Anna Collins is the chief operating officer of Bulletproof.
Retailers are learning to use software to price and move goods; however, the technology is just at the starting point.
When Goody’s Family Clothing Inc. was acquired last year by two investment firms, its new CEO, Isaac Dabah, charged the retailer’s management team with boosting the company’s profit margins.
The challenge wasn’t taken lightly by merchandise and store managers. Goody’s sells name brands like Nike, Reebok, Levi’s, Adidas and Dockers across nearly 400 stores in 21 states from northern Ohio to Texas and Florida-and margins were already tight under a strict value-pricing strategy. Squeezing better margins from a diverse customer base, the management team realized, would not be easy.
Yet squeeze they must, and with a new web-enabled price optimization system in place, Goody’s management team is now expecting a $17 million improvement in annual profit margins-and the CEO is holding them to it. “Our focus on gross margin return on investment enabled us to build on Goody’s strong foundation and perform according to our financial plans in 2006,” says Dabah, who also is CEO of GMM Capital, which teamed with Prentice Capital Management last year to buy out Goody’s. “We anticipate even better results this year.”
Indeed, if Goody’s continues along the process it began this spring, $17 million will only be the starting point in improving margins, says Dave Smith, vice president of store systems. “We think $17 million is a conservative estimate,” he says.
It’s conservative, he adds, because the retailer’s new Oracle Retail Price Optimization system, which Goody’s is making accessible through web browsers to a team of 30 merchandise buyers, is designed to recommend price points that will move the most merchandise at the highest margin and within planned selling periods.
As with most retail operations, he adds, Goody’s too often has been left with too many products at the end of a planned selling period because prices were not reduced soon enough. Or products sold out too fast because merchants marked down prices too soon, forfeiting sales at higher prices.
“We needed a better way to manage price markdowns so we can make better and more timely offers to customers when demand is still high for a product,” he says. “We don’t want to leave money on the table.”
Goody’s, of course, is not the only retailer facing the challenge of improving margins, and like scores of others it has identified price optimization as an effective tool for sharpening its financial performance. “We’re expecting a significant early payback,” Smith says. Although Goody’s declines to reveal the cost of its markdown optimization application, Oracle notes that price optimization systems typically run more than $1 million.
Price optimization technology has become common at 50 or more large retailers, and several case studies have shown that, if deployed with proper preparation of product data and business processes, it’s effective at improving sell-throughs and profit margins, says Hung LeHong, analyst at research and advisory firm Gartner Inc.
The technology comes in multiple flavors: basic price optimization, markdown optimization and promotion optimization. In addition to Oracle, vendors include SAP AG and DemandTec Inc.
Basic price optimization is used to set initial pricing, often on staple products like detergent and breakfast cereal that don’t usually need to be marked down to sell out during a limited selling season or promotional period.
Promotional optimization, the newest and most complicated, is designed to show how promoting a product at a particular price is likely to impact not only sales and margins but also sales and margins of other products.
Markdown optimization, which Goody’s is using, provides recommendations on when and how much to discount prices on products in order to sell out of them during a planned period at the highest margin.
Markdown optimization has become the most widely used and accepted by merchants, LeHong says, mainly because it addresses a more targeted goal and over a set period of time. “It’s focused on -relatively short selling spans, addressing only the end of the product’s life cycle,” he says.
At Goody’s, an internal review of its financial performance didn’t take long to identify markdown optimization as a useful tool, Smith says.
When CEO Dabah challenged Goody’s managers to improve -margins, it quickly became apparent to them that Goody’s could do better. “We took a new look at improving financial performance and when we looked at sales they were strong,” Smith says.
Yet the management team realized that Goody’s was also leaving too many products unsold at the end of a selling season or promotional period. “When we’re left with too many items in inventory at the end of a season, we have to do something about it,” he adds. “So better management of those inventory dollars became a strong focus.”
Deep into the products
Pulling off that strategy was another matter. The retailer’s merchandise managers had traditionally relied on sales data and their own knowledge of how well certain products sell during particular seasons.
“It had been a very individual merchant-run process,” Smith says. “Merchants did a lot of these calculations based on their own belief on how well products were moving, but they had to work at a high-enough level of product classifications to get it done. It comes down to how much data a person can look at and analyze. When you have 150,000 products, it makes it impossible for merchants to manage those products at super-detailed levels.”
For a markdown strategy to produce a significant improvement in margins, he adds, it must cut across a large volume and range of product information, from a division down to individual SKUs-in other words, from the women’s division, down to women’s knit tops, then to classes of women’s knit tops like short or long sleeves, then subclasses like crew or V-necks, then individual SKUs of particular combinations of class, color and size.
As an apparel retailer with a large number of SKUs, Goody’s decided to go with Oracle Retail Price Optimization, which is hosted on the web by Oracle and based on web-based software developed by ProfitLogic, a company that had built a strong reputation for handling markdowns of short-cycle fashion products before Oracle acquired it in 2005.