BusinessWeek, The Web Knows What You Want 0
By dissecting behavioral data, e-marketers are creating sites armed with predictive technology
By Stephen Baker
Every once in a while in most Web surfers’ lives, a suggestion pops up on the screen that leads them to wonder: How did they know that about me? The moment can seem magical, and a bit creepy.
Consider this one. A shopper at the retail site FigLeaves.com takes a close look at a silky pair of women’s slippers. Next a recommendation appears for a man’s bathrobe. This could seem terribly wrong—unless, of course, it turns out to be precisely what she wanted. This type of surprising connection will happen more often as e-marketers adopt a new generation of predictive technology. It’s fueled by growing rivers of behavioral data, from mouse clicks to search queries—all crunched by ever more powerful computers.
Why the bathrobe? ATG (ARTG), a Cambridge (Mass.) e-commerce software company that crunches data for FigLeaves, has found that certain types of female shoppers at certain times of the week are likely to be shopping for men. Like all Web recommendations, this one will be wrong a good portion of the time. But as marketers scrutinize shoppers in greater detail, they’re edging closer to their ultimate goal: teaching computers to blend data smarts with something close to the savvy of a flesh-and-blood sales clerk. “In the first five minutes in a store, the sales guy is observing a customer’s body language and tone of voice,” says Mark A. Nagaitis, CEO of 7 Billion People, an Austin (Tex.) startup that competes with ATG. “We have to teach machines to pick up those same insights from movements online.”
This dissection of online shopping comes amid growing fears about invasions of privacy online. But unlike the most controversial advertising technology, which tracks Web surfers’ wanderings from site to site, many of these “preference prediction” methods limit their scrutiny to behavior on a retailer’s own Web page. Much of the analysis looks simply at the patterns of clicks, purchases, and other variables, without including personal information about the shopper. In most cases, personal details are incorporated only if customers register on sites such as Amazon.com (AMZN) and Walmart.com (WMT) and supply them.
In the early days of e-commerce, most of the analysis focused on simple buying patterns among shoppers. Amazon and others introduced so-called collaborative filtering in the late ’90s. They found, to no one’s surprise, that people who bought the same book were likely to share interests in other books.
Now the science is growing far more sophisticated. Three years ago, Netflix (NFLX), the movie rental powerhouse, dangled a $1 million prize before anyone who could plow through data from millions of anonymous users and improve Netflix predictions of what movies customers would like by 10%. Last month an international team of computer scientists reached that goal by introducing deeper analysis. The winning team factored loads of details into its algorithm. It attempts, for example, to compensate for the shifting sentiments of a movie watcher over time. If one reviewer pans a number of movies in a row, are they all really so terrible? The algorithm might allow for a stretch of the blues—and take those ratings with a mathematical grain of salt.