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Location based social networks are suggestion engines
Location based social networks are suggestion engines
Location based social networks are suggestion engines
Location based social networks are suggestion engines
Location based social networks are suggestion engines
Location based social networks are suggestion engines
Location based social networks are suggestion engines
Location based social networks are suggestion engines
Location based social networks are suggestion engines
Location based social networks are suggestion engines
Location based social networks are suggestion engines
Location based social networks are suggestion engines
Location based social networks are suggestion engines
Location based social networks are suggestion engines
Location based social networks are suggestion engines
Location based social networks are suggestion engines
Location based social networks are suggestion engines
Location based social networks are suggestion engines
Location based social networks are suggestion engines
Location based social networks are suggestion engines
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Location based social networks are suggestion engines

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  • This is where Bob Solish is going to work
  • This isn’t a shiny new thing. This is a search engine designed to pull data into one place. A list of things my community likes. A Suggestion engine that pulls suggestions from Facebook and FourSquare into one place. This is where things are going for people.
  • Transcript

    • 1. The rise of suggestion engines
    • 2. Early Internet
      • Before Larry Page and Sergey Brin created Google, search engines based results on the number of times a search term appeared on a page.
      • The results weren’t always good.
    • 3. The switch
      • About 3.7 billion people actively use about 4.3 billion mobile phones.
      • They use them to create massive amounts of content on location.
      • But Google doesn’t always return the best place to get a vegan cupcake.
      • For that, I require something else.
    • 4.  
    • 5. Location based social networks
      • FourSquare
      • Facebook places
      • Google Latitude
      • Twitter places
      • Gowalla
      • Whirl
    • 6.  
    • 7. Location based social networks
      • In 2010, FourSquare had 381 million different check-ins
      • All people with a Facebook App have Facebook places.
      • These are suggestion engines: search engines for getting suggestions.
    • 8. Location based social networks
      • Yelp
      • Google Earth
      • Facebook
      • Twitter
      • FourSquare
      • These are all places where one can get “Suggestions” on where to eat, drink, stay, vacate, etc.
    • 9.  
    • 10.  
    • 11.  
    • 12.  
    • 13.  
    • 14.  
    • 15. These are suggestion engines
      • To turn people’s suggestions into searchable engines, properties need to incent people to check in and leave tips using:
        • Game mechanics
        • Badges (rewards)
        • The reward of getting followed (being a go to resource)
        • Pulling data when you need it (what friends have left tips around here)
    • 16. What can brands do?
      • Understand the underlying dynamic.
      • Facebook, FourSquare, Yelp, etc are all trying to incentivize check ins because there aren’t enough brand offers.
      • Offers are the best reason to check in, but properties can’t make brands give offers.
    • 17. Examples of good ones
    • 18.
      • Brands that do get on board, can succeed in a world of check ins because all the properties are trying to build suggestion engines.
      • An offer is a reason to give a suggestion.
    • 19. In conclusion
      • We’re on the cusp of user-check ins.
      • But maybe we should call them suggestion engines.
      • It sounds like another buzz word, but instead of thinking about FourSquare, think bigger.
    • 20. Question to ask
      • Instead of thinking about offering deals, think about Optimizing for Suggestions.
      • Make it easy for people to suggest and review
      • Make sure there is a plan to interact with people who suggest or review.

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