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Local Search: Who Should Tell You Where To Eat Tonight?

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There is much discussion about the role of traditional search engines like Yahoo and Google when it comes to local search. Traditional web search was built and optimized to value the popularity of a page rather than the popularity of a place. 

bizzy-micro-reviewsThe rise of Web 2.0 gave birth to products like Yelp, focused on collecting reviews from local business patrons and organizing them into search results, with the most liked businesses at the top. Yelp created a step function improvement over what Google and Yahoo had to offer, allowing real customers to voice their opinions and be heard. 

Seven years later, it's time to re-imagine local search for this decade. This next generation search will be highly impacted by the two leading trends of the last few years: social and mobile. 

Local & Social

Our real world social network has always been a key source for local search and recommendations. We all know a few people we can call when we want to try a new restaurant or travel to a foreign city. 

The problem is, we don’t always know which person to ask which question. Your next-door neighbor might be a fantastic source when it comes to recommending where to go to get the best sushi, but you might not know that. You might not even think to ask him. 

Until now, the best way to cover your bases has been to broadcast your question to your network and hope that the right person sees your inquiry at the right time. 

Facebook or Twitter, the two best options for broadcast, have yet to harness the recommendation power of your social connections fully. The aspect of Facebook posts and Twitter feeds that make them most interesting, their temporal nature, means there’s no structured way store or recall information from those chiming in on your requests. 

Moreover, your friends are not always necessarily the best source of recommendations for you. Some live far away, are much older, much richer, or simply have different tastes. 

It is fair to assume that while you feel comfortable taking a restaurant recommendation from a friend, it isn't always better than a random recommendation from someone you don’t know, and sometimes worse.

Local & Mobile

Friends are only part of the picture when it comes to local search. Since so much of local search is focused on taste-based searches, like restaurants and bars for example, the best people to provide you with recommendations are people that go to the same places you already like. 

These people might not be your friends, but you may have seen them hundreds of times, perhaps while waiting for your coffee in your favorite local coffee shop, or sitting at the next table in your favorite restaurant on date night. They can be a rich and effective source of recommendations, but how can you find them when you need them? 

This is where mobile comes to play. Location aware smartphones can lower the barrier of entry, and help amass vast stores of data that can be used to identify similar people and seamlessly poll their data, to help you find the best restaurant for you.

Yelp collected 17 million reviews over seven years on the web, just about 2.5 million reviews a year. Foursquare collected 500 million check-ins in only two years. Different than Yelp, where most users are content consumers only, Foursquare created a network where participation is the only way to get value out of the system, turning the old consumption vs. creation content ratios of yore on their ear. 

The Recipe for the Next Decade’s Local Search

Can we create a review system that’s as easy to use as Foursquare or Twitter yet does a better job than Yelp of matching people up with local recommendations? Absolutely. Here what it needs to be:

  • Primarily smartphone-based and location aware
  • A single unified database of all local businesses 
  • Super fast and easy way to collect sentiment towards a local business 
  • Smart algorithms matching people based on what they like and powering recommendations based on those networks
  • Massive dataset so the system can scale worldwide with no degradation of recommendation quality

Who is Doing it Right Now? 

Yelp is still the clear market leader, but will be challenged with providing personalized results, because only between 1 and 2 percent of its users actually participate. 

Foursquare offers personalized recommendations through its Explore feature, but they are based on where users went, not what they liked. 

Bizzy offers personalized recommendations based on a similar people algorithm that takes sentiment into consideration and provides a quick and easy way to tap out a mini-review while you’re still at the place, but it’s still new and doesn’t yet have the user base that Yelp or Foursquare have.

This is a picture of a market that is ready for disruption. How do you think local search will look a year or two down the road?


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