Personalized Pricing and Consumer Deliberation

Description

Nowadays, firms across a wide range of industries are collecting large amounts of data from their consumers, such as their click-stream data, purchase history, and social media data. Given these rich data sources and advanced data analytics tools, firms may know better about consumer preferences than the consumers do themselves (Xu and Dukes 2019a, 2019b). Firms know both their products’ attributes and consumer preferences, according to their behavioral data; then, using AI algorithms, they can evaluate how a particular product fits consumer needs and interests.Consumers, in contrast, have no prior experience with a new product and have to invest in cognitive efforts (e.g., recalling past experiences, simulating potential usage scenarios) to find out the product’s true valuation. This process is known as consumer deliberation (Wathieu and Bertini 2007, Guo and Zhang 2012).In this proposed study, we examine a firm’s optimal pricing strategy when it has superior information about consumer preferences while consumers incur a deliberation cost to find out the true valuation of the firm’s product. We argue that (1) the firm often has incentive to abuse information advantage to trick consumers with low valuation to overpay for its product, (2) confronted with a high price, consumers become skeptical of the firm’s offers and deliberate to find out the product’s true valuation, and (3) consumer deliberation brings about a deadweight loss to the system, which hurts both firm profits and social welfare. Thus, under certain circumstances, the firm’s data advantage can backfire on its own profit.We will develop a game-theoretic model to understand the firm’s optimal pricing strategies. We allow the firm to offer consumers personalized prices according to their willingness to pay (e.g., offering consumers targeted coupons), and consumers make rational inferences about a product’s value from the charged prices. The model can be used by firms to design better pricing strategies in the era of big data and guides them on ways to effectively collect and use data.In addition to designing pricing strategies that maximize firm profit, we will also propose marketing strategies (e.g., nondiscriminatory pricing) that curb firms’ incentive to abuse information advantage and also alleviate consumer suspicion, thereby reducing consumer deliberation and improving social welfare. Finally, our study will guide public policy makers on ways to regulate data collection and protect consumer welfare.