Cambridge, MA-based Stylefeeder is bending more brainpower toward the study of online shopping behavior than virtually any other startup I know. The personalized product recommendation company has seven full-time employees, and five of them actually write code.
Recently, they’ve been coding up some cool algorithms that show site visitors a customized set of products depending on their location. Not only are visitors twice as likely to click on Stylefeeder’s product suggestions when they’re filtered geographically, says founder and CEO Phil Jacob, but by tracking who clicks on what, the company is gradually assembling a detailed, real-time picture of shopping trends in various regions of the country. Jacob shared some of Stylefeeder’s data with Xconomy this week—and it’s extremely interesting stuff.
By tracking the Internet addresses where Web requests originate, Stylefeeder is able to pinpoint which brands of shoes, apparel, and other items are gaining and losing in popularity in each region, down to the level of individual Zip codes. To cite one data set Stylefeeder sent us, site visitors from Seattle have been shying away from traditional, outdoorsy brands like Levi’s, Timberland, and Converse this week and toward more expensive designer brands like DKNY, Victoria’s Secret, and Kate Spade. Since Thanksgiving Day, DKNY has moved up almost 60 slots in Stylefeeder’s rankings for Seattle.
In Boston, meanwhile, The North Face, Nanette Lepore, and Diane von Furstenberg are down, while Lucky Brand Jeans, Moncler, and especially Marc Jacobs are up. San Diego shoppers seem to be losing interest in Hurley, Volcom, and Kenneth Cole, but are excited about Puma, Oakley, and Charlotte Russe.
Here’s the data in chart form:
While most clothing and accessory purchases are highly personal—Stylefeeder abounds with boots, blouses, belts, and bags—the company is also tracking items that site visitors are probably perusing as potential gifts for others. Here’s a rundown of the hottest gift items this week in Xconomy’s three cities:
Stylefeeder doesn’t compile all this regional data out of idle curiosity—ultimately, it helps the company show site users items they’ll like better.
While the company has spent years perfecting the collaborative filtering engine that underlies its personalized search service, that engine only works for regulars who have accounts and login names on Stylefeeder, according to Alex Nauda, Stylefeeder’s director of recommendation technologies. But many users arrive at the site from search engines or other locations, or never bother to log in. So Nauda and the other Stylefeeder programmers have put quite a bit of work into