The Web Knows What You Want

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SHOPPERS' SIGNALS

ATG quantifies the crazy quilt of relationships between every item in its customers' stores, whether Tommy Hilfiger or Body Shop International. It analyzes which type of customer buys each product, and what else they buy or look at. This adds up to hundreds of billions of relationships. But it's through such analysis that ATG finds connections such as the one between silky slippers and men's bathrobes. ATG also studies the shifts in Web surfers' behavior over time. Shoppers, D'Ambrosio says, tend to be in a big hurry when dropping in from work and have more leisure time on weekends. Ideally, the site adjusts to their rhythms, leading shoppers on a leisurely stroll on Saturday afternoon, and sending them hurtling toward checkout on Monday morning.

The algorithms used by 7 Billion People attempt to mimic the human feedback loop in a brick-and-mortar store. While a sales clerk can see a shopper is in a hurry, the Web site must pick up that insight from other signals, such as rapid-fire clicks of the mouse. The trick then, says CEO Nagaitis, is to fit the site to the customers. Those who dally over reviews and related products find themselves transported to Web pages with more features to explore. Shoppers who are more likely to be swayed by demonstrations, for instance, may find videos to click.

Such adjustments can prove very valuable, say customers. Doug Scott, who heads Web strategy at ASAP Ventures, a British e-commerce incubator, says he used to optimize sites he controlled to his own tastes, with lots of details and choices. But after running tests with 7 Billion People, Scott learned that only about one-third of its users shared his tastes. Others wanted to read testimonials or simply to hurry up. "We could fine-tune for one-third, but then we would upset the other two-thirds," he says. Adjusting for the different types, based on their behavior, has lifted the conversion of Web site visitors to buyers by 30% to 50%.

Another competitor of ATG, San Francisco's richrelevance, is dusting off theoretical algorithms from before the computer age to see if they can be used to predict customer behavior. "If someone's looking at a Dell (DELL) computer at Wal-Mart (WMT)," says CEO David Selinger, "is he more likely to be interested in a more expensive PC, a cheaper one, or a warranty?" Well, the answer depends on the individual, the time of day, and a few hundred other variables.

Baker is a senior writer for BusinessWeek in New York.

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