Optimizely’s Assault on the One-Size-Fits-All Web
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you can test the effectiveness of your discount offer based on geography—which is often a proxy for income and other demographic factors.
By tapping into databases like those owned by Facebook, you can even target messages according to gender and other characteristics. “If somebody is logged into a site using Facebook Connect and the site is using Optimizely, we can use any of the information that Facebook makes available through their API to target a message,” Siroker explains. “We can say ‘Show men this and show women that,’ or ‘Show people who like a certain flavor of ice cream this and show others that.’ That’s something that, even last year, was impossible.”
The Amazons of the world already have the engineering firepower to run their own sites this way. But with cloud-based tools like Optimizely becoming affordable for more and more companies, it’s not hard to imagine a Web that’s essentially one huge, ongoing festival of optimization.
“Our most successful customers start out by asking, what is the best one-size-fits-all solution, which A/B testing is really good at finding out,” says Siroker. “But later they move from one-size-fits-all to segmentation. They ask, what is the best thing for new users? For returning users? For people who have purchased before? And eventually, for each individual user. That is a natural evolution, and that’s why we think this is the future of our company.”
Over time, Siroker hopes, optimization would become not merely dynamic but automatic. Using what it’s learned from tens of thousands of past experiments, in other words, Optimizely could begin to make decisions on its customers’ behalf. “The choices shouldn’t be around ‘This variation is better than this,’ but ‘This is my goal, you figure out the rest,’ using what we know about what worked for other customers,” he says.
All of that may be great for Optimizely, but is it truly good for consumers? In a world of mass personalization on the Web, will there be such a thing as authority or curation or consensus? And if everything is optimized by algorithms, will there any need for people like advertising copywriters or newspaper editors? Perhaps not, to hear Siroker tell it.
“There are some domains that may not be well suited for this, but I think consumers will be the voice at the end of the day,” he says. “My guess is that the New York Times Page One meeting in the future will be a meeting where they say, ‘Optimizely has been running for an hour and it looks like these three stories are the most popular on our site, so those should go on the front page, and we should have this go to the West Coast and that go to the East Coast.’ It’s going to be much more data-driven and objective than having the guy with white hairs on his head decide.” (Sorry, white-haired guy.)
Wesley Chan is a partner at Google Ventures, which just invested in Optimizely despite (or perhaps because of) the fact that the startup is giving Google’s own A/B testing tool a licking. He says it was the analytics background Koomen and Siroker brought to the business—in particular, Siroker’s success helping the Obama campaign raise hundreds of millions of dollars in 2007 and 2008—that attracted Google to the company. But he says it’s the potential for huge financial returns, assuming the technology takes off, that sealed the deal. “I think their immediate focus right now is on building the simplest and best A/B testing tool that anyone can build,” Chan says. “But they will move beyond that. Am I a believer in that vision [of mass personalization]? Absolutely, that is why we are invested.”
“If we can achieve that vision, that ambition to dynamically transform every page on the Web, then we can build a really big business,” Siroker sums up. “I can’t imagine a future where that is not going to happen. It’s just a question of how.”
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