Topic:A Model of Smart Technologies
Speaker:Monic Sun, Associate Professor of Marketing, Boston University’s Questrom School of Business
Time:Wednesday, 27 June, 13:30-15:00
Location:Room 217, Guanghua Building 2
We study the pricing and profit implications of smart technologies that can predict consumers’ real-time needs based on contextual factors and his past behavior. We also allow the technology to customize its offering according to its prediction in order to help the consumer save usage cost. In a simple two-period monopoly model with dynamic pricing and random consumer preference, we find that as prediction accuracy increases, the firm initially adopts a conservative pricing strategy and the main effect of smart technologies is to help consumers save usage cost. The technology in this case benefits both the consumer and the firm. When prediction accuracy keeps increasing, the firm starts to raise second-period prices and consumers are more reserved to engage in initial consumption. Anticipating the consumer’s reactions, the firm finds it optimal to lower the first-period price. Under certain conditions, the reduction of this price or the loss of first-period demand can dominate the increase in the firm’s second-period price and lead to a lower total profit and consumer surplus when compared with the traditional technology. When prediction accuracy is near perfection, usage cost savings become the dominant force and smart technology once again outperforms traditional technology in terms of firm profit. Our results suggest that it is not always profitable to increase prediction accuracy as a firm’s inability to commit to low future prices may lead to lower overall profit in the presence of forward-looking consumers. The dynamic inconsistency in our analysis relies heavily and solely on the random nature of consumer needs, and differs significantly from the traditional “ratchet effect” in the behavior-based pricing literature in that profit of smart technology in our model would always be higher than that of traditional technology if the consumer’s need is fixed over time.
Professor Monic Sun's research primarily focuses on the impact of new information technologies in the realm of digital marketing. She has used both theoretical and empirical approaches to investigate strategic interactions between firms and consumers in the context of online product reviews, informative advertising, blogs, social networks and mobile marketing. She is currently exploring the implications of retargeted advertising, smart technologies and peer-to-peer markets. Broadly speaking, she is interested in markets with technological innovations that have significant and often surprising consequences on firms' marketing strategy.
Sun's research has appeared in premier journals such as Marketing Science, Journal of Marketing Research and Management Science and been discussed by popular media such as Forbes, NPR and the Harvard Business Review. Her paper on product ratings was a finalist for the John D.C. Little Award which acknowledges the best paper published in Marketing Science and Management Science. She currently serves as an associate editor for Information Economics and Policy and on the editorial board of Marketing Science and Customer Needs and Solutions.
Prior to joining Questrom, she served on the faculties of Stanford Graduate School of Business and USC Marshall School of Business. She holds a Ph.D. in Economics from Boston University and a B.Econ. from Peking University.
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