When should sellers offer rebates for consumer reviews

Online sellers often use rebates as a means of inducing consumers to share reviews of products, services and experiences so as to optimize the benefits of those reviews. There are generally two formats for the timing of rebate offers, broad and targeted, depending on whether the seller provides review-based rebates to all potential consumers up front (broad) or only to consumers who have bought the item but have not yet reviewed it (targeted). Within a Bayesian social learning framework, this paper develops game theoretical models to analyze the seller's optimal rebate provision strategy for promoting a new product. We show that with dynamic pricing policy, while broad rebate provision can incentivize early-arriving consumers to purchase, the seller incurs the wasteful expense of offering rebates to those consumers who are already actively sharing reviews. While targeted rebate cannot enhance consumer purchase utility, it is more effective at eliciting reviews from consumers who are inherently hesitant to share them. In equilibrium, the seller provides rebates only if the market contains a limited number of consumers that like to share reviews. Moreover, we show that the seller may offer rebates more proactively under targeted format when it becomes more costly for consumers to write high quality reviews. However, if review quality is influenced by consumer types, such that consumers who are more inclined to share reviews generally produce higher-quality ones than those less inclined to do so, and the seller does not require reviews to be informative for rebate redemption, the broad format consistently offers a stronger incentive for the seller to provide rebates than the targeted format. Interestingly, with a fixed pricing policy, the seller provides rebates only if the market contains more consumers who like to share reviews, which contrasts with the dynamic pricing policy.