely a customer would recommend the service to other people. From a more general perspective, how to carry out a survey process has been summarized. A review is available in (Fowler, 2013), describing how to conduct a survey, to design questions and to sample users.

Common techniques to analyze survey data is presented in (Rossi, Wright & Anderson, 2013). In the telecom industries, analysis of survey data became a key research subject to provide better services for customers. In (Qian, 2011; Rossi, Wright & Anderson, 2013; Zhang, 2014), links between surveys and external indicators have been studied. Also, (Yu, 2014) brings an analysis on network quality satisfaction through a survey, determining which main factors influence the network quality.

As for complaints analysis, an overview of available information from those data can be seen in (Goodman & Newman, 2003). This article points out the root cause of a complaint generally cannot be directly deduced. Prediction tasks have also been performed using complaints data, including big data techniques (Chen, 2007; Chen, 2014; Hadden & al., 2006; Wang, Wang, Zang & al., 2012).

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