Abstract
The results of an analysis of sales and price data from a speciality retailer of women's apparel are reported. The data set contains 184 styles sold during the Spring 1993 season. A demand model similar to those in the existing literature is hypothesised, fit to the data, and then analysed to obtain estimates of revenues under various pricing policies. Both full information and adaptive policies are considered. The optimal prices suggested by the models are compared with those of the study company and the revenues generated by various policies are estimated. The analysis suggests that if the firm had made smaller mark-downs earlier in the sales season, it would have increased its revenues significantly. The results also indicate that model-based pricing schemes can potentially increase revenue by approximately 4 per cent.