Women’s clothing brand Roman Originals has been inundated by calls since the photo became the center of an online debate.
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By combining data on daily store sales with web browsing data, hhgregg learned, for example, how some customers purchased TVs or other items in stores, including the specific model and the price, after having researched similar products on hhgregg.com. Knowing which price points and model a customer finally purchased in the store, he adds, has enabled the retailer to sharpen its online merchandising to capture more sales online, Tobias says.
Gilt reaches out
Gilt Groupe, the members-only, flash-sale retailer of designer fashions, has used analytics data to expand its market reach after it figured out it was missing the mark in terms of persuading women to buy men's goods. "Women tend to buy stuff for men, but we were under-performing," says Zucker, the chief marketing officer.
To turn that around, Gilt worked with customer analytics data compiled by Mu Sigma, the retailer's analytics service provider, to build predictive models of women shoppers most likely to respond to pitches for men's products.
Mu Sigma combined such data as demographic profiles of women known to purchase men's products with information about how women shop on Gilt.com. Mu Sigma then helped to identify segments of shoppers most likely to respond to particular promotions. Women within certain age and income ranges, and from particular areas of the United States, for example, might be more likely than other women to shop for gifts such as men's cufflinks.
Zucker adds that Gilt also knew from web site analytics data that many women shoppers do their impulse buying at noonÑwhich turned out to be a good time to present them with offers for men's products.
When Gilt sends e-mail offers for men's products to segments of female shoppers that it deems mostly likely to respond, the online retailer experiences a 10% to 25% increase in the rate of shoppers who respond to those offers and make a purchase, Zucker says.
While Gilt has developed in just a few years into a top-50 online retailer, with 2010 web sales of $425 million by Internet Retailer estimates, much smaller e-retailers also are finding ways to use analytics data effectively. CastCoverz, a small web-only retailer that sells decorative and waterproof covers for medical casts placed on broken arms and legs, has learned from its analytics applications that what it had thought of as a tiny niche is bigger than expected, and that consumers shop for these products in unanticipated ways.
"When building this business I had expected it to be a lot of one-time sales, and that I wouldn't have a lot of repeat customers—but I was wrong," says Annette Giacomazzi, who launched CastCoverz.com after her daughter needed a cast for a broken bone. Analytics, she says, has enabled her to build customers' interest in a variety of products and generate repeat orders, she says.
Giacomazzi relies on constant and real-time updates of online shopping activity on her site that she can view through an analytics dashboard from Spring Metrics, a company that caters to small retailers. In addition to showing which Internet search terms visitors may have used to arrive at CastCoverz.com, the dashboard lets Giacomazzi drill down into the actual on-site browsing paths shoppers took, and whether those paths led to a sale. She can also see a visitor's geographic location.
The dashboard, which she pulls up on her computer screen daily, also continually updates the number of visitors to the site and sales conversion rates. "I can watch all this in real time," Giacomazzi says.
Giacomazzi reads off details of a recent site visit: "From Stamford, Conn., on the site for 17 minutes, 18 seconds, viewed 10 pages," she says. "It shows how much time spent on each page, and how the visitor went to sections on Legs, Boots, and About Us, but not to Arms."
By combining that kind of specific visitor information with broader aggregated visitor data, including conversion rates and sales, she says, CastCoverz learns about consumer interest in product mixes, and demand from different regions of the country. By monitoring how many visitors click the About Us section, she learns how many visitors want to know more about the company as well as its products.
The retailer also gathers customers' e-mail addresses when they check out with Google Checkout, the only payment method offered on its site. That helps it track customers' repeat activity. And it learns through social media and other communications with customers that many come back repeatedly to buy cast covers as gifts, such as when a friend or someone in their son's or daughter's local high school suffers a sports injury.
By combining such information with Spring Metrics data on how visitors arrive at its site and shop for products, CastCoverz can more effectively promote product combinations on its site and through targeted marketing campaigns, Giacomazzi says. For example, it may promote packages of products likely to sell in certain regions and during particular seasons, such as decorative and waterproof leg-cast covers promoted in winter e-mails to customers in northern areas, a customer segment that orders leg cast covers more often than consumers who live in less icy climes.
"It's all about selling to customers when they're ready to buy," Giacomazzi says.
That's what analytics can help retailers do, if they've got the technology and expertise to bring their data to heel.