By automating its markdown pricing, Goodyâ€™s Family Clothing is able to set the optimal markdown schedules and prices for a greater volume of its merchandise.
Goody's Family Clothing, headquartered in Knoxville, TN, sells moderately priced fashion apparel at 385 stores in 21 states, mainly in the Southeast and Midwest. It stocks national brands such as Dockers, Levi's, Nike, and Reebok in addition to its own labels, Goodclothes and Duck Head. The retailer has long relied on a marketing strategy that includes competitive pricing, a broad inventory, and frequent promotions.
Goody's typically carries 150,000 items at any given time. Each item has an expected life span, based on a projected demand curve, and at the end of its life span, in an ideal world, each would be entirely sold out. Goody's employs a team of merchants who manage the pricing of all of its products over their life spans, including determining when to take markdowns on each product and how much to mark them down. Traditionally, the merchants used detailed historical sales reports and projections, along with their own knowledge, experience, and gut feelings, to decide when and how much to mark items down. However, with so many products to manage, it was impossible to stay on top of each one and implement timely markdown strategies for each. As a result, Goody's was finding that it was left with too much inventory at the end of the season or selling period, because it did not mark down prices soon enough. At the same time, the retailer wanted to avoid marking down prices too soon or by too much, as doing so led to stores being sold out of products too soon, as well as eroding margins.
To improve its ability to manage markdowns on all of its products — and thus maximize the margins on its products over the course of their lives — Goody put together a team to choose a pricing solution. After evaluating several vendors' solutions, the team chose Oracle Retail Price Optimization. According to David Smith, Goody's VP of store systems, Oracle's pricing solution had a more extensive track record of being used by clothing retailers than some of the other applications the company examined. "Managing pricing on fashion apparel is much different from managing pricing of hard-lines goods," explains Smith. "Apparel has a shorter life span, and it changes more drastically from year to year. Oracle had more experience with the types of demand curves that typify our merchandise than did the other vendors."
Historic Sales Data, Consumer Demand Curves Drive Pricing Recommendations
To get the solution up and running, Goody's fed three years' worth of detailed sales and inventory history into the Retail Price Optimization database. This gives the price optimization software a deep history from which to build recommendations and predict sales. Then, Oracle performed a variety of analytics on the data, giving the tool the ability to analyze consumer demand, inventory levels, store performance, and other factors to predict when Goody's should mark down prices and by how much. Before the solution could go live, Smith had to establish a set of business rules that constrain the recommendations it provides, such as how frequently price change recommendations can be made for any given merchandise, how many times over its lifetime a product can be marked down, and how deep the price cuts can go. "The more flexibility you configure the system to have, the better job it can do," says Smith. "But you have to set rules, because, for instance, you can't change every price every day — even though that might give you the best margins on your merchandise. Business practicalities, such as the cost of reticketing merchandise, have to be taken into account."
The price optimization solution compares the demand curves for particular products across classes and subclasses with demand curves for the same or similar products or for other years and selling seasons, then generates markdown recommendations. Merchandise managers can accept, modify, or reject the optimization system's recommendations in order to set price points and issue markdowns that will help to achieve the best possible margins while selling through inventory before the end of the season or product life cycle.
Early Price Change Notification Enables More Efficient Staffing
While Goody's has not yet used the price optimization application through an entire merchandise season, Smith asserts that it is helping the company's merchants develop accurate price changes for a larger number of products than they were previously able. "Each of our merchants has thousands of products to manage," says Smith. "Previously, they would come in on a Monday morning and review the weekend's sales figures. From those, they would generate their markdown recommendations for the week and send them to the stores by Tuesday or Wednesday. The pricing tool does the heavy lifting for them, crunching the numbers at an even more granular level than they are able to do, so they can address more products." The tool forecasts sales for the upcoming week, so the merchants no longer have to wait until Monday before developing their suggestions. As a result, the merchants can get the markdown instructions out to the stores a week ahead of time, giving store managers more time to reticket merchandise, enabling stores to schedule their labor more accurately.
Goody's is currently using the price optimization solution for broad levels of products across the entire chain, but it plans to apply the tool to regional groups of stores in the future. The solution is capable of recommending markdowns as granularly as the individual store level. "Obviously, we wouldn't want to break it down to the individual store," says Smith. "But we have identified climate zones that we are planning to use in the future for markdown purposes."
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