Dynamic pricing and inventory management in the presence of online reviews

We study the joint pricing and inventory management problem in the presence of online customer reviews. Customers who purchase the product may post reviews that would influence future customers' purchasing behaviors. We develop a stochastic joint pricing and inventory management model to characterize the optimal policy in the presence of online reviews. We show that a rating-dependent base-stock/list-price policy is optimal. Interestingly, we can reduce the dynamic program that characterizes the optimal policy to one with a single-dimensional state space (the aggregate net rating). The presence of online reviews gives rise to the trade-off between generating current profits and inducing future demands, thus having several important implications for the firm's operations decisions. First, online reviews drive the firm to deliver a better service and attract more customers to post a review. Hence, the safety-stock and base-stock levels are higher in the presence of online reviews. Second, the evolution of the aggregate net rating process follows a mean-reverting pattern: When the current rating is low (respectively, high), it has an increasing (respectively, decreasing) trend in expectation. Third, although myopic profit optimization leads to significant optimality losses in the presence of online reviews, balancing current profits, and near-future demands suffices to exploit the network effect induced by online reviews. We propose a dynamic look-ahead heuristic policy that leverages this idea well and achieves small optimality gaps that decay exponentially in the length of the look-ahead time window. Finally, we develop a general paid-review strategy, which provides monetary incentives for customers to leave reviews. This strategy facilitates the retailer to (partially) separately generate current profits and induce future demands via the network effect of online reviews.