Alike Decodes the DNA of Places: Hear More at Mobile Madness Dec. 12

I haven’t been there in far too long, but I know all the details of my favorite Seattle coffee shop: The low light and loud music, the quirks of the baristas, which snacks are worth buying, and the best tables for silently banging out some writing work.

I’d love to find more places just like it. But if I’m using Google or a smartphone app, what would I search for? Probably just “coffee” or “espresso,” maybe with “free wi-fi” thrown in for good measure.

And then, the sifting begins: Is it highly rated? Do I even believe these reviewers? What do the photos look like?

As first-world problems go, this ranks right near the top. But it’s the kind of consumer behavior that is happening constantly in the smartphone era, with on-the-go customers able to tap into a mind-boggling amount of information.

“The problem has flipped around,” entrepreneur Maria Zhang says. “In the past decade, it used to be a lack of data—and now it’s too much data.”

Zhang thinks she’s found a better way to connect those folks with the right places—and help merchants win over some customers in the process. Zhang’s smartphone app, Alike, wrangles massive amounts of data about some 25 million places in the U.S., giving each spot a kind of digital genome that describes what makes it special.

It does so by tying together data on places from a wide variety of sources: Crawling websites the way a search engine would, for instance, while also incorporating real-world reviews and social media signals to give a more human take on what makes a place unique.

For the user, that information can be used to find other places that are similar. So if I’m tromping around Boston on a cold November day, for instance, and need a place to coffee up without stooping to Dunkin’ Donuts, I can drop the name of my favorite Seattle coffee shop into the app and find out which ones nearby might be a good match.

In my test, Alike works pretty well—trying to get a match for my beloved Caffe Fiore in Ballard turned up what are probably the three most Seattle-like coffee shops in the Boston area. And no, Dunkin’ was nowhere to be found.


“I think the essence of these places, of these products … sometimes it’s even hard for us to describe in words,” Zhang says. “We know it, we feel it. We say, ‘It’s like that place.’”

It’s just one example, but Alike’s mission is a great illustration of the big, hairy problems that today’s mobile entrepreneurs can tackle with a relatively small footprint.

That’s why I’m excited she’ll be joining us for Dec. 12 for Mobile Madness Northwest, Xconomy’s half-day conference highlighting the smartest takes on the most pressing trends and issues for entrepreneurs, executives, and investors involved in the ever-expanding world of mobile computing. Get your tickets now to get the best possible prices.

Building an application that attempts to corral an enormous amount of information was something of an obvious career step for Maria Zhang. A data geek through and through, she previously built large-scale data-mining services for companies including Zillow and Microsoft.

Her startup, Propeld, took an earlier crack at tying together the similarities of locations with an app called UrbanQ—something I saw in a very early form last year at a pitch competition downtown.

That app was a good test of the technology—it only had about 1.2 million places, as opposed to Alike’s current 25 million—but it also had too many features, Zhang says. “But we had this one feature—it used to be almost hidden. It was called, ‘more like this.’”

Fast forward to the present day, and “more like this” is the centerpiece of Alike. And the company has bigger ambitions for the app: A new version released today adds a Web-page interface, and also incorporates coupons and offers from certain merchants. Zhang also sees a tantalizing opportunity to apply the Alike rankings and connections technology to specific product categories, such as shoes or cosmetics.

It’s free for consumers, in the now-common means of seeking a big user base, but the app has some ability to generate revenue by offering messaging services to merchants, such as the deal integration or push messaging to reach consumers. The startup has eight people on its team and is angel backed.

“I think 2012 is just the perfect timing to do this,” Zhang says. “I’ve been working with data solutions since the ‘90s. The type of processing power that this little company has would cost millions of dollars 10 years ago.”

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