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When the leaves start to change colors in autumn, they signal to fashion-minded shoppers to begin their search for the perfect new fall coat. But for some buyers, price trumps being the first to wear this year’s trends; and it is these consumers who ultimately wait, before parting with their cash, for retailers to slash prices. Retailers, of course, for their part want as many shoppers as possible to make their purchases before prices are decreased.
When it comes to buying and selling goods with a limited life cycle—such as fashion apparel, concert tickets, and holiday merchandise—shoppers and retailers face different dilemmas. Shoppers must decide whether to buy early in the season at a higher price or later in the season after a markdown. Buying later poses a trade-off because while the product will be cheaper, shoppers will have less time to use it. Meanwhile, sellers must decide how much to order, how to get the shoppers to buy at higher prices, and when and by how much to mark products down.
Lakshman Krishnamurthi, a professor of marketing at the Kellogg School of Management, sought to better understand these dual dilemmas by studying actual sales data from a national specialty apparel retailer. Krishnamurthi, along with his student Gonca Soysal, now a professor at the University of Texas at Dallas, gained some key insights by analyzing two years’ worth of data on the sales and inventory levels of hundreds of products, such as coats. They designed a structural model that accounts for a shopper’s expectations and buying behavior based on patterns that emerged from the data. Their model assumes that consumers know the current prices of products, and have a sense of the stocking levels, letting them form expectations about future prices and availability.
About the Model
Krishnamurthi and Soysal treated each type of coat as a separate market—for example, someone looking to buy a leisure coat is not likely to buy a coat suitable for office work instead. The retailer whose data they mined, whose coats are initially priced between $100 and $350, has an established history of offering at least two to three markdowns as each season progresses, with seasons varying from 11 to 30 weeks long. The first markdown is the largest (38 percent of retail price on average) and has the biggest effect in terms of creating a spike in demand. The second and third markdowns are not as large and always have smaller effects on sales.
When coats sold at the full retail price, the retailer moved 43 percent of their inventory, which amounted to 57 percent of total revenue. Another 45 percent of the inventory, or 36 percent of revenue, sold after the prices were marked down to between 40 and 80 percent of the full retail price. (The retailer typically applied deeper markdowns on coats with higher initial prices.)
The model revealed some key characteristics about the typical consumers patronizing the retailer in the study. “We discovered that there were two kinds of buyers. First, there were fashion-sensitive buyers, and these make up about 80 percent of the sales,” Krishnamurthi says. “They are very important to the retailer’s profits. Then there were price-sensitive buyers, and while these make up only 20 percent of the sales, they are pivotal to soaking up excess inventory.” They also learned that the composition of the market changed as the season progressed, from the fashion-sensitive buyers early in the season to the more price-sensitive buyers later in the season.Consumers were assumed to exit the market after making their purchase.
What Is a Retailer to Do?
These findings lead to a key question for the retailer: How do you manage prices to maximize overall revenue? Offering a markdown too early in the season or slashing prices by too much can harm sales. Conversely, waiting until the product’s utility has diminished too severely may discourage price-sensitive shoppers from snapping up the remaining inventory.
By running a series of counterfactual experiments, Krishnamurthi and Soysal discovered a counterintuitive result. They showed that retailers could increase their profits by inducing scarcity to convert some late-season price-sensitive buyers to early season buyers. By slightly reducing their inventory—thereby creating a sense of urgency for shoppers to buy earlier in the season—the retailer would be able to increase the number of sales taking place at higher prices. This occurs because shoppers who are knowledgeable about how much stock remains will fear that the product may not be available if they wait too long to make their purchase.
“By slightly reducing their inventory—thereby creating a sense of urgency for shoppers to buy earlier in the season—the retailer would be able to increase the number of sales taking place at higher prices.”
“When we reduced stock by just 5 percent, it increased profits by 4.5 percent,” Krishnamurthi says. “But reducing stock by 10 percent yielded only a 3-percent gain in profits. Ultimately, each retailer will have to experiment with stocking levels to see what level of reduction maximizes their own profits.”
Additional experimental scenarios revealed that the retailer was better off offering smaller markdowns earlier in the season rather than large markdowns late in the season. The experiments showed that when small markdowns were offered earlier, strategic buyers caused, on average, a 9 percent dip in sales revenue if there was a risk of stock running out. But this dip was as high as 35 percent of the sales revenue if large markdowns were offered a little later in the season and there was no risk of stock running out. Again, Krishnamurthi points out that individual retailers will have to experiment with their own numbers to see what percentage markdown works best for them.
Krishnamurthi says this research may be unique within the revenue management literature that deals with demand and pricing strategies for seasonal goods because it develops a realistic demand model that accounts for variability among consumers and their behaviors. It is also unique, he says, in that it is, to his knowledge, the first empirically based model that takes into account the impact of limited product availability on consumer decision making.
The findings can be applied beyond the fashion industry, Krishnamurthi says, to any product that is sold over a short life cycle. With the roles of buyers and sellers now decoded, it just may be a seller’s market in the fall.
Related reading on Kellogg Insight
Soysal, Gonca P., and Lakshman Krishnamurthi. 2012. “Demand Dynamics in the Seasonal Goods Industry: An Empirical Analysis.” Marketing Science 31(2): 293–316.
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