I knew it! The state of POI data does suck!
So I can still recall driving around Manhattan with my brand new Garmin device circa 2005 just playing with all the cool features and seeing what I could see… it certainly didn’t work perfect… particularly living in midtown where all those tall buildings make getting a GPS fix difficult, and where it could very easily show you a block or two off on either side, making things even a bit more confusing… something to do with signals bouncing off buildings I think.
But what resonated with me most, was chuckling at the business listings that were purported to be surrounding me as I drove down fifth avenue on to Central Park South… home to some of the toniest hotels and shops like The Plaza and Pierre Hotels. According to my Nuvi right next door to those hotels was supposed to be a place called AAA Als Towing and then a few blocks later an auto repair place… yeah right, how many auto repair and tow places do you know paying more than a few grand per square foot for such prime real estate… it was apparently the early days of POI spam, or at least a really bad dataset.
You’ve probably seen articles about the locksmith map spam problem on places like Google Maps, but its much more than that… the industry as a whole suffers from just really poor information related to documenting places… for too many years the map and navigation device makers were seemingly so focused on getting folks from point a to point b and documenting the roadways they’d use to get there, that they forgot about all the places in between point a and point b where you may want to stop and gas up or grab a bite to eat.
But nowadays things have changed and folks are giving away the capability to navigate from point a to point b and looking for ways to make money from getting you interested in making stops along the way.
As I mentioned a few months back a company called Placecast is trying to help bring together the disjointed state of POI data, to help move the industry forward, by introducing a product called the MatchAPI. What the MatchAPI does is allow developers to send in a reference to a geographic location through the API and receive in return any other references in the system that are a close match. The reason something like this is necessary is because there seems to be so many different proprietary datasets around without any centralized and unified source of reference to help connect them. Let’s start with a simple two dataset example where a company has a list of movie theatres from one source like Navteq (where they get all their POI data) and then wants to go to a different vendor like Fandango to get movie times and reviews for the movies happening in those theatres. So if there are a few thousand movie theatres in the U.S. you can see how the task of matching up the Navteq list of theatres with the Fandango list of theatres could be pretty labor intensive… that’s one area where the Match API can come into ease some of this pain. Now if you wanted to do something similar with say the 1 million or so restaurants from that same Navteq POI set in the U.S. and linking in all the Yelp reviews tied to those restaurants… well you can only imagine the amount of pain that the MatchAPI is alleviating.
But where things can really start to take off is when various systems that rely on “place” can more simply and seamlessly integrate and “speak” with one another on the fly, creating a much larger opportunity for all the players involved when the network effect begins to kick in.
Right now there are hundreds if not thousands of location based services all largely operating independently with their own systems, definition of places and customers. When the ability to share information between these services becomes more seamless, not only will the value to users increase dramatically, but folks like advertisers can begin to view this industry as a cohesive ecosystem, and one that has some meaningful scale and reach which will expedite this becoming a viable new medium for them.
Right now even the most successful Location Based Services in the U.S. just have a few million active monthly users, which won’t put them on the radar screens of many big national advertisers. In a way it’s all similar to the way that DoubleClick first helped cobble together a bunch of small websites into an online ad network and create a highly simplified way for advertisers to buy a large volume of eyeballs over 15 years ago. Right now if McDonalds wanted to throw in a two week promotion of their Shrek Glasses in the business listings for their 31,000 stores across all the map platforms, navigation devices, and LBS iPhone apps… well they probably couldn’t do it without a small army of buyers, designers and integrators. This is the pain that the MatchAPI could eventually help go away.
Just today Placecast came out with news that after 60 days of having launched the MatchAPI platform, they’re finding that error rates in the data they’re seeing runs anywhere from 8% to 40% depending on whether it ‘professional’ data or ‘user generated’… finally a somewhat quantitative representation of all those towing and auto repair shops I noticed along Central Park South.
For the nascent LBS industry there is no way that this becomes a big and viable opportunity for marketers if 8% to 40% of the time you either send someone to the wrong place, send them to a place that doesn’t exist or is closed, or give them the wrong phone number, etc. … so fortunately there are folks hacking away at trying to help solve some of these problems, so we can move along to some of the bigger and more interesting innovations that are possible.
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