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.
[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 analysis. 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…
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