Dynamic pricing with Bayesian updates from online reviews
This work addresses pricing uncertainty for firms launching new products, offering a method to leverage online reviews for better pricing decisions, though it appears incremental as it builds on existing bandit and Bayesian update frameworks.
The paper tackles the problem of pricing new products under quality uncertainty by modeling a seller's dynamic pricing strategy as a bandit problem, using Bayesian updates from online reviews to adjust prices, and shows that this approach allows efficient computation of future rewards via a connection to Catalan numbers, comparing optimal static and dynamic strategies for learning product quality.
When launching new products, firms face uncertainty about market reception. Online reviews provide valuable information not only to consumers but also to firms, allowing firms to adjust the product characteristics, including its selling price. In this paper, we consider a pricing model with online reviews in which the quality of the product is uncertain, and both the seller and the buyers Bayesianly update their beliefs to make purchasing & pricing decisions. We model the seller's pricing problem as a basic bandits' problem and show a close connection with the celebrated Catalan numbers, allowing us to efficiently compute the overall future discounted reward of the seller. With this tool, we analyze and compare the optimal static and dynamic pricing strategies in terms of the probability of effectively learning the quality of the product.