Blis’, Head of Insight, Alex Wright kicked off this morning’s agenda at Place Conference London by providing insight into how location data can help brands succeed in a world where mass broadcast has continued, but mass consumption is increasingly rare.
In order to explain how location data can help brands, we firstly need to define what location data is.
Location data provides us with four types of information:
- Location – where something is
- Place – what this location is e.g cafe or shop
- Brand – what type of place this is e.g Pret or Waitrose
- People – how many, who they are, their interests and more
All of this information makes location data one of the most valuable data sets available for marketers, however, when it comes to marketing science, this data set is not being fully utilised.
Professor Byron Sharp, founder of the Ehrenberg Bass Institute of marketing science, and author of ‘How Brands Grow’. In his book, he encourages marketers to strive for Universal Availability — mental availability and physical availability. This essentially means keeping your brand top of mind and increasing purchase opportunities.
He recommends marketers do this by focusing on presence (achieved by optimizing all channels to maximize reach — both in media and retail), prominence (aiming to out-shout your competitors — make yourself noticeable), and relevance (achieving presence and prominence at the right time — i.e. when there is an opportunity to purchase).
Now, this is great if you’re a big FMCG brand, with a broad target audience, and an enormous marketing budget. But, what if you’re a sub-brand, or have a niche audience, or a lighter distribution of stockists, or longer purchase cycles? And with Sharp being against granular targeting, these brands need to be smarter about how they achieve relevance. But more on this later…
Until relatively recently, content and content platforms belonged to a handful of all-powerful media moguls. Until 1982 there was only one commercial TV channel in the UK, and tens of millions of viewers tuning in regularly — and simultaneously — to the biggest shows. It took another decade after that for us to get a third commercial channel, it was at this time that cable and satellite TV arrived in the UK.
Soon after came the domestic internet, which at the time felt like a revolution — waiting ten minutes for a page to load while nobody was allowed to use the phone — another device shared by the family and plugged into the wall.
From Media Owners to Media Consumption
The smartphone changed all that, (for all intents and purposes), with the launching of the iPhone in 2007. This signaled the real shift in power, away from media owners to the consumers who were no longer bound to a rigid and predictable ‘this content on this platform’ model.
It’s worth noting that Professor Sharp published ‘How Brands Grow’ in 2010, with smartphone penetration at around 30%, his research was based primarily on a bank of broadcast campaigns, which could explain why targeting felt like a waste of time. Before 2010, mass broadcast campaigns worked because mass consumption still happened. The X Factor peaked in 2011 and has seen viewers dwindling ever since.
This meant the role of the agency had to change. Yes, data had been used for planning and buying and optimization, but they were specialisms. In these few years, we saw the real shift away from glossy creatives and schmoozy account management, into data-driven planning and programmatic digital buying.
Out with the Mad Men misogyny, and in with multi-variant collinearity and data scientists. But even then, with all this new information at our disposal, we still fell back on legacy processes. We still put audiences into boxes, reducing their attitudes and motivations down and down until we got an over-index against a buying audience based on age and gender. If the whole notion of advertising is to influence choice, then isn’t it strange that we use the two measures people have no choice over to define them?
So, if consumers don’t think of themselves according to the labels we apply, why should they consume media in the way we predict they might?
The Illusion of Universal Availability
For every new tool, sample, supplier, model, or representative survey we use, we multiply the margins of error. Conveniently, the persistence of mobile device IDs, and the volume of observable devices means that you can profile an audience based on their behavior, buying impressions against that audience (those same devices) and measuring the impact of the impressions served.
Through movement and location data we can profile, buy and attribute behaviors. Looking at real-world behavior to create the Illusion of Universal Availability.
It means your brand doesn’t have to indiscriminately maximize reach, or shout the loudest — which both mean spending the most — it means you can talk to the right people, with the right message, at the right time. These are the things that drive awareness, consideration and purchase intent.
We are even able to move beyond purchase intent to measure true ROI with partners such as IRI, and understand brand affinity through ODR.
The further down the funnel you go, the better campaigns powered by location data perform. If you’ve bought an audience you’ve profiled based on behavior that fits the desired audience in the brief, then you’ve got a better chance of getting people who are in-market, or may be in-market at some point. This gives your brand a head start over the competition, improving consideration.
But the real win is to drive purchase intent, and this is the real strength of location data in that your final nudge can be made when there is an opportunity to purchase. This fits nicely with Professor Sharp’s components of success:
Not the Only Source, but a Single Source
The better you understand behavior, the better chance you have to influence decision-making.
You don’t have to be top of mind all the time, just top of mind at the right time.
Keep going with the 1st party data, the market research, the DMP, the segmentations. They’re all important, and have distinct roles to play, but location data is a source that can often complement these, filling in some of the blind spots.
So while it’s not the only source, and nor should it be, it’s a single source, and having that red thread from profiling to attribution is extremely valuable for marketers and brands in better understanding their consumers.
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