Incorporating Consumer Ratings in Retailers’ Discount Pricing of Digital Goods

Retailers of digital goods often use discount pricing to attract consumers. To make an effective promotion, they naturally want to understand consumers’ valuation. Nonetheless, rigorous research is lacking on how to use consumer ratings on the retailer side. Our study aims to fill this research gap by investigating how retailers can determine optimal discount size in response to consumer ratings. We use both an analytical model and an empirical analysis. Our analytical results showed that discount size decreases with consumer ratings for non-supreme ratings. Nevertheless, there is no significant impact for supreme ratings. In addition, we find that consumers’ confidence and the regular price of digital goods are critical moderators. Using a unique dataset of 419 online audiobooks, we empirically test the proposed hypotheses. The predictions of our model are consistent with empirical evidence. Our study demonstrates that retailers can provide smaller discounts when consumers give higher ratings of digital goods. In addition, consumers’ confidence enlarges the consumer rating effect, while the regular price reduces such effect. Our findings can be applied to other digital goods such as digital movies, software/APP and online newspapers.