Social Search Series: This summer I am embarking on a journey through on the emerging web of Social Search. Traditionally known as the Questions & Answers industry, this category is currently being transformed by social and mobile technologies. No more asking a site questions and finding old answers. I believe the future of the web is ingrained in the dynamic interdependence of social and informational networks. This is part III of the series. For background, check out Part I and part II.
Social, although hot right now, is not the only technology transforming the web today. Location-based social search applications are bridging the gap between our online and offline worlds – and in doing so creating a whole new way for people to find and use information.
This post dives into the new territory of Location Relevant Social Search.
We first determined the traditional question and answer model is now insufficient, since the system doesn’t know your exact location, who your friends are or have any contextual understanding of your query. The resulting answers are typically of low quality and relevance proving a broken model.
Additionally, search technology needs renovating and although Google is currently King of the Search Land they still have a lot to do if they want to hold onto their throne. Basically, the amount of information on the web is growing so quickly that even the major search engines are bringing back mostly meaningless results.
I am postulating the next generation of search will reside within your network of contacts, and I call it Social Search. In my first article a graph was used to illustrate four quadrants separating the field of emerging social search startups. The first quadrant revolves around Location Relevance and it looks as if a few associated startups are positioned well to change the very way we interact and search online.
First, a few tenets we can stand on when talking about Location based social search applications:
- Most of the worlds information is generated, organized and stored by human beings
- People generating information are always at a specific location found with exact coordinates
- So naturally, generated information always has specific geographical data attached to it
- Combining those data sets: Search + Social + Location + Context = Maximum Relevance
In a related post, Evan Britton noted “the goal of real time search engines is to inform the public of what is going on right now. By adding location data, internet users can be specifically informed as to the happenings in a city.” Indeed, real time search results are incomplete without geographical data included in the context. Location relevance completes the equation to help provide users with the best possible results when searching for specific information.
Location based technologies are changing our lives in every way imaginable. Take the emerging location tracking application Glympse for example. Watching someone drive along a map on their way to meet you, being found when lost on a mountain side or viewing thousands of people moving throughout your city in real-time are just a few ways Glympse will change our lives.
Or think about a similar application Geoloqi, a service using persistent location tracking to trigger notifications tied to real-world places. Maybe it’s a note you or a family member left for you at the grocery store or maybe it’s part of a set of geolocated data that you opt-into subscribing to as a layer because it was of interest to you. Some use the app to let their co-workers know how quickly they are getting through traffic to arrive at work. Make no mistake, location aware applications are already changing the way we interact on the web.
Quadrant: Location Relevance
So what happens when you combine social, searching and location? Annotating results with specific geolocation data when a query is submitted is fundamental to providing users with the BEST answer possible. According to Bing, over 50% of mobile device originated search queries are about a specific place. Think how often you quickly grab your mobile device to search for something. Exactly. The search world needs to catch up to the intricacies of how we are using the web today.
You can find the entire list of emerging social search startups here, but I am highlighting two emerging startups innovating location-based search and are poised to be big players in the search space.
LOCQL, Seattle startup some would refer to as “Foursquare Meets Quora”, has smartly put together two basic premises; 1) everybody knows a little bit about something and 2) location specific information always make things more valuable. Marry those together, involve some game mechanics and you have a living, breathing repository of location relevant information based on where you currently find yourself. Using social power, LOCQL finds the missing links between the user’s queries and the places in the local landscape for which they are searching. They are still in beta but anyone can use the application.
LOCQL Co-founder Robert Mao can see the future of search lies within humans; “The idea for LOCQL came from our life experiences, as International travelers we traveled to many different places, relocated our home’s several times in different countries. There are so many ‘best kept secrets’ only local people know about, those who’ve been there just know it. Unfortunately, without a service like LOCQL, you won’t be able to find it from the web, nor can you find it through search engines.”
A major problem with current search engines is the “objective vs subjective” issue, and the qualitative differences found between their results. Through quantitative analysis, Mao found up to 60 percent of location intended searches are subjective, meaning relevance can vary a lot between two different users searching on the same subject. “Social search is basically harnessing collective intelligence by crowdsourcing the answer from real people, so by nature it better solves the queries which are subjective.”
With LOCQL, users search or submit on topics and questions – typically in relation to a specific location – and receive highly relevant, useful answers. “Who has the best burger joint in Seattle?” searched on LOCQL would give you one or two specific answers left by other LOCQL users who actually know the answer. The same searching on Google will send back hundreds of useless links, most gamed by SEO keywords. Plus one for LOCQL.
Where LOCQL is building a repository of location based information, Localmind, co-founded by Lenny Rachitsky and Beau Haugh, is centered around a real-time social search platform. It can be thought of as the power of omniscience at your fingertips — the ability to know what’s happening anywhere in the world, right now.
According to CEO Lenny Rachitsky, they are working on a somewhat obvious concept. “We’re living in the 21st century for god sakes; we have data on people’s locations, we have always-on devices in our pockets, we have all kinds of sensors in our devices and in our world. We know more about what’s happening across the country than we do at the restaurant we’re thinking about going to. We are putting all those pieces together and solving that problem.”
Localmind allows you to send questions to users checked-in anywhere around the world to help solve your basic needs and inquires – like how crowded is the bar, how many girls at the club, how good is the food at the restaurant, how long is the line at the airport. More interesting uses include people sending questions to Japan after the tsunami asking if there’s anything they can do to help, or people getting free concert tickets when asking about a concert venue, or saving a family a few hours of travel by finding out a certain hotel was closed.
It has been found that subjective queries can be monetized at 5x – 10x higher than objective queries. It doesn’t take a rocket scientist to see where all this is going. Google and Microsoft, I hope you are listening. The problem incumbents face is these types of platforms are so different they are usually built from the ground up using a whole new infrastructure, not tacked onto an existing search tool.
Lenny noted there are 4 core things they focus on: 1) Your preferences, 2) your friends preferences, 3) your current location, and 4) your exact date and time. Combining those gives users much more relevant and useful information. Interestingly, Google would have no clue how to answer those above searches and probably just shrugs its big shoulders if you try. Alas, plus one for Localmind.
The much accomplished team of three launched Localmind at SXSW in March and have already shipped four major updates to the iPhone app. Their Android app is in it’s final beta release and will be entering the marketplace in a few weeks, and they also have an open API (www.localmind.com/api) that allows anyone to built on top of their platform. Amazingly, Rachitsky says 70% of searches are answered in 5 minutes and they just reached 20,000 users, both numbers are satisfactory to Rachitsky at this point.
With a newly raised angel round of funding and relocation plans to San Fransisco, Localmind looks like they are warming up to play some hardball. And LOCQL, a relatively quiet startup still in their beta release, is very strong technically and has a promising future a head of them. Indeed, it seems both are ready to play David to Google’s Goliath. Now, where is that rock again?
Next time, I will determine if Location Agnostic applications are changing the way we are searching on the web. Yes, I’m looking at you Quora.