How Service Quality Variability Hurts Revenue When Customers Learn: Implications for Dynamic Personalized Pricing

Gregory DeCroix (), Xiaoyang Long () and Jordan Tong ()
Additional contact information
Gregory DeCroix: Wisconsin School of Business, University of Wisconsin-Madison, Madison, Wisconsin 53706
Xiaoyang Long: Wisconsin School of Business, University of Wisconsin-Madison, Madison, Wisconsin 53706
Jordan Tong: Wisconsin School of Business, University of Wisconsin-Madison, Madison, Wisconsin 53706

Operations Research, 2021, vol. 69, issue 3, 683-708

Abstract: We formalize an understudied mechanism through which quality variability hurts firm revenues, analyze when and why this mechanism is important, and generate new insight into how dynamic personalized pricing strategies can mitigate the negative effects of quality variability. To do so, we model a firm that sells a service repeatedly with variable but stationary quality. Customers update their quality beliefs based on their experiences (exponential smoothing), and their purchase probability in each period increases with their respective beliefs about mean quality (logit choice). For any fixed price, we show that quality variability reduces the firm’s revenue and leads to a downward bias in customer beliefs about quality. These effects arise even when customers are risk neutral and update their beliefs symmetrically after good and bad experiences. We then investigate whether the firm can improve revenues through dynamic personalized pricing. We find that a fixed perceived surplus pricing policy—charging a lower price when a customer believes the quality is lower to induce a constant purchase probability—is not only optimal but can also match the optimal revenue when quality is not variable. The revenue gain from implementing this pricing policy (compared with fixed pricing) is greatest when quality variability is large, customers react strongly to recent experiences, and/or mean service quality is high. Our numerical results further show that firms can achieve significant revenue gains through dynamic pricing even in information-poor or low-price-flexibility environments. Finally, we extend our model to consider social learning and competition between two firms.

Keywords: Dynamic programming: semi Markov; Operations and Supply Chains; service quality; customer choice dynamics; stochastic models; customer learning; behavioral operations; dynamic pricing (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://dx.doi.org/10.1287/opre.2020.2058 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:69:y:2021:i:3:p:683-708

Access Statistics for this article

More articles in Operations Research from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().