Dynamic pricing of electronic products with consumer reviews

Consumer reviews have become pervasive for e-commerce in recent years, especially for electronic products. In this paper, we investigate the optimal pricing strategies for a platform selling electronic products when consumers sequentially learn about product quality from consumer reviews. We focus on the transient analysis to calibrate how information externalities across the time dimension would distort the seller’s optimal pricing strategies. Facing the “cold start” problem, the seller of high-quality products would choose lower prices to speed up the consumer learning process. Consequently, the optimal prices suffer from downward distortions that increase in product quality in this reputation-riding regime. In the extensions, we propose a tractable and flexible framework to support both operational and strategic decision-making processes. We explore the value of persuasive advertisement, and the results suggest that consumer reviews and marketing efforts are strategic substitutes. In terms of quality control, we find that it would be optimal to invest in quality in the early stages, but stop at a certain time threshold, resulting in a reputation-building and reputation-spending pattern. Finally, we extend the framework to study a duopoly pricing problem. We show that the high-quality seller could strategically accommodate the low-quality seller in the early stages, and wages a price war at later stages.