In this opinion piece, Nick Ballard (pictured below), managing director at Blis for Australia and NZ, explains how the myriad of location data can help marketers locate and engage with customers and prospects in today’s marketing landscape.

In its simplest terms, location-based marketing is a brilliant idea. After all, if we can segment by age, gender, and income – why not location? It may have taken mobile and wearables a little while to catch-up with the dream, but location marketing has now certainly hit critical mass.

To understand where we are now, it’s important to see where we’ve come from. I don’t know about you, but my first foray into mobile location was FourSquare. The app let you log in to a location and tell your network of friends. You could even become the ‘mayor’ of the bar if you checked in enough.

In themselves, these kinds of applications ultimately proved to be fairly limited in utility, and then Facebook ate their lunch with ‘check-ins’. By the time masses of Facebook users were declaring their locations (usually an international airport before an exotic trip, in my experience), the idea had gone mainstream.

Like anything that most social apps do, it wasn’t simply altruistic; they realised that location data was a very powerful tool for marketers to identify and build advertising audiences. However, in those early days, many marketers weren’t really aware of the scope of opportunity apps presented. While user adoption of apps was rapid, there was a flawed but common perception that location data was difficult to wrangle. This perception blinkered marketers from understanding the full value apps had to offer.

To start with, mobile penetration has gone through the roof, with reports suggesting that a staggering 2.2 billion people will own a smartphone by the end of 2017.

Adding to the technological scale is the human element. People have embraced all kinds of apps; they spend their leisure time adding locations to the billions of images and statuses, and they have embraced the utility of location in millions of daily interactions. Every year, more apps capture more location information than ever before.

Great stuff, but where are we now? How can this myriad of location data help marketers locate and engage with customers and prospects?

Traditional thinking would say ‘Okay, you know where a person is, so you can target them with an offer directly related to their latitude and longitude’. Yes you can, and that can certainly be incredibly effective.

But the smartest part comes when geo-location signals are used to build out audience groups. Aligning a user’s locations history to a client’s audience profiles creates a deep synergy – you’re using new tech to enact the principles behind any channel marketing strategy. The only difference is massively heightened relevance to the consumer.

Based on a collection of locations, say for instance regular visits to Fitness First, David Jones and the Qantas Business Lounge, it’s possible to build incredibly sophisticated audience groups offering insight and intelligence to marketers across all categories. From there, brands can deliver targeted messaging based on more than demographics or income. It’s a simple concept, but it certainly provides amazing returns.

That’s a pretty simplified view of location-based targeting and some of the incredible potential it can deliver, but what’s next for the category? Well, the in vogue answer is probably the rise of artificial intelligence and machine learning algorithms.

It’s an accepted principle of physics that the same object cannot be in two places at the same time. Put another way, a mobile phone will be where it says it is and its owner will likely be there too. Understanding how objects move through space and time provides ‘analytic superfood’, and applications range from astrophysics to simply building mundane consumer profiles!

I will digress for a moment, but based on this principle, Jeff Jones, the chief scientist of complex computing at IBM, created a model that can more efficiently predict the trajectory of asteroids over 25 years. These predictions are based on which of the celestial bodies have shared a certain “box” of space on the same day.

In the most simplistic terms, knowing exactly where and when the asteroids have been in one location – both individually and in relation to each other – and feeding that information into a sophisticated commuting algorithm, can make it possible to predict where the asteroids will be in future. We’re in the early stages of an era of computer learning that has unfathomable potential.

By distinguishing devices, understanding the locations they frequent and using predictive modelling, we can predict where they are likely to go. Imagine the opportunity for marketers to improve ad targeting as well as the overall consumer experience.

The combination of smart learning with ever-evolving and accurate data will deliver huge impacts for location marketing. The ability to deliver highly sophisticated and personalised advertising experiences to users at a genuine time and place of relevance is incredibly exciting.