In the case of Cleverset, the system starts collecting data and forming relationships within that data the instant a person hits the retailer’s website. D’Ambrosio says that, as with many site analytics tools, Cleverset relies on little programs that retailers install on their websites. These programs can track the previous site that the consumer viewed, and if it was a search engine, it logs the keywords used. As the user clicks on items, Cleverset’s system creates a more detailed view of his interests and compares it with those of other people using the site. What sets the system apart is that it organizes customers’ behaviors into a data set that includes information on how various behaviors relate to each other. The system also pulls in outside information, such as whether or not a person is shopping during a Super Bowl commercial break.
While Cleverset was founded in 2000, its technology has only recently reached the point at which the results are good enough to make a significant difference in the competitive e-commerce industry. D’Ambrosio says that sites that use Cleverset–which include Overstock.com and Wine Enthusiast–experience, on average, a 20 percent increase in revenue per customer. The company is also earning some media buzz: when Cleverset presented its technology at the Web 2.0 Summit in San Francisco last month, it came away with two audience-voted awards: “Best in Show” and “Most Likely to Exit First.”
Stanford’s Koller says that a recommendation system such as Cleverset’s “fit neatly into the framework of statistical relational modeling because it’s all about relationships.” She argues, though, that it might be impossible to make a single system fit every kind of e-commerce site. For instance, Netflix, which launched a competition to build a better system, uses different methods than a site that recommends clothes. (See “The $1 Million Netflix Challenge.”)
Cleverset works with each site to tailor its technology appropriately, says D’Ambrosio, which will be important, as the company soon plans to launch with a number of undisclosed “very large retailers” that bring in $100 million or more annually. D’Ambrosio adds that the technology is still improving, and he and his team see future versions of their system including even more input from merchandisers about how their customers use their site.
Smaller design teams can now prototype and deploy faster.