Article written by: Bruce Burgess, Managing Director for Posterscope SSA
Location is a remarkably powerful razor for intent. The reason for this is that people don’t just go places around the city for the sake of it. In addition, physical locations typically have finite uses (stations are for waiting for transport, bars are for consuming and socialising, stadia are for watching sports and concerts, you go to a hardware store if you want to fix or build something etc.) which means that they tend to attract people with similar intentions or mind-set by daypart, regardless of their demographics. Intuitive examples of this: consumers travelling in an Uber to Braamfontein on a Friday night are likely on their way for a party; consumers in the Cape Town Foreshore area at 10am on a Saturday are likely in the market for a new car.
The upshot of this for marketers is that if you could know what the intention or mind-set of consumers are in specific locations, you could make decision about what the most appropriate product or service offerings would be to serve to them. We call this Out-of-Home Location Marketing.
But in OOH Location Marketing today, we don’t have to use personal experience or intuition about points on a map to make assumptions about consumers’ mindsets in locations anymore. We have mountains of data. The data that we use is a combination of two things: audience insights overlaid with geo-location. An appropriate descriptor for this is Geo Audience Insights (GAI).
As consumers do more in and share more about their offline lives via their mobile devices, they generate data points linked to locations that reveal insights into what they are doing, thinking about, feeling or responding to. The three main sources for the data that powers Geo Audience Insights are mobile exchange data, social sentiment and web/app browsing behaviour, but in short, GAI can be derived from any data source that describes the affinity of an audience at a particular geographical location.
Geo Audience Insights are a holistic understanding of aggregated groups of audience behaviour, and understanding the migration patterns of these groups. This is a significant step forward for the OOH industry as it evolves from demographic to audience targeting.
In other words, we can now use mobile data to apply the same concepts of online marketing to the physical offline world of OOH.
Let’s have a quick look at three different examples of GAI from South Africa, and what they can be used for.
A Social Sentiment Map: Where Are Consumers Mentioning a Specific Topic on Social Media
What you see here is GAI heat map of where consumers have been and are talking about a specific topic. The way that we use this type of insight is to identify and prioritise target locations for a campaign that appeals to a specific sentiment or mood. For example, Gautrain communication would be ideally targeted at hotspots where people regularly complain on social media about being stuck in traffic (you know who you are). Or more broadly, Coke could use this type of targeting to position OOH advertising in locations where people are regularly posting about Happiness, Sharing and Friendship.
A Mobile Adserve Response Map: Where Are Consumers Responding Directly To A Specific Offering
By tracking online actions such as mobile ad requests, responses, or even video views, we can start to attribute affinities of audiences in specific areas to specific category actions. For example, if we use the histories for a mobile video campaign for a series of movie trailers on behalf of Ster Kinekor, we can start to map out where people who like to watch movie trailers are, and use OOH in those locations to retarget and remind these high affinity audiences.
A Mobile Location Affinity Map: Where Does A Specific Audience Like To Go
What you are looking at is one part of a location affinity map for beer drinkers, based on the audience’s mobile location and social sentiment history. In other words, where do beer drinkers like to go?
For this study, we partnered with a data supplier who aggregated user data that was sourced from over 30 app categories and social media sentiment data. By interrogating this kind of GAI map we can start to help clients not only understand where to be planning OOH, but also where they should be prioritising trade support.
We are still only scratching the surface of the potential of the applications of location data. New applications for it are being experimented with daily, which makes this an unbelievably exciting time for consumer insights. While we are still working out what all of the potential opportunities are for location data and Geo Audience Insights, it has become quickly apparent that the applications stretch beyond OOH planning. Using digital actions to influence targeting in the real world is a really a great example of online and offline converging. We are already seeing potential applications of these tools for helping mobile adserve platforms identify high potential geo-targeting locations for campaigns where new economy clients don’t actually have store locations to target.
While we don’t know what insights or applications we will be pulling out in the future, we can say that Location Data reveals a whole new world of consumer understanding that will cross the offline and online words, and affect the future of both media and marketing.