Living in a digital world

Location Based Services part 3 – New ways of choosing

In the final post in this series, having introduced Location Based Services and described how HP’s Mscape platform is blazing a trail in this space, it’s time to consider the implications for digital marketing.

It’s extremely challenging to fully grasp the potential of having access to location data on a mobile device. One way to approach the problem is to consider that the most useful sites online have proved to be those that help us to search for things and/or choose from the things that we have found. I segment search/choice-making strategies (implemented either by a user or by an algorithm they choose to run) in to four main types: Objective, Curated, Similarity-based and Profile-based.

Objective choice

This is the simplest strategy in which we have just one or two well-defined, objective criteria in mind, and any choice that satisfies these criteria will do. Perhaps we just want the cheapest (or most expensive) wine on the menu; the most popular camcorder on Amazon that costs under £250; the mobile contract that gives the most minutes for £25 a month.

When we consider how location based services (LBS) might help this strategy, the most obvious benefit is being able to search for places by distance – so we can find the nearest free WiFi zone, or cashpoint, or public convenience. When combined with other criteria, this can become even more useful – find the cheapest beer within 10 minutes, or the most popular tourist attraction within half an hour.

The other benefit of mobile services is having access to time-sensitive information. If you wanted to travel to, say, Baker Street, a service could take into account real-time public transport data, and find the best possible way to get there from where you stand right now.

There’s a further benefit -  if usage of these kinds of services reaches critical mass, we will suddenly have much more efficient ‘load balancing’ across all kinds of services, as people can find pubs, buses, facilities etc that are below peak capacity.

Curated choice

When we seek something curated, we use a trusted curator – be it an individual or a group – to help us narrow the field of what to choose. We might listen to a particular DJ to find new music, rely on a few critics and a trusted friend to highlight movies we should seek out, or a community like Digg to direct our attention online.

Although we can already choose places based on a trusted curator – reviews of restaurants being a common example – LBS will still massively improve this type of location-finding. The ease of access to this information while on the move makes it much more appealing, because we can discover interesting places that happen to be close to us that we wouldn’t usually make a specific trip to see, and we don’t have to print out a map in advance.

Possible curated services could include places where a particular celebrity has been spotted, shops that your Facebook friends spend a lot of time in, places that have been mentioned on Boing Boing, great examples of grafitti identified by a Flickr group, or instead finding places to avoid by overlaying geographic crime data.

Similarity-based choice

In this strategy, we narrow the field by looking for something similar to things we have enjoyed before. Musically, this would be when we try music in a genre that we already know we enjoy. More recently, Pandora.com uses a team of musicians to classify thousands of songs, and is thus able to stream music to a listener that is similar to a given song or artist that they choose. Of course, because similarity matching works in the same way for everyone, this is one of the most useful ways shops can lay out their wares.

When it comes to LBS, passively recorded data of places you tend to visit can be used to find similar locations, and alert you when you happen to be close by to somewhere of potential interest. For example, if you visit modern art galleries whenever you are in a new city, a similarity-matching algorithm could notice this, and any time you are 10 minutes away from one you’ve not yet discovered your device could alert you.

In true permission-marketing style, you might opt-in to a service from Wagamama that will alert you if you are within 10 minutes of one of their restaurants and it’s between 6pm and 8pm. Similarity matching could then take this further by identifying nearby places that serve similar food. If you prefer niche rather than chain clothing shops, similarity matching can identify this and point them out to you. (Indeed, a key factor in the success of chains such as Starbucks or HMV is that people can choose to visit a new one knowing roughly what to expect. LBS has the potential to provide much better information about shops you’ve never heard of, at the very instant you are trying to decide whether to go in or not. As LBS takes off, chains may find themselves under threat).

More advanced similarity-matching algorithms might identify (from automatic time/geo-tagging of your Flickr uploads) that you often take photos of sunsets, and could let you know when there’s a beautiful view just around the corner. They could identify pubs/clubs that are frequented by people like you, or find public transport routes that match your preferred balance between scenery and efficiency. Wherever there is tagged location data, similarity-matching will naturally arise.

Profile-based choice

This type of choice has only recently become an option at all. Not so long ago, the closest we could get would have been speaking to that one employee at the record shop that who knows what you’ve enjoyed in the past and can identify what new music you would love.

This sounds like a curated choice, but there is an important distinction. When we choose something curated, we leverage the fact that we know and understand someone’s way of thinking; in profile matching, we instead use the fact that someone (or an algorithm) understands our own way of thinking.

The modern version of this service is provided by Last.fm, which processes the listening data of thousands of users in order to predict for any one individual what other music they may enjoy. One of the reasons this works so well is that Last.fm can capture the music you listen to through your PC or laptop. This gives it access to a greater quantity and quality of data than if you had to manually tell it your preferences.

Similarly, LBS could track your everyday movements (both real and virtual) and use these to build an accurate picture of your preferences. If sufficiently well programmed, it can then make intelligent recommendations. You watch Sci-Fi films at the cinema and go to and Forbidden Planet – you might like this obscure retro collectible shop that’s down the back street you are about to walk past. You go to art shops and once went to Amecon – you’d probably like this manga art exhibition. You often move at a speed consistent with skateboarding – there’s a great skate location just around the corner. You favourited a Banksy book on Amazon – there’s a Banksy on the street across from here. You subscribe to techy RSS feeds – Inamo, a restaurant with a digitally projected table interface, has a table free for two right now and is 2 minutes away.

This is the top of the tip of the iceberg

Those were just a few examples of using LBS to help choose something, but the potential generalises so much further. There are many more possibilities for each of the four strategies outlined, and you can always mix and match these strategies. There will of course be applications beyond choosing, and beyond using just the location data of your own mobile device. Then there’s the possibilities offered by platforms like Mscape, enhancing the real world with parallel virtual worlds. New business models and new marketing opportunities will inevitably emerge.
So what can we do right now?

Despite relatively low penetration of suitable mobile devices, the conditions are already in place for an LBS killer app to emerge - and this would then drive further support. Although several mobile operating systems already have the potential to support LBS (including Symbian, the dominant OS among smartphones), the greatest opportunity right now sits with the iPhone and the G1. This is because they also each operate a single hub which makes downloading applications easy - the iPhone App Store, and Google’s Android Market. Each has its limitations – iPhone apps can’t yet run in the background (crucial for passive LBS), and the Android Market is some way behind in terms of maturity. But these hurdles can be overcome very quickly.

Keep an eye on interesting and well-implemented LBS applications that are coming out right now. Or better still, make one.

November 7, 2008 - Posted by mantim | Uncategorized