The women’s footwear retailer launched more than five years ago under Nordstrom’s off-price HauteLook brand.
Retailers can target a customer now based on what she bought a year ago.
Personalization services vendor MyBuys Inc. this week announced the release of a new marketing tool called Big Data Long-Term Targeting. With it, retailers can use consumer profile information from the past and integrate it with real-time information to recommend products. The tool can also help retailers drive lapsed customers back to the site, and can build marketing campaigns to target customers months in the future, MyBuys says.
At the heart of the tool is the ability to react immediately to all available data about a customer, says Chip Overstreet, senior vice president of marketing, corporate and business development at MyBuys. For example, the new tool will provide personalized recommendations based not only on what a customer bought and browsed on the site in the past, but also based on the products she is viewing on a retailer’s web site.
In addition, the tool can target customers who haven’t visited the site in a year or more. It can also schedule product recommendations for a future date. For example, he says, a customer who bought a camera last year might not want a new one yet, but might now be interested in a lens.
“What makes this big data project so interesting is most customers come back with some cadence that is outside the typical marketing window,” says Jason Marshall, vice president of e-commerce and marketing at Cost-Plus World Markets, a MyBuys client. Traditional retargeting services, he says, usually go after a site visitor pretty hard for seven to 10 days, then trail off. The Long-Term Targeting tool can instead look for a customer who perhaps only buys around the holidays—a big deal for retailers with seasonal selling curves, like World Markets—and schedule promotions to begin in November or December for that kind of customer, Marshall says.
“It’s almost like rather than re-target, you pre-target someone to get them back to the site,” Marshall says. “I don’t think this exists anywhere in the market yet.” He says he is excited to begin using the tool and send product recommendations to customers based on what they’ve bought over the years. For example, the retailer may send a campaign to customers who bought a patio set ads to “refresh your cushions” the following year, or suggestions for stemware to customers who already have the store’s dining table.
The product is MyBuys’ first release since upgrading its data-processing platform from Adobe’s Omniture system to Apache Hadoop, open-source software used by many large university and research institutions to process large quantities of data.
Hadoop makes sense of the several terabytes of consumer data MyBuys collects in its data centers, which are distributed across the United States. MyBuys says the Hadoop framework is efficient enough that MyBuys can send new information back to customers’ websites in milliseconds after querying the system, which allows the company to make use of much more personal data than before, while still presenting recommendations almost instantly.
“The key message is really extending that window so that retailers can now reach consumers six or twelve months out—it just opens up a whole new set of possibilities for them,” Overstreet says. “The market has been asking for it for a long time, but the technology hasn’t been there.”