Grayboxx: A Better Local Search Engine?

I hear lots of claims from startups about how they’ll improve upon or do better than the competition. Most of the time, those claims don’t prove to be true in practice. Of course if you didn’t believe your own PR, why would you even try? Now a new San Francisco-Bay Area startup called Grayboxx is making similar claims about local search.

Local is now seeing a flood of startups that contend they can offer something better, simpler, more intuitive or more accurate to consumers and local businesses. The sheer scale of the local market – tens of millions of small businesses and many billions in annual ad spending – makes local a highly desirable, if tremendously challenging, opportunity.

Grayboxx CEO Bob Chandra rightly asserts that local community input – ratings and reviews – are spotty on even the best and most complete sites. Either the coverage is deep in selected categories but not across the board, or it’s broad but thin. Indeed, social media sites and those with community content have historically been challenged to generate “critical mass” when it comes to user content, at least at the outset. Over time, sites that develop momentum and increasing content depth find that getting ratings and reviews becomes easier.

Chandra argues that in certain categories, which lack the “sex appeal” (my term) of bars and restaurants, it’s almost impossible to find reviews/ratings; or if they exist, there are perhaps a small handful. Chandra told me the Grayboxx algorithm and methodology has allowed the site to compile hundreds of reviews on even the most ordinary and mundane categories, such as “notaries or pest control and tile contractors.”

But Chandra says that Grayboxx’s algorithm, which the company has labeled “PreferenceScoring,” has allowed it to compile “community input” (ratings) on an unprecedented scale across 3,000 business categories. Chandra told me that “in over 500 categories there are more 200 cumulative recommendations within the category.” In all he says the company has “50 million local endorsements are [that are] spread evenly throughout the nation.” Chandra claims about 80% coverage of U.S. cities and numerous suburban areas at launch, which will happen shortly.

To prove what he is saying, Chandra invited me and others to conduct searches and compare results on Grayboxx vs. Yahoo Local and Yelp.

While he wouldn’t elaborate on the specifics, Chandra says Grayboxx compiles data from many sources, some of which are considered proxies for user recommendations. The methodology, however, raises the question of the accuracy and validity of the recommendations. But Chandra says that when Grayboxx compares its results to critical and expert reviews “in the few categories users and critics get around to reviewing, Grayboxx matches those results.”

Here’s an explanation from the Grayboxx site:

Put simply, Grayboxx recommendations come from other people in your neighborhood. We use a patent-pending technique, called PreferenceScoring, in which we identify business references in user data such as e-mail address books and tagged digital photographs. When a user takes the time to store the name of a restaurant in his address book, chances are he likes the place well enough to do so…probably because he’ll call again in the future for reservations or to order take-out. We aggregate these references (which we call “implicit endorsements”) to local businesses – and these make up the recommendations you see on our site. The businesses you see on Grayboxx have met the interest of one or more of your neighbors. Our approach has brought an unparalleled level of community feedback to local search and we hope you see the difference. In addition, our algorithmic results strongly correlate with critic’s choices…our top results correlate with CitySearch Editor’s Picks in a range of categories such as Best Hair Salons. (70%), Best Fine Dining (80%) and Best Health Clubs (80%).

The current interface is skeletal at best. (The site is currently in “alpha.”) But Chandra says that’s just a matter of putting a better looking skin on the engine. That may be so, but the site’s look and feel and ease of use will matter in terms of whether it ultimately succeeds or fails.

There’s also the question of trust. Right now Grayboxx isn’t a known brand and so it will take time to build up trust among users. If the algorithm is as good as Chandra contends that could happen relatively quickly. But local is still an extremely tough nut to crack.

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