Style goods pricing with demand learning

For many industries (e.g., apparel retailing) managing demand through price adjustments is often the only tool left to companies once the replenishment decisions are made. A significant amount of demand uncertainty can be resolved using the early sales information. In this study, a Bayesian approach is used to summarize sales information and pricing history in an efficient way which then feeds into a periodic pricing model to optimize revenues for a given stock items over a finite horizon. It is shown that the optimal price in a given period is a non–decreasing function of sales in the earlier periods when demand learning takes place. A computational study is carried out in order to find out the circumstances under which learning is most beneficial. Finally, the model is extended to allow for replenishments within the season, in order to understand global sourcing decisions made by apparel retailers. Findings are empirically validated using data from U.S. apparel industry.