Analyzing Customer Experience Feedback Using Text Mining: A Linguistics-Based Approach. (August 2014)

• Record Type: Journal Article
• Authors: Ordenes, Francisco Villarroel; Theodoulidis, Babis; Burton, Jamie; Gruber, Thorsten; Zaki, Mohamed
• Is Part Of: Journal of service research. Volume 17:Number 3(2014:Aug.)
• Page Start: 278
• Page End: 295
• Publication Date: 2014-08
• Subjects: activities -- resources -- context -- customer feedback -- text mining -- case study -- value cocreation -- customer experience; Customer services -- Periodicals; Service industries -- Periodicals; 658.81205
• DOI: 10.1177/1094670514524625
• Languages: English
• ISSNs: 1094-6705

Abstract

Complexity surrounding the holistic nature of customer experience has made measuring customer perceptions of interactive service experiences challenging. At the same time, advances in technology and changes in methods for collecting explicit customer feedback are generating increasing volumes of unstructured textual data, making it difficult for managers to analyze and interpret this information. Consequently, text mining, a method enabling automatic extraction of information from textual data, is gaining in popularity. However, this method has performed below expectations in terms of depth of analysis of customer experience feedback and accuracy. In this study, we advance linguistics-based text mining modeling to inform the process of developing an improved framework. The proposed framework incorporates important elements of customer experience, service methodologies, and theories such as cocreation processes, interactions, and context. This more holistic approach for analyzing feedback facilitates a deeper analysis of customer feedback experiences, by encompassing three value creation elements: activities, resources, and context (ARC). Empirical results show that the ARC framework facilitates the development of a text mining model for analysis of customer textual feedback that enables companies to assess the impact of interactive service processes on customer experiences. The proposed text mining model shows high accuracy levels and provides flexibility through training. As such, it can evolve to account for changing contexts over time and be deployed across different (service) business domains; we term it an "open learning" model. The ability to timely assess customer experience feedback represents a prerequisite for successful cocreation processes in a service environment.