Yabbly proclaims on its Web site: “Product search powered by people, not an algorithm.”
So it was interesting to learn that Mike Fridgen, who headed Decide.com—which offered very powerful product and pricing searches powered by algorithms, not people—has signed on as an adviser to Seattle-based Yabbly. I had a chance to talk with Fridgen to learn more about what he sees in the social commerce startup, and what he’s doing at eBay.
Fridgen and Yabbly co-founder and CEO Tom Leung have known each other since attending Harvard Business School in the early 2000s. Lately, Leung says in a blog post today, they have come to see eye-to-eye on an issue that defined their companies’ divergent approaches to shopping recommendations. “It turns out social commerce and big data commerce can co-exist,” he writes.
Before Seattle-based Decide was acquired by eBay in early September, I had been contemplating a story comparing Yabbly and Decide, which together embodied the man-versus-machine debate applied to product buying advice. (It is, of course, an age-old debate. See: Henry, John. Or, for a more modern take, my colleague Curt Woodward’s look at software versus human editors in the digital newsfeed business, in light of acquisitions earlier this year of Wavii, Pulse, and Summly.)
“I had hoped that there would be ways that Yabbly and Decide could have partnered up,” Leung told me in the company’s Pioneer Square office last month. “I think we both brought something different to the table.”
He was imagining the potential of melding the work of Decide’s data scientists to track and forecast product price fluctuations with the community and crowd-sourcing setup of Yabbly.
That community aspect is important, Leung argues, particularly for big-ticket purchases. A new dad shopping for a digital camera might be told by a recommendation engine to get a Canon EOS Rebel T3i. He might get the same advice on Yabbly, this time from another dad who had answered other questions about cameras for other users, and was rated highly by them.
“Even though it’s the same answer, what we found is it’s how you get it, and where it comes from” that matters, Leung says. “It’s a much more compelling thing when it comes from a human” as opposed to “the cold steel of Google servers giving you results.”
Fridgen—now a general manager in eBay’s 250-person, data-focused Bellevue office—is excited about Yabbly because “no one has quite cracked the code” in social commerce, and Leung and his small team are going at it the right way, “putting the user first, and iterating quickly with the product,” he says.
We live in a world of social data that describes relationships among consumers: your social graph points to people whose opinions you would probably value in certain contexts. “It seems like there’s a whole untapped opportunity of mining that data more efficiently and making use of it at the moment it’s most relevant to you,” Fridgen says.
(Others are pursuing this line of thinking: There’s Preferling, an early-stage effort of Andrew Vest to build a recommendation platform for venues and services that your friends like, profiled last week by GeekWire. Livestar was working on something similar before being acquired by Pinterest earlier this year.)
It’s not immediately clear to what extent Yabbly
