The athletic apparel retailer also boosts site visits by 50% using customer analytics platform AgilOne.
The right price at the right time and place can improve conversion rates and sales.
With another college semester underway, the folks at eCampus.com are in peak operating mode. They sell and rent college textbooks—a business challenged not only by seasonal surges in demand, but also by quickly changing inventory. A textbook high in demand one semester can be worthless the next.
So it's crucial to price products to grab a sale when demand is hot. And there's little room for error. After all, the retailer's customers are typically penny-pinching college students probably more interested in blowing their dough on the next rock concert.
"Our job is to make our books affordable and easy to purchase," says Matthew Taylor, director of online marketing and business development. "Selling textbooks is a tricky game to play. Inventory can go stale quickly; new editions come out, and old ones are not needed."
One way eCampus locks in sales is through dynamically generated pricing offers based on customers' recent shopping behavior. If a shopper puts $80 worth of books into the eCampus.com shopping cart but leaves without completing a purchase—clicking, say, to a web site selling tickets to see Lady Gaga—eCampus may use SteelHouse to serve a follow-up ad on that site.
And instead of that ad simply showing a generic offer, such as "Come back to eCampus for a 10% discount," it will specifically address the shopper's just-ended eCampus shopping visit—in this case the aborted attempt to buy $80 worth of books. The personalized offer might say: "Get $4 off an order of $80 on eCampus."
That combination of a quick follow-up and a pricing promotion tied to a shopper's particular shopping experience has made a big difference. "Clickthrough rates are astronomically higher—50 to 100 times higher—than on standard display ads when we match a shopper's recent shopping behavior with the offer we're giving them," Taylor says. The subsequent conversion rates are also far higher than on less-targeted display ads, helping eCampus increase sales 70% this year over last year, he adds.
SteelHouse hosts its technology on the web for clients, who pay the company a percentage of sales driven by SteelHouse-supported promotions. Greg Girard, program director of retail merchandise strategies at research and advisory firm IDC Retail Insights, says he knows of no other company offering similar marketing technology outside of far more expensive and complex systems from companies such as SAS Institute and Fair Isaac Corp.
The ability to offer multiple and even quickly changing pricing promotions based on any number of criteria—a shopper's recent or long-term shopping behavior, geographic location, time of day, among others—can make all the difference in how well consumers respond, experts say. "A lot of times consumers have a bank of price offers before them, but often many of them aren't personalized or timed well enough," Girard says.
But offering different prices to different consumers entails risks, especially when consumers catch on.
Sportswear maker and marketer Adidas Group, for example, found itself lambasted in news reports and on the Internet this past summer when its popular rugby jerseys for the World Cup and New Zealand's All Blacks rugby team were selling in New Zealand stores for about $200, more than twice the price on some foreign e-commerce sites. Adidas declined to comment on the controversy.
In other cases, merchants have found that differentiated pricing just wasn't worth the trouble. That was the case at Tool King LLC, a retailer of power tools and accessories that sells mostly online and operates a single store near Denver.
Tool King sells through several comparison shopping engines and e-marketplaces in addition to ToolKing.com. The retailer a couple of years ago developed a system that varied prices based on the online venue where the product was being sold or by time period.
But managing partner Donald Cohen found the system too difficult to manage and keep up to date. "There were things that we should've raised prices on but never did, or things that should've had prices lowered but weren't," he says.
The retailer now operates with two pricing levels: one for its single store, where prices are higher than online, and one for all online channels. "All of our online pricing now is the same, it simplifies everything ," he says.
Matching price and demand
Other retailers, including big retail chains, take the opposite approach and deploy web-enabled price management systems from companies like KSS Retail and the Oracle Retail division of Oracle Corp. These systems, for which a large chain might spend about $1 million, according to KSS, will recommend pricing based on expected consumer demand and available levels of inventory, and also project how particular pricing will result in changes in key metrics like conversion rates and sales.
By incorporating analytics data on consumers' shopping behavior online and in stores as well as other information on consumer demographics, retailers can vary price offers by customer segments as well as by retail channels and geographical areas, says Lyle Walker, vice president of marketing at KSS Retail and a former merchandising executive at Kroger Co., a major supermarket chain.
Walker notes that, in the past, retailers were more likely to set prices by store or region, based on consumer demographics or their competition. But now they can use systems such as KSS to learn about customers' purchasing behavior across online and offline shopping channels, and use the system's built-in predictive analytics to project how consumers will respond to a particular price.
"A retailer can now project, if it advertises a 2-liter bottle of Coke for $1.99, how much it will sell across different stores, but also how it will impact sales across different segments of customers," he says. Merchants can then e-mail or direct-mail customers customized pricing offers that they can redeem with coupons or loyalty cards.
The KSS system pulls data from multiple sources to produce recommended prices in real time, instead of taking a day more with older types of systems. That's made possible by using the XML programming language in concert with a schema known as service-oriented architecture designed to enable applications to share data with each other.