An empirical analysis of demand variations and markdown policies for fashion retailers

In this paper, using data from a leading specialty apparel retailer, we empirically examine the determinants of a retailer's dynamic pricing policy and investigate consumer response to price changes (markdowns) throughout a fashion product's selling season using a product diffusion setting. In order to do that, we first develop and estimate a markdown pricing model and a consumer demand model that capture the important characteristics of the fashion apparel market. Next, we use the estimates from these two models to design and simulate four alternative markdown pricing policies to investigate the impact of these different policies on consumer demand and retailer revenues. Our results, in line with the previous literature, show that markdowns implemented early in the season but small in magnitude generate the highest retailer revenues. Our paper not only provides a comprehensive empirical framework for fashion apparel retailers that is easy to implement, but also shows that using this framework will lead to timely decision making and will improve sale and revenue outcomes in the fast paced fashion world.