Remember this?: Someone who browses listings for bike trailers on a site run by CSN Stores (upper left) might later see a related ad on a separate website, such as Weather Underground.
For every 100 people who visit a retailer’s website, only about two will buy something; the other 98 will just leave. But with enough reminders, some of these 98 people might become customers someday. The trick is to figure out which ones they are, and then to nudge them by means of targeted ads. The practice is known as retargeting, and it’s growing on the Web.
When you see an online ad for a product you previously looked at on a different website, you’ve been retargeted. Making that happen is the business of companies like TellApart, which was founded in 2009 by Mark Ayzenshtat and Josh McFarland. They came from Google, where they had both worked on AdWords and other advertising platforms. TellApart uses data it gleans from online retail traffic to predict whether someone who leaves a given website is still a likely customer of that site—or not. People deemed likely to return can be shown ads for the site whenever an opportunity arises on the real-time bidding exchanges used to fill online ad space. “We only get paid when we drive a sale,” says Ayzenshtat. “We’re confident that this works.” About 7.5 percent of viewers click through on their personalized ads, the company says. If so, that is dozens of times higher than the click-through rates on generic Web ads. And about 4.5 percent of those who click on a TellApart-generated ad eventually make a purchase, the company says.
Ayzenshtat says the company’s algorithms assess site visitors much as a salesperson would in a physical store. “When you walk in, the salesperson develops a profile based on what you’re wearing, who you’re with, if you have other shopping bags,” says Ayzenshtat. “In the online world, the signals are harder to tease out.”
To do it, TellApart’s servers sift through a tremendous amount of data provided by the online retailers it works with. For instance, when someone visits a site, it can examine where the person had been previously: did he click a link from a blog or an ad? Did he end up at Diapers.com after searching for a particular crib that he had also checked out on other sites? And once on the retail site, what did he do? Did he leave after seeing one page, or did he seem to be comparison-shopping? Also factored in are what products a customer browses on the site, whether they are all in a particular category, and how popular these items are with other visitors. TellApart’s algorithms use the time of day and location to determine whether a person is browsing at home or at work. It follows the user by placing a cookie in his Web browser, a standard method of keeping track of people online.