The Voice of the Customer: Managing Customer Care in Twitter

Reza Mousavi (), Monica Johar () and Vijay S. Mookerjee ()
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Reza Mousavi: Information Technology Area, McIntire School of Commerce, University of Virginia, Charlottesville, Virginia 22904;
Monica Johar: Belk College of Business, University of North Carolina at Charlotte, Charlotte, North Carolina 28223;
Vijay S. Mookerjee: Naveen Jindal School of Management, University of Texas at Dallas, Richardson, Texas 75080

Information Systems Research, 2020, vol. 31, issue 2, 340-360

Abstract: In recent years, managing customer sentiment—particularly on social media—has become crucial as more customers use social media to seek help from firms. Therefore, we strive to determine an optimal strategy to manage customer sentiment on social media sites such as Twitter. We also aim to identify factors and external events that can influence the effectiveness of customer care. To understand the antecedents of digital customer care, we model a diffusion process of customer sentiment over time. This diffusion process is influenced (or controlled) by the firm through the strategy employed to respond to customer tweets. We then use real data consisting of sentiments expressed by customers directed at Twitter’s service accounts of four major U.S. telecommunication-service providers (AT&T, Verizon, Sprint, and T-Mobile) to estimate the parameters in our analytical model and shed several insights into digital customer care in this industry. First, we find a clear separation among the firms in terms of digital customer care effectiveness. Second, we find that good customer care is not merely a matter of responding to customer tweets. Third, the quality of digital customer care that customers expect varies across firms: Customers of higher priced firms (e.g., Verizon and AT&T) expect better customer care. Fourth, seemingly unrelated events (such as signing an exclusive contract with a celebrity) can impact digital customer care. Our study has important implications for managers as it can help firms determine the optimal strategy to influence customer sentiment.

Keywords: digital customer care; customer sentiment; stochastic differential equations; forecasting; Twitter; sentiment analysis (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (3)

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