When is the price right?

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.

Moving merchandise

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.

Measuring performance

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.

“This price optimization tool allows a merchandiser to manage products at a level they never could before because there just aren’t enough hours in a day,” Smith says. “The tool does the number crunching for us. It compares the demand curves for particular products across classes and sub-classes with other demand curves for other products or for other years and selling seasons.”

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Merchandise managers are free to either accept or reject the optimization system’s recommendations, and to implement recommended price changes either immediately or gradually until the recommended target date, though not without the company holding them accountable for the final performance of a pricing strategy. “We’ll report the history on what was recommended, what the buyer did, and call attention to how we’re performing,” Smith says.

Getting the system to produce good recommendations, however, takes extensive and careful preparation. While preparing its system, the Goody’s I.T. team transferred three years’ worth of product sales data—2004 through 2006—to Oracle, a process that took nearly five weeks.

Then the retailer’s merchandise and store operations managers spent about three weeks in daily 3- to 4-hour meetings to establish the business rules to regulate the markdown recommendation process, such as setting minimum improvements to margins in order for the system to recommend a markdown. Other rules require the system to factor in the costs of labor and materials used in executing markdown recommendations.

“A lot of work went into these meetings to set business rules with a great deal of involvement from people in merchandising and store operations,” Smith says, adding that the planning process helped to win buy-in from merchandise buyers and store personnel.

Involving store operations staff as well as merchandisers also helps to determine just how granular and flexible to get in applying business rules across product lines and store locations, Smith says. One of the biggest challenges in deploying a price optimization system, experts say, is deciding how far to drill down into product data across classification and SKUs as well as into regions and subgroups of stores, or even individual stores. The more detailed the data on products and stores analyzed by the system, the more precise the recommendations on how, when and where to change prices.

“If we limit the system to chain-wide data, it can only recommend optimized prices across the entire chain,” Smith says. “But the more flexible we are with the business rules in analyzing data throughout product lines and individual stores, the better Oracle can make recommendations.”

Value of recommendations

The value of recommendations applied to more products and stores, however, must be weighed against the additional time and costs involved in executing the -recommendations, he adds. “The offset is the question of whether more granular recommendations can be executed in the stores, and that’s a discussion that’s been going on for some time—how granular to go with the recommendations,” Smith says. “If we say the Oracle system can mark down products on any SKU on any day in any store, we’d be doing nothing but changing prices every day.”

And after factoring in the cost of labor and materials involved in changing the price tags on marked-down SKUs in individual stores, the net effect on margins might not be as favorable as initially expected, Smith says.

One step at a time

Goody’s will take it a step at a time, he adds, first getting comfortable using the system’s price recommendations for broad levels of product classifications across the entire chain, then experimenting with recommendations for sub-classes of products and across regions of stores.

“We’ll look for the lowest level in the product hierarchy where our team feels the demand curve is accurately portrayed, though this can differ from one apparel department to another,” he says, adding that it will compare the value of such information against the cost of execution.

In extending the recommendations into small groups of stores, Goody’s expects to mostly focus on groups with similar weather patterns for analyzing common demand trends as applied to pricing strategies. “We probably won’t go lower than climatic zones,” Smith says.

Reaching a high level of performance in the use of optimization systems, however, also takes careful planning in other areas, experts say. Although many retailers have reported good results, “we also know of large retailers where it hasn’t worked as well,” LeHong says, declining to name the under-performers.

To make these systems work, he and others say, retailers need to have accurate product data throughout their enterprise software applications and trust in the ability of the optimization technology to make worthwhile recommendations on when and how much to change prices.

“Any price optimization system can’t work without a good data repository and central data management,” says Sahir Anand, retail industry analyst with research and advisory firm Aberdeen Group, a unit of Harte-Hanks Inc.

With accurate data synchronized across the enterprise, including consistent product definitions and sales data, an optimization system is capable of producing usable price recommendations. Without accurate data, merchants will never come to believe those recommendations, experts say.

“It boils down to trust,” LeHong says. “For merchants who have been planning pricing strategies with spreadsheets and a gut feeling about sales trends, it can take time to build trust in a software application’s recommendations. But these applications are complicated, so you have to use them, then do your homework and check the recommendations and see if they held true.”

Non-stop learning

The more Goody’s uses its price optimization system, the more it expects to find new ways to apply its information, Smith says.

For example, retailers may decide to run markdowns more or less frequently based on customer response to markdowns in month-to-month or year-to-year comparisons, which can also be analyzed in the system.

“If a retailer marks down men’s pants once a month, but finds customers over extended periods very responsive to price changes, it might make more sense to do markdowns more frequently,” says Chris Morrison, vice president of planning and optimization solutions for Oracle.

Smith adds that Goody’s is beginning to gain from the price optimization system better insight into overall product demand curves, which in turn may lead to improvements in how Goody’s works with suppliers to deliver products that will produce the highest margins and sell-throughs. “It’s opened our eyes to other ways of managing and understanding our entire demand curve and understanding the changing elasticity of it,” Smith says.