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http://dx.doi.org/10.9716/KITS.2016.15.1.197

A Method of Predicting Service Time Based on Voice of Customer Data  

Kim, Jeonghun (경희대학교 일반대학원 경영학과)
Kwon, Ohbyung (경희대학교 경영대학)
Publication Information
Journal of Information Technology Services / v.15, no.1, 2016 , pp. 197-210 More about this Journal
Abstract
With the advent of text analytics, VOC (Voice of Customer) data become an important resource which provides the managers and marketing practitioners with consumer's veiled opinion and requirements. In other words, making relevant use of VOC data potentially improves the customer responsiveness and satisfaction, each of which eventually improves business performance. However, unstructured data set such as customers' complaints in VOC data have seldom used in marketing practices such as predicting service time as an index of service quality. Because the VOC data which contains unstructured data is too complicated form. Also that needs convert unstructured data from structure data which difficult process. Hence, this study aims to propose a prediction model to improve the estimation accuracy of the level of customer satisfaction by combining unstructured from textmining with structured data features in VOC. Also the relationship between the unstructured, structured data and service processing time through the regression analysis. Text mining techniques, sentiment analysis, keyword extraction, classification algorithms, decision tree and multiple regression are considered and compared. For the experiment, we used actual VOC data in a company.
Keywords
Text mining; VOC; Unstructured Data; Sentiment Analysis; Classification Algorithm;
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Times Cited By KSCI : 4  (Citation Analysis)
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