I get flack every time I say that. There’s this perception that if we can make a really, really good video ad, it will “go viral,” and then people are going to share it all over the place after they initially see it while they’re out and about, and our work is done.
Location-based video advertisers needs to take a cue from online advertising and understand that great content doesn’t do much if it’s not seen by the right people at the right time. Think about it. You know how when you visit a website and look at clothing, highly relevant clothing ads start to appear on other web sites? Why? Because online advertisers know your behavior and use that to tailor the right message accordingly.
Location-based video adds an extra layer to this degree of predictive audience planning because it delivers content not only when but also where. It’s not just targeting a chosen demographic—say, older adults—at a time they’re likely to be near a screen. It’s targeting those older adults at a time they’re likely to be near a screen, based on where that screen is in the physical world.
Let me explain: an antacid ad might be more effective just before dinnertime near a drugstore, for instance, whereas on a golf course, an ad for the arthritis medicine displayed early in the morning could serve as a reminder to this same audience that they’re still young enough to play a round (with the right pharmaceutical assistance, of course). A well-timed ad that’s ill-placed, or a well-placed ad that’s ill-timed, can hurt more than it can help. The important thing is to make sure that (a) the content being generated lends itself to a contextual environment (i.e. arthritis on the golf course), and (b) the platform serving these ads is capable of this context awareness.
In order to do this, we need data. There aren’t (yet) cameras or beacons plugged into every location-based video screen, telling us who’s watching what at a given time. But through the use of mobile location data, we can analyze the way that consumers move—where they’re likely to be, and when. We can also easily ascertain, without the use of any personally identifiable information (PII), general user demographic information—age, ethnicity, gender, maybe even marital status or family size. So in my example above, if we know that a certain drugstore, located a mile from a retirement community, gets really busy at 4 p.m., and that most of the devices near that store belong to adults over age 55—that’s how we know to run the antacid video on a nearby screen. It’s not guesswork; it’s data science. No matter how good an ad for new athletic shoes might be, the video using a star basketball player to sell it simply won’t be nearly as effective in this location, at this time.
Context awareness in location-based video also makes for a great opportunity in adjacent content. Imagine an ad for a designer watch. It’s showing on a screen in a city’s busy financial district at 7:30 a.m., just in time to catch the early arrivals going to work. This is a highly connected group with a good amount of disposable income, and first thing in the morning, they’re less likely to be distracted by other matters, and more likely to pay attention to the video. This can be further reinforced by a call-to-action at the end of the video instructing them to visit a site or download a mobile app for more information. Because this adjacent content is accessed by consumers voluntarily, we get additional engagement and the opportunity to deliver richer, better content based on this deeper context.
Instead of blindly saying: “we think this is a good ad, let’s see what happens,” we can instead say: “we think this good ad will be most effective at this date and time”—and in addition: “we have the ability to make our campaign even more effective by offering adjacent content to the most engaged consumers.”
Try doing that with your “viral” video.