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But ProfitLogic’s software went beyond the traditional forecasting methods and the manual processing of data. It added the rigor of more data points and a speedier analysis of the relationship between them to suggest a different scenario. The software determined that demand for the shirt was not likely to fall off until week 11, and recommended delaying the markdown until week 12. It also recommended that the price be taken down to a different level. Continuing analysis by ProfitLogic suggested an additional cut in week 15. The traditional approach, meanwhile, suggested a markdown in week 17, and a third one even later when the price cuts still didn’t clear the inventory. The bottom line? Implementing the two strategic price cuts recommended by ProfitLogic achieved total unit sales of 1,711 versus 1,508 using the conventional approach, at gross margins that outstripped it $23,270 to $20,622.
ProfitLogic marketing director Julie Driscoll says that while FashionStyle is a brick-and-mortar retailer, “The mathematics, how merchandise behaves, and how analytics are used to inform pricing hold across channels.”
A similar story from McKinsey’s Zawada supports that. He cites a consumer electronics retailer that used analytics software to dig deeper than traditional forecasting methods into the online sales trend for a new product. The software program judged that the sales trend would support full-price sales for two weeks longer than anticipated earlier. While Zawada isn’t disclosing the retailer or the analytics vendor, the numbers tell a compelling story on their own. The retailer increased profit on the product by 17% with its decision to delay the scheduled markdown, he says.
“Price optimization technology offers companies the ability to establish the perfect price points on an item-by-item basis in order to maximize profits,” says Paul Ritter, an analyst with The Yankee Group, Boston. “The beauty of the Internet as a sales channel is the ability it offers to respond in real time to changing inventory levels, consumer demand, and other factors that influence pricing and buying decisions, and to do it on an almost infinitely scalable basis.”
In theory, that is, for the opportunity does bear challenges as well. Online shoppers have come to expect dynamic pricing at auction and accept rapid price changes on sale merchandise, but analysts believe they’re less tolerant of prices that change online outside of those situations. Then there’s the problem of channel conflict. Price consistency among channels remains a major issue for multi-channel retailers who now dominate online.
Early on, when Internet retailing was populated mostly by pure-plays, the web was known to consumers as a place to get great deals as merchants gave away the store in a bid to acquire traffic and customers. But with multi-channel merchants now the big players online, web prices are more often anchored in some way to store and catalog prices.
“We haven’t seen a desire on retailers’ part to vary Internet pricing. Instead, they’re treating the Internet almost if it’s another store location,” Verheuvel says. “The intelligence they gather online is being evaluated as a kind of early warning signal. They can use the Internet to understand price sensitivity and whether a new product will move before they put it into stores.”
But that’s web price versus prices in other sales channels. What about a pricing experiment that pits web prices not against those in the merchant’s other channels, but against different web prices-offers on the same merchandise, but at different times and under different conditions-on the merchant’s own site?
This gets close to the exercise that got Amazon into trouble, but retailers have learned a few things since then. Whether merchants that test online pricing in this way wind up with valuable information or just egg on their face is to a large extent a function of how they present the different prices to consumers, experts say.
Companies are uncomfortable for the most part in varying prices on the web, says Zawada, but many are finding ways to do price research while moving merchandise online in ways that minimize risk. “One example is the office supply industry,” he says. “For years, it’s been part of their offline strategy to have zoned pricing-it’s accepted. So some now have zoned pricing on the web. When shoppers sign on, they provide their zip codes. That gives them pricing consistent with store pricing for their region.”
The timing of online price changes also is critical to consumer response. Companies are bypassing random price testing among individual online shoppers to experiment with time as the basis for segmentation. That means shoppers who buy one week could get a different price from shoppers who buy a week later. It’s an approach that’s riskier for some product categories than others.
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If a product on a retail site has high price recall among consumers and gets a lot of repeat visits prior to purchase, a wide-screen TV, for example, it’s going to be more difficult to change the price. In such a case, “You want to extend the test for a longer period,” says Zawada. “You might also want to mitigate the risk of a customer having a problem with it by giving him some kind of reward. You don’t have to tell them why you’re doing it, but you’ve given them something in recognition.”
By contrast, specialty retailers of products such as giftware and gadgets face fewer constraints. Less heavily researched week to week, and less frequently purchased, they’re tied to a specific gift giving event anchored in time and little affected by price changes.
In experimenting with online pricing, it’s a rule of thumb that prices that are labeled “promotions” go over better with customers than outright price changes, though interestingly, the revenue impact on a retailer might be the same. “From the customer perspective, a promotion versus a different price tends to be more acceptable,” says Lisa Plaskow, director of revenue and pricing optimization at Manugistics. Merchants also are seeing that it’s less risky to vary pricing by customer segment than by customer. “It’s not very acceptable to change price based on the individual customer. But retailers are finding that it may be more acceptable to offer different promotions to different types of customers. More loyal customers, for example, might get a different promotion than those who don’t order as much,” says Plaskow.