Using Text Mining for Customer Feedback

[We’re pleased to welcome Francisco Villarroel Ordenes, who is one of five collaborating authors on the article “Analyzing Customer Experience Feedback Using Text Mining: A Linguistics-Based Approach” from Journal of Service Research.]

The Big Data phenomenon is not only about exponential growth of customer data, but about new and challenging data structures such as textual information which require new methods and metrics to facilitate 02JSR13_Covers.inddanalysis. Customer experience feedback, usually found in platforms such as social media, e-mails and feedback forms represents a form of complicated data structure which is challenging organizations to develop new methods for its timely and consistent analysis. Our paper, “Analyzing Customer Experience Feedback Using Text Mining: A Linguistics-Based Approach”, is the result of a collaborative effort between Marketing and Information Systems researchers. We develop a Case Study with a UK service organization which receives more than 10000 comments of customer experience feedback per month. In this context, we design and implement the ARC (Activities, Resources, Context) framework, which is able to automate the analysis of customer feedback through a text mining model. The text mining approach used with this guiding framework is useful for analyzing customer experience feedback with the standard flow of activities (stages) of any service. Due to its flexible evolutionary format we describe it as an ‘open learning model’. Specifically, application of the text mining model within the ARC framework provides efficient and faster analysis of textual information compared with the current manual processes (seconds compared with 2 weeks). The consistency of the information extracted and the specificity of the analysis provided deliver an additional advantage: namely, the practicality of identifying resources or activities that the company can improve immediately. The article provides managers and researchers with a text analytics methodology and application which departs from simple sentiment analysis. Instead a more holistic representation of customer experience feedback in verbatim data is identified, which enables managers to identify what is causing particular sentiment outcomes and thus they can then act to reallocate resources or change processes at an organizational or even customer-specific level.

Read “Analyzing Customer Experience Feedback Using Text Mining: A Linguistics-Based Approach” from Journal of Service Research for free by clicking here. Make sure to click here to sign up for e-alerts and be the first to know all the latest from Journal of Service Research!

s200_francisco.villarroelFrancisco Villarroel Ordenes is a PhD candidate at the Marketing and Supply Chain Management Department at the School of Business Economics in Maastricht University. His research interests include social media conversations, customer experience feedback, sentiment analysis, value cocreation, and the development of text mining methods for marketing research.

publicphoto.ashxBabis Theodoulidis is an associate professor in information management at Manchester Business School, University of Manchester. His research has been published in both science and social science journals such as International Journal Services Technology and Management, Journal of Information Systems, Knowledge Management Research & Practice, Expert Systems with Applications, International Journal of Information Management, International Journal of Data Warehousing and Mining, and Journal of Visual Languages and Computing. His most recent research interests focus on the design of service-based information systems, the temporal and spatial aspects of information, the analysis of information using data and text mining techniques, the visualization of information, and service information management issues within organizations.

jamie.ashxJamie Burton is head of the marketing group and an associate professor in Marketing at Manchester Business School (MBS). He is a research director for MBS’s Customer Management Leadership Group, publishes in a number of journals including the Journal of Marketing Management and the Journal of Service Management and his research interests include customer experience and feedback, transformative service research including service marketing, servitization, relationship marketing, and customer profitability. He is a lead author of a 2013 British Quality Foundation report and is coauthor of Murphy, J. et al. (2006), Converting Customer Value: from Retention to Profit, Chichester: John Wiley and Sons.

Thorsten_GruberThorsten Gruber is a co-director of the Centre for Service Management and a professor of Marketing and Service Management at Loughborough University. His research interests include consumer complaining behavior, services marketing, and the development of qualitative online research methods. His work has been published in journals such as Journal of the Academy of Marketing Science, Journal of Product Innovation Management, Journal of Business Research, Journal of Service Management, and Industrial Marketing Management.

MZMohamed Zaki is a research associate at Cambridge Service Alliance, University of Cambridge. His research lies in the field of information governance, business intelligence, and big data analytics. He has many publications in these areas. His experience in the business intelligence/data analytics and service innovation areas enables him to consult in various projects to investigate business intelligence issues in different domains within a service-oriented architecture. Currently, he investigates “How Big data could play a role in improving and optimising services within complex service network organisation” in different sectors such as education, asset heavy, and defense.


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Reblogged this on Center for Services Leadership Blog and commented:
Great to see new research that’s happening on the intersection of multiple disciplines. With large amounts of data that companies are accumulating through various communication channels finding new methods and metrics for timely and accurate analysis is becoming more and more critical. This research tested a new framework that allows to automate the analysis of customer feedback through a text mining model. The article is currently available free on Journal of Service Research website.

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