Dynamic Pricing of Limited Inventories with Product Returns

Many online retail channels face high rates of product returns. This poses a new challenge to the sellers’ dynamic pricing problem when some returns in good condition can be resold in the selling season. To study the impact of product returns and guide sellers in adjusting pricing policies, we build a product return model by augmenting the classic monopolist’s dynamic pricing framework. We show that the return dynamics can complicate the problem by making it generally not Markovian. We address the technical challenges both analytically and numerically. Our analysis finds that ignoring returns leads to overpricing and can cause significant revenue loss when the demand is high, initial inventory is moderate, product return speed is high, and, intuitively, return probability is high. The analysis yields easy-to-implement heuristic policies that have good and robust performance relative to the theoretical benchmarks. We obtain many important findings for managers. For example, restocking product returns can be highly profitable even when the restocking cost is considerably high. Gaining visibility to customers’ product return decisions, although helpful in forecasting returns and gauging total sellable inventory level, often provides small revenue benefits once the seller properly adjusts its dynamic pricing.