Pre-packaged segments are giving way to the free flow of location data pipes
The history of location data has been largely written by a market that buys audience segments for look-back re-targeting. That means a DSP, DMP or agency trading desk, can buy a segment for their commercial use. Keep in mind, this data has already been prepackaged by the location provider, so it has little use other than for re-targeting — and there’s the opportunity.
Based on what smart buyers of location data are doing, there are excellent applications for this data for both verification of location, and attribution (the holy grail of digital advertising).
The key question every advertiser wants answered is: Did someone turn around and walk into my store to buy my shit?
Perhaps the best-known tech company moving aggressively into this space is Foursquare, which in 2016 introduced its Attribution product, which has since implemented by TGI Fridays, Flipboard and even programmatic advertising companies such as Drawbridge to connect digital and mobile ad experiences to store visits. The results have been eye-opening: clear evidence of a 12% lift in store visits as a result of a consumer’s interaction with ads.
Combined with the rise of simplified Audience Sharing connectivity via companies like mParticle, this has opened the floodgates for a new breed of ‘piped’ data, allowing brands to easily share specific audiences and locations data with each other while bypassing traditional data exchanges, cutting out the middleman, and improving ROI. But that’s really just the beginning.
As the pipe continues to open-up and near or real-time data becomes increasingly easier to share, the use cases for location data are growing quickly, setting-up a seismic shift in the marketplace.
Evolving use cases for location data
Brands can now leverage these location data pipes to create interconnected experiences that leverage consumer predispositions across the digital and physical worlds in new ways to drive in-store revenue and improve omnichannel customer satisfaction.
Online publishers, who have struggled to make their data meaningful to these same brands, are now positioned to offer them ideally-suited mobile audiences with a history of specific mobile behaviors, making it easier to foster partnerships that demand a superior market valuation from traditional bricks and mortar operations.
This has created a brave new world for marketers seeking to engage mobile consumers with creative experiences that are at once personalized, scalable, and friendly to the LTV of both parties customers.
“The use cases for location data go much further than simply re-targeting users by buying off the shelf, pre-packaged data segments.”
One scenario gaining momentum is using location data for modeling purposes (e.g. a buyer of location data using it to conduct matches and/or complete an in-house analysis of what they know about a user). A specific use case could involve mobile gaming apps offering retail brands high-value interactions with committed gamers who, for example, would be happy to watch a retailer’s rewarded video ad at a key moment in the game loop in order to advance to the next level of their favorite game.
Predicting tomorrow’s location data market
These new applications for location data share the common trait of being able to inform decision-making at the top of the funnel, greatly impacting how and who marketers target based on how many times a user has entered a physical store, and what they’ve done while there.
This will create a future where location data markets are built around buying and selling visits data like a commodity, and NOT on the simplistic exchange of pre-packaged segments. The result will be a tremendous shake-up that takes money and control out of the hands of trading desks whose business models may make them may slow to respond, and tilts the balance of power in the location data ecosystem towards brands and marketers.
That said, actually aggregating and making sense of the minutiae of the visits data — including things like Device ID, latitude-longitude coordinates, relational context for places and dates, IP addresses, dwell time, etc. — ain’t easy. So, while each of these data sources has great value as an individual commodity, extracting that value is very technically complex, leading to a surge of location-data based technologies and techniques.
By ingesting and applying visits data at-scale, clients are free to manipulate it like putty to form new market opportunities. As the business cases for doing so continue to prove themselves out and gain acceptance, the value of the data will grow, and visits will become firmly established as a commodity in the information economy.
Every company I speak to — Oath, Pandora, Pinterest, Blis, Ground Truth, etc. — have unique goals or challenges, it’s never ‘one size fits all.’ With raw visit data, they are free to use location as they please (and can do so because they have some smart fucking people that know what to do with it).
If you are not already thinking about visits data and how it fits into your location strategy, or you’ve yet to bring the people in-house to support that strategy (think: data scientists), you’d best get on it, because this is going to be the biggest, most critical shift in the young history of location data markets.