UberMedia Aims To Help Marketers Link Sales Data To Campaigns In Real-Time
With new tool Location Visit Optimization, “we’re linking campaigns to what matters most to marketers: retail store traffic,” says CMO Michael Hayes.
Cross-screen mobile ad platform UberMedia has launched Location Visit Optimization (LVO), a targeting tool designed improve mobile ad relevancy and performance by analyzing consumers’ in-store visits.
With LVO, UberMedia aims to use its first-party location data to determine the success of the ad campaigns by analyzing which people who saw an ad actually made a subsequent visit in-store, and then to target ads to similar audiences accordingly.
And while UberMedia believes that mobile advertising has come a long way, marketers still often struggle to link their sales data to campaigns in real-time. “LVO changes that by optimizing on what matters most to marketers,” says Michael Hayes, CMO and CRO at UberMedia. “And that’s real-time, real-world location visits.”
GeoMarketing: How does LVO benefit physical retailers looking to boost sales?
Michael Hayes: With LVO, we’re now linking campaigns to what matters most to marketers: retail store traffic. Historically, digital ad campaigns and mobile ads specifically have been optimized on what I would call media outcomes, whether delivery or click-through rate, [that don’t connect] necessarily to in-store visits or purchases.
This really starts to change the game, in my opinion, because you can run in-app ad campaigns — which is the majority of where consumer time is spent — and you can measure it and track it and optimize it to an actual store visit, hotel check-in, etc. [Brands can] both find consumers and understand them better.
Does LVO target consumers based on real-time location data, historical geo-data used to build profiles, or a combination of both?
It’s a mix of both, depending on what the advertiser wants.
So, if you’re a quick service restaurant, the optimizer runs in real time so it’s using real-time location data. When it runs an ad and then it sees that ad is run to a specific device ID, does that device then make it in to a, say, local McDonald’s? If it does, then at scale it tries to find more people like that and uses [that audience] to drive more incremental traffic. That would be one use case.
Another use case may be if a [department store] wanted to advertise this holiday season to all the folks that went into their stores as well as shopping malls last holiday season. We have that historical data, and the brand could optimize on the store visits to that specific location that they find interesting.
Can you provide an example of a brand currently testing LVO? How are they using it?
So, the categories we’re testing are automotive, quick service restaurant, and hotels.
In the case of our client [hotel chain], they’re a mass-market chain. They have tons of locations around the globe and, with that, they know what people do: They’re on vacation and they pull over and they stay at that hotel. Most of their clients are booking through walk-ups, not through a travel agent or the website.
Meanwhile, we know that there’s no significant correlation between click-through rate and a booking. So what [LVO can do for them] is to measure the people that are at those hotels very accurately, and then go find more of those people and target them with the right message. That’s incredibly valuable to that hotel, and that’s what we’re doing.
What distinguishes UberMedia? And how will LVO be uniquely used going forward?
I think what really separates us are two things. Number one, the optimizer on real world visits which we already talked about.
Number two is that, in order to do that, you have to have a few key elements. One, you have to have huge amounts of location data because you can’t optimize on one or two visits. You have to have huge amounts of location data and process that in real-time, and then you also need a learning system that allows you to understand the context of where people are.
Everyone says, “I’ve got location data.” That’s true, but if you don’t understand specifically what that lat/long is and where it is, you’re going to have trouble distinguishing between visits to, say, the Toyota car lot and the Honda lot next to it.
We do that through our polygon technology. Our polygon technology that allows us to, through satellite imagery, custom polygon any location that is accurate within three feet. We can tell if you’re at the hotel or the Starbucks specifically. We have that pretty well figured out with these polygons, and that includes eighteen thousand car dealerships, the hotels we mentioned, and all the current QSRs, as well as their competitor QSR locations.