The writing is on the wall: predicting customers' evaluation of customer-firm interactions using computerized text analysis

Abstract

PurposeThis methodological paper demonstrates how service firms can use digital technologies to quantify and predict customer evaluations of their interactions with the firm using unstructured, qualitative data. To harness the power of unstructured data and enhance the customer-firm relationship, the use of computerized text analysis is proposed.Design/methodology/approachThree empirical studies were conducted to exemplify the use of the computerized text analysis tool. A secondary data analysis of online customer reviews (n = 2,878) in a service industry was used. LIWC was used to conduct the text analysis, and thereafter SPSS was used to examine the predictive capability of the model for the evaluation of customer-firm interactions.FindingsA lexical analysis of online customer reviews was able to predict evaluations of customer-firm interactions across the three empirical studies. The authenticity and emotional tone present in the reviews served as the best predictors of customer evaluations of their service interactions with the firm.Practical implicationsComputerized text analysis is an inexpensive digital tool which, to date, has been sparsely used to analyze customer-firm interactions based on customers' online reviews. From a methodological perspective, the use of this tool to gain insights from unstructured data provides the ability to gain an understanding of customers' real-time evaluations of their service interactions with a firm without collecting primary data.Originality/valueThis research contributes to the growing body of knowledge regarding the use of computerized lexical analysis to assess unstructured, online customer reviews to predict customers' evaluations of a service interaction. The results offer service firms an inexpensive and user-friendly methodology to assess real-time, readily available reviews, complementing traditional customer research. A tool has been used to transform unstructured data into a numerical format, quantifying customer evaluations of service interactions.

Guest editorial: Emerging digital technologies and professional services: current and future research agenda

“…(2023) use a focus group discussion with participants from PSFs followed by in-depth interviews with employees from knowledge-intensive PSFs in Sweden to explore the combined influence of artificial intelligence applications' usage and CXOs' experience in making business decisions. Finally, Ferreira et al . (2023) use three empirical studies to illustrate the use of computerized text analysis to interpret unstructured data by professional service providers, such as advertising and market research firms.…”

“…The eight papers published in this special issue adopt diverse theoretical and managerial perspectives along with a broad range of methodologies. Based on their approaches and contributions, we have classified these eight papers into three broad themes, namely (1) Diverse theoretical perspectives , including strategic agility perspective (Liu et al ., 2023), theory of resources and capabilities (Marino-Romero et al ., 2023) and a multidisciplinary perspective (Panda et al ., 2023); (2) Challenges in digitalization process , for professional legal firms (Kronblad et al ., 2023) and small professional services firms (Cardinali et al ., 2023) and (3) Practical applications of digitalization: the role of transformational leadership and technology-mediated knowledge sharing (TMKS) (Nguyen, 2023), AI usage in CXO decision-making (Kondapaka et al ., 2023) and the use of computerized text analysis to predict customers' evaluation of their service experience (Ferreira et al ., 2023). Next, we briefly describe all the eight papers under these three broad themes in this section, followed by a general discussion and recommendations for future research.…”

“…Finally, Ferreira et al (2023) use a methodological approach to demonstrate how service firms can use digital technologies to quantify and predict customer evaluations of their interactions with the firm using unstructured, qualitative data using computerized text analysis to harness the power of unstructured data and enhance customer-firm relationships. Three empirical studies were conducted to exemplify the use of the computerized text analysis tool.…”

“…Similarly, Kondapaka et al (2023) use a focus group discussion with participants from PSFs followed by in-depth interviews with employees from knowledgeintensive PSFs in Sweden to explore the combined influence of artificial intelligence applications' usage and CXOs' experience in making business decisions. Finally, Ferreira et al (2023) use three empirical studies to illustrate the use of computerized text analysis to interpret unstructured data by professional service providers, such as advertising and market research firms. All these papers employ different approaches to demonstrate the practical benefits of using digital technologies for different types of professional service firms in three countries, namely Vietnam, Sweden and South Africa.…”

A tale of two platforms: A comparative analysis of language use in consumer complaints on Reddit and Spotify Community

“…In current consumer complaint literature, experiments and surveys have been the most employed methods (see Kitapci et al, 2019), despite the vast research resources available in the online environment. The seemingly overwhelming volume of online data poses a challenge for firms to read them one by one, yet with the help of computerized text analysis tools, firms can quantify and predict consumer behavior (Ferreira et al, 2023). For example, Ferreira et al (2023) extracted reviews of three healthcare insurance companies from an online review platform.…”