What Marketers Need to Know About Location-Based Advertising and Where It’s Headed

6 experts from brands and agencies weighed in over Slack

Headshot of Lauren Johnson

Marketers have long envisioned a world where millions of people walking by a Starbucks store on a hot day are served an offer for an ice-cold coffee while browsing a website on their smartphone. That scenario, by and large, hasn’t worked out for brands so far due to complexities in ad targeting, challenges around reaching a sizable audience and questions about the accuracy of the location-based firms’ data that relies on bits of information pumped through technology platforms.
Still, there’s a lot of money to be made in location-based advertising. According to research firm BIA/Kelsey, U.S. marketers are poised to spend more than $16 billion on targeted mobile ads this year, reaching $20.6 billion in 2018. What’s more, brands are beginning to invest in location insights outside of advertising and into the burgeoning world of mar tech, where reams of data inform everything from what people are buying and how often they purchase to planning exactly where stores should open.
To dissect the ins and outs of what’s working, what needs fixing and where location marketing is headed, Adweek assembled a panel of six marketers from brands and agencies to hash out the issues for one hour via workplace messaging app Slack. Read on to see how the conversation unfolded.

Lauren ? [3:01 PM]: First question: For years, location data has promised marketers granular targeting to hit “the right consumer at the right time.” What are your thoughts about when and how to use location data?
Lisa ? : Location data today has many inaccuracies. We use location data with our retail partners as data points to inform decisions at the individual level.
Location accuracy: Measures how closely a company’s data matches to actual location coordinates in the real world.
Abbey: Location data is the digital bread crumbs of offline activity, so we use it in a variety of ways: for insights, segmentation, targeting, analytics and understanding if we drove people to a desired physical location.
Lizzy: I agree. I think everything is still just estimations. You can’t get 100 percent accuracy at any given time. What we have now is better than five years ago or even a year ago, but it’s not perfect.
Warren ? : Nothing is 100 percent accurate. It’s knowing how to use the data and understanding the sources you rely upon.
Brian: Agreed with @Lisa on inherent inaccuracies with location data currently available today, which is why we typically use geofencing to align HotelTonight’s use cases with people who are more likely to have a need rather than target based on a broad DMA location.
Designated Market Area (DMA): A population in a specific geographical area.
Warren ? : Geofencing is likely the most inaccurate of all location data sources. We rely mostly on first-party, app-based IDFA data.
Geofence: A virtual mapped area used by brands to target messages to people at specific locations.
Identifier for Advertising (IDFA): A piece of location-based code within iPhones used for ad targeting.
Brian: Interesting, why do you say that @Warren? It’s been incredibly successful for HotelTonight.
Warren ? : We use geofencing as part of a holistic solution to derive better data. For example, we can determine the difference between an employee and a consumer. Then we can match that user’s ID to ad calls.
Abbey: We used geofencing for an entertainment client to target people at Comic Con because we knew our show’s audience was there. That data drove activation, audience understanding and future targeting.
Lauren ? [3:06 PM]: Why is location data inaccurate today?
Adrian ?? : It’s all dependent on the device, browser and user … all things that are definitionally inaccurate at times. That’s why you mix location data with intelligence data (age, context, sequence, market, etc.). Location is one vector point, not all.

Patrón used three years of drinking data across 12 million users and 100 markets [with Foursquare] to get smarter about what spirits drinkers like. Then we fed that into AI-powered bots, Amazon Alexa and media units. The location data was the building block, but we mixed it with other tools. ?
Five to seven years ago, I was just using Foursquare data to get people to check in to stores for badges. Big change!
Lisa ? : @Adrian Agree ?. Triangulation of data is critically important in the digital landscape. Not all location data is inaccurate. However, precision and accuracy is key to scale, and using location data in a silo will not deliver the best consumer experience. In addition, the location of an individual might detect they are near or in a particular store. However, you have no idea [if there is an] intent to purchase.
Triangulation: The process of using multiple pieces of data to verify the accuracy of one piece of data.
We have many retailers that utilize location data in addition to other data sources to deliver the right product mix and creative messaging to their consumers. As an example, an outdoor clothing retailer would customize their messaging strategy differently for someone in Florida versus New York.
Lizzy: I almost see location data like ??. Most advertisers still use cookies and they aren’t always accurate. It helps guide your targeting. I also find issues with location data with search advertising. A lot of the accuracy is reliant on your patterns of behavior, which may or may not build a reliable profile. I think a huge differentiation now with campaigns versus five years ago is having the option to track foot traffic based on locations. Any step toward attribution with mobile in particular is huge.
Attribution: Determining that a piece of media drove a conversion.
Lauren ? [3:14 PM]: With vendors all pitching their own sets of data, what do you look for in location data?
Warren ? : We look for users who were exposed to ad messages in out of home, digital and TV, and then we can retarget, track them or create look-alikes.
Look-alikes: Piecing together anonymous data to create mini-profile groups that have the same characteristics as an advertiser’s goal.
Abbey: We look for partners to allow us to action against their data across as many platforms as possible and to be merged with as many additional data sets as possible. So for us, the best partner is a flexible one.
Lizzy: Agreed with @abbey. I look for a partner that can optimize based on their data that is frequently updated if not in real time. The data is useless unless I can optimize against it while the campaign is live.
Adrian ?? : Facebook and Google offer varying degrees of both, but it’s hard to use data outside their ecosystem, of course.
Lauren ? [3:18 PM]: @abbey Are you seeing more location firms open to sharing and mixing their data? A few years ago it seems like that would have been a no-go because it’s a competitive advantage.
Warren ? : We see this now. The data market is getting very crowded, so we are seeing partners selling and buying each other’s data in order to dimensionalize their offerings. Sort of an “if you can’t beat ’em, join ’em” mentality.
Abbey: Honestly, that often comes down to us doing the due diligence of looking at data ? side by side and triangulating ? it. What we really like are when partners don’t just make their data available on their own networks, but also unbundle it so it’s not just packaged with media. That’s a big plus for us.

This story first appeared in the Sept. 25, 2017, issue of Adweek magazine. Click here to subscribe.

@laurenjohnson lauren.johnson@adweek.com Lauren Johnson is a senior technology editor for Adweek, where she specializes in covering mobile, social platforms and emerging tech.