Think Local, Act Social

Social networking has taken on many forms (and continues to morph) in the quickly evolving online media world. In local search, this trend manifests itself in user-generated ratings and reviews for restaurants, bars, lawyers, and dog washers.

There’s clear demand from searchers who increasingly expect user reviews as part of their local search experience. Some of this demand can be gleaned from the data in The Kelsey Group’s User View consumer survey (subscription required) released in March, which shows ratings and reviews as the fastest growing feature in which users place importance among local online sources (see graph below).

Social networking has increased demand for ratings and reviews. Social media fuels a whole new kind of social identity and influence on a local scale. Meanwhile, Internet Yellow Pages companies (including and have been falling over each other to be next to launch user ratings and reviews features.

Online pure plays have also had a PR blitz over the past few months for new launches or integrations of ratings and reviews features. The latest? Shopping search site ShopLocal announced new features two weeks ago that will allow users to establish an identity and save, share, and comment on products.

Getting Social with SEO

The trend has also reached search giants. Google Maps integrated a user review feature in June, while Yahoo! Local went through a major redesign last month that included enhanced and more prominently positioned reviews features.

Yahoo! Local has also modified its relevance engine to include review content in its index. This change brings up another benefit of user reviews: the deepening of content to improve SEO. CitySearch’s March acquisition of local reviews site InsiderPages (and its 600,000 reviews) was driven, in part, by SEO considerations.

“We’ve always considered ourselves to be a social utility with a core focus on relevance, accuracy and depth of content,” said Brian Gil, lead project manager of Yahoo! Local. “We’re shifting the site design to spur more active engagement from the community, and we also modified our relevance engine to bring the best and closest results.”

Beyond feature integrations and retooling, some local search destinations have launched over the past year with UGC (user-generated content) baked right into the product model. YellowBot, for example, offers local search results based on social networking and tagging. The site launched in March and now claims more than a million unique users.

Help from Yelp

The poster child of this new (maybe not so new anymore) breed of local-social search sites has clearly been Yelp, with 4 million monthly unique users and over a million reviews under its belt since its late-2004 launch.

Yelp’s success mostly comes from playing to the egos of its target demographic: twenty- and thirty-something urban “foodies” who want to be social and speak out in a social-networking type format. This includes the standard set of personal profile bells and whistles (profile photo, personal likes and dislikes, etc.) that have come to characterize the MySpaces and Facebooks of the world.

The key lesson: Anticipate who your audience will be and, more importantly, play to its appetite. Many subsequent social search integrations will conversely try to play catch up, developing ratings and reviews models in haste.

Another concern: The expanding universe of social media will lead to a state of “social fatigue.” Think of the many log-ins and profiles an average social media junkie must manage across Facebook, MySpace, Yelp, Yahoo! Local, Google Maps, ShopLocal, and others. Worse, mainstream, non-power users likely won’t be interested in getting lost in this sea of social media.

A vast sea of social search could begin to approach the same degree of fragmentation we see in local search. The resulting industry competition and differentiation could inspire the next generation of social media products. Also, expect to see a new set of social networking aggregators, widgets, or clients (a’ la meebo) that will organize all your social media in one place.

We’ll Always Have Paris, Texas

The recently launched Grayboxx is striving to differentiate itself in local search user-generated content. The company wants to solve a perennial challenge with local UGC: It’s hard to build a consistent library of content across categories and locales.

Restaurants in New York are likely to garner their fair share of reviews, for example, but what about plumbers in Ogallala, Nebraska? The company’s model essentially relies on algorithms instead of humans to get consistent volumes of ratings for businesses in categories where people aren’t interested in writing reviews.

It does this by measuring “implicit reviews” that can be gleaned from various online and offline activity. This involves tracking behavioral patterns to extrapolate the quality or popularity of certain businesses.

“If a user checks out a restaurant online and makes a reservation. And then comes back and makes another reservation for two, this can be a positive sign that he was happy with the experience,” said CEO Bob Chandra. “Conversely, if I go to a hardware store and make a large purchase, that says something. If I go back the next day and make a large return, that says something else.”

Taking this a step further and examining more granular minutiae of online and offline activities on a much larger scale, Grayboxx is able to pick up patterns and devise what it calls preference scoring for businesses.

“It’s an algorithmic and scalable way to rank businesses, not just restaurants and night life but every category,” said Chandra. “The nice thing is that we have results just as well in Moscow, ID, and Bridgeport, CT, as we do in Chicago.”

The site launched last month in Burlington, VT, a town emblematic of the small population (38,000) for which it hopes to establish an edge. There, it has 12,000 ratings on about 3,000 local businesses, according to Chandra.

But the company’s secrecy about exactly how it tracks these implicit reviews has garnered some distrust and backlash from a small number users who want to know what’s under the hood. What’s worse, more than a few conflicting opinions arose between Burlington locals and the company over the accuracy of ratings.

This led to Chandra’s conclusion that he might have started too small, and that larger cities in the range of 100,000 to 1 million would constitute its sweet spot. Given its covert formula, the company will now have to prove itself and hammer out any kinks as it rolls it out to about 250 additional cities over the next three months.

If Chandra can achieve what he says he can, Grayboxx could stand out amongst the growing set of local user reviews sites, which are calling out for something different.

“Places like Yelp are social networks for foodies,” said Chandra. “I’d love to see a social network for people interested in plumbers.”

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