Modeling Customer Response to Service Quality Variability with Implications for Pricing

【主讲】Jordan Tong，威斯康星大学商学院运营与信息管理系副教授

【主题】客户对服务质量波动的反应以及对价格影响的模型

【时间】2019年5月22日（周三）上午10:00am – 11:30am

【地点】清华经管学院伟伦楼453

【语言】英语

【主办】管理科学与工程系

【简历】Jordan Tong的简历

Short Bio

Jordan Tong is the Wisconsin Naming Partners Professor at the Wisconsin School of Business, where he is an Associate Professor in the Operations and Information Management department. Professor Tong’s research uses mathematical modeling and experimental methods to investigate questions in behavioral science and operations management. His research appears in journals such as Management Science, Psychological Science, Manufacturing & Service Operations Management, and Production and Operations Management. He is an associate editor at Decision Sciences and an editorial board member at Production and Operations Management. Professor Tong is the Co-Academic Director of the Master’s of Business Analytics Program. He teaches undergraduate and Master’s-level courses in Operations Management, Operations Analytics, Data Visualization, and Prescriptive Modeling. He received his PhD in Operations Management from the Fuqua School of Business at Duke University and his BA in Mathematics from Pomona College.

【Speaker】Jordan Tong, Associate Professor, Department of Operations and Information Management, Wisconsin School of Business

【Topic】Modeling Customer Response to Service Quality Variability with Implications for Pricing

【Time】Wednesday, May 22, 2019, 10:00am – 11:30am

【Venue】Room 453, Weilun Building, Tsinghua SEM

【Language】English

【Organizer】Department of Management Science and Engineering

【Abstract】 We consider a firm that sells a service repeatedly with variable but stationary quality. Customers update their beliefs about quality based on their experiences, and their probability of purchase in each period is increasing in their belief about mean quality. We show that under a fixed price, quality variability leads to a revenue penalty. With quality variability, customer beliefs not only fluctuate, but also become downwardly biased. These effects arise even when customers are risk neutral and update beliefs symmetrically for good and bad experiences. Through sensitivity analyses, we find that this revenue penalty is greatest when quality variability is large, consumers place a strong weight on recent experiences, and when the mean service quality is high. We then investigate whether the firm can improve revenues through dynamic pricing. We show 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 achieve the same revenue as the optimal revenue under no quality variability. We then use heuristic policies and numerical examples to show that the firm can still achieve significant revenue gains through dynamic pricing even in scenarios in which one cannot perfectly estimate customer beliefs about quality. Finally, we extend our model to consider social learning and competition between two firms who both have variable quality.