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While Americans may not watch TV on their phones, they do watch it with their phones. In their hands or on the couch next to them, that is.
The phone is an easy way to IMDb an actor, google a reference on a show and, of course, look up and possibly buy a product you’ve just seen in an ad.
The problem, at least if you’re a brand, is that it’s been tough to get much in the way of actual data on what the people who saw your commercial on TV did on their phones once they’d seen it.
While it would seem to be an obvious metric for brands to have, obtaining cross device data is not easy, as TV and mobile data sets are rarely owned by the same companies and rarely shared.
There’s also the fact that TV networks and streaming platforms tend to downplay the amount of concurrent activity that takes place on viewer’s phones.
That may be because they’re afraid to admit that viewers aren’t always paying rapt attention to what’s on the TV, or because they don’t want to create the impression that people are watching TV on their mobile devices rather than on an actual TV set.
Whatever the reason, looking at mobile data to understand how streaming TV ads are working makes a lot of sense and can help fill in gaps in marketer’s understanding of how their campaigns are performing, while also allowing brands to more wisely allocate their marketing dollars, running ads on mobile apps that people who have seen their ads are likely to use, and doubling down on streaming platforms that are driving the most traffic.
That’s what’s so interesting about a new insights platform called AppScience. It combines streaming TV data and mobile data from a proprietary household graph of 300 million mobile devices and 110 million connected TV households to help brands understand what streaming TV viewers are doing on their “second screens”, and whether what they’re doing relates to the ads they’ve just seen on TV.
Three obvious use cases for this type of data are auditing existing campaigns to see how well they are working, and developing more advanced audience segments marketers can then use in future buys and running ads on mobile apps that audiences who’ve seen and responded to a TV spot are more likely to be on.
So, for instance, it will be easy for a brand to tell which platforms drove the most mobile traffic to their site or spurred the most people to search for the brand.
It can also let them know who exactly they are reaching on the various platforms. This is a key piece of data for streaming TV platforms, where so many are still new and advertisers don’t have a real sense of who they’ll actually reach. By analyzing the mobile data that AppScience collects, marketers can better tune their media buys across various streaming platforms.
Similarly, a brand can use AppScience’s data to identify which market segments were more likely to respond to their ads. That allows them to refine the demographics they are targeting their ads to—either because the brand is over-indexing against that segment or under-indexing.
One Source Means Greater Accuracy
One issue we often see with cross-device data sets is that at least one of the sets comes from a third party, which can make it harder for the company doing the analyzing to get accurate results.
That thought was echoed by Marka Hinkamp, VP of Sales at App Science, who noted that “App Science was created to address the lack of measurement and transparency in the market. Due to most industry leaders using third party data, there was critical campaign intel being omitted, leaving brands with only a fraction of the information needed to make data-driven decisions. By using our own data, App Science is able to provide brands and agencies with more accurate and timely data at a more affordable cost, directly combatting and solving these industry challenges,”
I suspect we will see more brands making use of cross-device data in the years to come, especially given that younger generations are far more mobile-oriented than their parents. While a 50 year old might use a laptop to research a brand whose ad they’ve just seen on TV, look for reviews, and (especially) make a purchase, a 25 year old is far more comfortable doing those tasks directly on a mobile device and won’t need to switch to a laptop in order to make a purchase. This behavior is going to force brands to look for correlations between mobile and CTV data, especially where the mobile device is used as a companion device.
As Helen Lum, Executive Vice President of App Science, noted, “Streaming TV viewership is proliferating at a time when people are increasingly making purchases on their mobile devices. These two datasets must be in lockstep to empower modern marketers to design attribution models and execute business outcomes-focused ad buys.”
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December 18, 2020 at 04:56AM
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To Understand The Value Of TV Advertising, Try Using Mobile Data - Forbes
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