Herding, Learning and Incentives for Online Reviews

Abstract

We investigate the role of consumer herding and learning on the design of incentives for online customer reviews. Herding occurs when consumers are drawn to a product that appears to be popular because it has garnered a large number of reviews. Learning occurs when consumers infer product quality from reviews. We evaluate and compare three incentive policies. The first announces an incentive to all customers before purchase, the second offers an incentive after purchase, and the third rewards buyers only if they write positive, possibly fake, reviews. We use a generalized Polya urn process to model the evolution of reviews. The expected value of the resulting aggregate demand has the form of the Gompertz function. We obtain conditions under which each type of incentive is profitable, and preferred by a seller to the other incentives for reviews. The results imply that sellers should use different incentives policies depending on the quality and profit margin of a product. A pre-purchase incentive is the most profitable when product quality and profit margin are both high; an incentive offered to buyers after obtaining voluntary reviews is the most profitable when product quality is high and profit margin is low; and an incentive for only positive reviews is the most profitable when product quality and profit margin are both low. E-commerce platforms that limit their sellers to using post-purchase incentives might be more effective in curbing fake reviews if they also allow sellers to announce pre-purchase incentives to all customers.

Keywords: Customer reviews; incentive design; online platforms; e-commerce; herding; learning; diffusion; stochastic processes; Polya urn.