If you have been around the mobile industry long enough you may remember when the great topic of the day was “Location-Based Services” (or LBS). No one really knew what that meant, but everyone seemed to agree that someday it would happen.  When pushed the best example people could come up with was “You’re walking past a Starbucks and the store text messages you a coupon to come in.”  This example was used so much it became something of a joke. And slowly the hype around LBS faded.

Then something funny happened. As with all things mobile, that something began with the iPhone. App developers employed vast creativity to put interesting new features into their offerings, and many of them latched on to the iPhone’s GPS as something novel, and not available on a PC. Today, almost every app uses geo-location to do something; whether that’s recommend a restaurant, find new “friends” nearby, or just report on the owner’s whereabouts to some ad network. All that expectations around LBS actually came to be, it’s just that no one uses that term anymore and almost everyone who was once at the forefront of evangelizing it has moved on to other projects.

Even a casual glance at the tech headlines any week this year should offer hints as to just how important location is to the mobile ecosystem. If we go back to the original thinking on LBS, one reason that geography matters so much to mobile is that it is one of the key ways in which mobile is different than other forms of computing (personalization and ubiquity are also high on that list). Probably the most prominent example of all this is the Apple Maps debacle, but that is really on the tip of the iceberg.

Location has become very important to the major revenue sources for mobile app developers, a fact which is not lost on the platform makers, Apple and Android.

This is particularly true for advertising Arguably, in-app ads are one of the major sources of revenue for app developers and probably the only source of revenue for Android developers. If we think back to the dark days of LBS and that Starbuck’s example, the need should be pretty clear. The more an advertiser knows about the person using the ad the more effective that ad will (should) be. For all the data that ad networks have about us, only so much of it is truly useful in offering insight into what a user wants. Advertisers and their supporting technology have to make educated guesses based on what information they have. This is why Google is powerful, because every time we use it we tell it what we want.  Mobile apps have to make a  best guess. They know the type of phone you are using and your mobile carrier (which speaks to your income bracket), the app you are using says something as well. In some cases, the app has history and know past behavior.  Beyond that, there’s not a lot to go on. Which makes location very important. Unlike other forms of advertising mobile ads are very much about what is happening at that moment. See a TV commercial you may remember it for a long time. But a mobile ad has to act quickly. So if the advertiser knows where you are, it can make a much better guess about what you want and how to influence you. If you’re standing near a Starbucks and you do a search for coffee, that ad network has a pretty good shot of delivering you an ad you will act on. (Just don’t hold your breath for a coupon, Starbucks does not do coupons.)

So location is important, but how does it all work?

We have explored this topic a lot in the past, so please let us know if you want some background. There are really three elements at work in mobile location. First, the latitude and longitude coordinates (lat/long in the jargon) which come from the GPS processors in the phone. This is some complex math, but its now pretty standard in smartphones, just keep a eye on your battery when you use it. Then the lat/long has to be translated into place on a map (the map layer or graphic layer), and finally that place has to have some meaning attached to it, a building or store (Points of interest).

When Apple rolled out Apple maps they had the first two nailed down, with a great user interface Apple maps looks very nice. They fell down on the third, converting all those lat/long coordinates and shaded polygons into useful information is not as simple as they thought. Apparently, there is a fourth layer at work, understanding what people want from their maps.

If anything, that whole affair just made everyone realize how high the stakes are for having location data on mobile. Getting all those layers to work together matters to consumers. A lot.

So far, we have kept everything simplified. At each node, however, there are complexities. Determining a precise location turns out to be trickier than just reading the lat/long from GPS satellites. Accuracy for GPS signals, especially in any place that is not a wide open field, is hard to calculate. Precision is possible but requires time and battery life. So smart people have found other, clever ways  to get a location fix. Cell towers have incredibly precise GPS attached to them, and so knowing the location of a cell tower helps. This also partially explains why Google has gone around the world recording the location of Wi-Fi routers. (And why privately-held Skyhook who does the same thing sued them).

Combining all of this (itself a complex process) finally gets us to the point where we can expect a reasonable degree of accuracy on our mobile phones, calculated quickly.

That is, if we are outdoors.

But it turns out most mobile usage, especially mobile app usage, takes place indoors. In malls, airports, office buildings, schools, and movie theaters.  Most of the methods above fall apart indoors. In theory, GPS signals can reach inside buildings, but since they are coming from space, the signals are very weak indoors and that means they are much harder to decode. This is a problem because the places where we are most likely to make decisions that can be influenced by an ad are indoors – in the mall before shopping, in the office trying to decide where to get dinner, etc. This indoor location problem is not a small one.

Of course, there are solutions. Many private companies have sprung up to tackle this problem. For instance, Apple recently acquired WiFiSLAM which uses a few tricks to determine indoor location. We also know privately-held Miello also operates in this space. Our sense is that there is still a lot of work that needs to be done here. And don’t forget that just determining location is not enough,  that information will have to be tied to POI data. It’s not enough to just say “You’re in the Westfield Mall”, apps need to know you are in front of the American Eagle at the San Francisco Center, 3^rd level.

If you take a step back and think about it, there is a tremendous amount of technology being applied here. Start with putting GPS satellites into space, then the math to decode those signals, a huge database of POI information, and all the algorithms to tie everything together. All of this to offer you 35 cents off your next Latte and entice you into Peet’s rather than Starbucks.

Given the amount of effort that has gone into all this, I think it is safe to say that advertising will not be the only use case for this data. Google’s interest is this is manifold, they have an insatiable appetite for data, and are good at finding ways to tie that to their core advertising business. Apple’s motives are less clear, they may not even be clear to Apple itself. But beyond the big players, we think there is a real market demand for better location, especially indoor location. Companies like Lanyrd which help facilitate meetings at large conferences are calling out for that. How much time did you spend in Barcelona trying to find some booth on the map, and then trying to figure out where you yourself were? We spent a lot of time doing just that and would have greatly preferred an app to do it for us.

All of this brings us to what is likely going to be a major field of tech research in coming years – contextual awareness. Phones capture a lot of data around us – location, radio signals, usage, atmospheric pressure, temperature, the list goes on. The next wave of the process which began with LBS, will harness all of that data to do something. Just what it will do is not clear, but it could be powerful and important. Look to companies like Alohar and Newaer for signs towards this part of the future, but the end goal seems to be phones that can anticipate our needs before we type anything into that search bar