Guang Li
Academic Director (Master of Management Analytics), Associate Professor & Distinguished Research and Teaching Fellow of Management Analytics
- Adapted from: “Pricing in the Presence of Strategic Consumers and Social Learning Under Contingent Pricing and Price”
- Based on Research by: Zhong-Zhong Jiang, Jinlong Zhao, Zelong Yi, Ying-Ju Chen, Guang Li
- Journal: Production and Operations Management
Key Takeaways
- Online reviews encourage customers to wait for more information, forcing firms to lower initial prices, but positive reviews enable higher later prices that ultimately benefit both companies and consumers.
- Underprice guarantee policies, high initial prices combined with expected discounts, can attract quality-conscious early buyers who leave positive reviews, challenging the belief that price skimming only works with price-insensitive customers.
- While flexible pricing typically outperforms price guarantees, social learning reverses this for highly patient consumers, making price-matching policies powerful revenue management tools rather than merely defensive strategies.
In a world where over 90% of consumers check online reviews before buying, understanding how these reviews reshape pricing strategies has become critical for business survival. This research investigates how firms should price new experience products—items like cosmetics, electronics and books where quality is uncertain upfront—when strategic consumers can delay purchases to gather information from reviews.
The study focuses on comparing two widely used pricing approaches: contingent pricing (dynamically adjusting prices like Amazon does), and price guarantee schemes (offering refunds if prices drop, as Best Buy and Walmart do). The researchers developed a two-period mathematical model where a monopolistic firm sells to consumers with varying patience levels and preference heterogeneity.
They analyzed scenarios both with and without social learning, assuming consumers exhibit bounded rationality by focusing on aggregate positive and negative review counts rather than processing detailed review sequences. Using backward induction and symbolic computation software, they derived optimal pricing strategies under each scheme and compared firm profitability and consumer welfare across different levels of consumer patience.
The findings reveal that social learning enables dramatically different pricing strategies depending on the scheme and consumer patience. Under contingent pricing, social learning creates win-win outcomes for moderately patient consumers, with firms lowering initial prices to encourage reviews, but charging premium prices when reviews are positive. Surprisingly, with price guarantees, firms can successfully use price skimming even when all consumers are price-sensitive—a strategy previously thought viable only with mixed customer types. For highly patient consumers, price guarantees combined with social learning actually outperform flexible pricing, as high initial prices improve review quality without deterring purchases.
These insights help explain why major retailers maintain price-matching policies during promotional events and suggest that the optimal pricing approach fundamentally depends on the strategic sophistication of a firm's customer base.