Together, Turner and Tunity Advance Out Of Home Measurement

By Media Insights Archives
Cover image for  article: Together, Turner and Tunity Advance Out Of Home Measurement

Turner's CNN Airport Network recently announced a new initiative using Computer Vision and Deep Learning technology developed by Tunity that has the capability of adding a new dimension to out of home television viewing. How often are we at an airport or at bar and can view the screens but can’t quite hear what is going on? Tunity’s download-able app enables viewers to also become listeners. Read on to learn more about this exciting new development in the OOH experience.

Yaniv Davidson (pictured at right), Founder of Tunity, believes that it is possible to attribute actual listening to specific out of homeviewing. “Turner’s bold decision to adopt Tunity and promote it first to its CNN Airport Network viewers opens up a whole new era in out of home viewing,” he explained. “Viewers will now be able to receive crystal clear audio via their cell phones at the same time they see the video on a live TV set.”

“Our goal is to constantly improve the passenger experience by bringing air travelers quality news and entertainment programming wherever they may be in the airport,” explains Debbie Cooper (pictured below), President of Turner Private Networks. “Bringing a technology like Tunity to CNN Airport Network allows us to better serve passengers, allowing them to be engaged with the screens not only with a visual but with clear, synchronized sound at their personal volume level. We know through years of research that if someone cannot hear the audio clearly they are less likely to pay attention. Tunity is a unique opportunity to take advantage of the technology that many of us use every day -- a smart phone and headphones.”

I recently spoke to Yaniv and Debbie about Tunity, Turner, out of home measurement and much more.

Charlene Weisler:  What is the unique advantage of using Tunity for television out of home measurement?

Yaniv Davidson: Currently there is no good metric for TV out of home because there is no technology out there that can capture who is watching what and where. For example, the portable people meter needs to capture audio, which either doesn't exist in out of home, or exists but might be just a background noise to a consumer that is merely in the TV's general area. Tunity can prove that a viewer is actively watching a TV, when and where. It is not an anecdotal piece of information, like a survey; it's hard, detailed data about real viewers. It can be the first platform to shed a light on an audience that's never been measured before.

Charlene:  How do you expect CNN Airport Network will use the information gained from Tunity? 

Debbie Cooper: The data sets that we are creating with Tunity will give us insights into who our audience is, what they are interested in, and maybe even someday where they are headed on their next flight. The strategic knowledge we get from this information will allow us to deliver a more personalized, engaging experience.

Yaniv: We are still in the phase of working on the data and we want CNN Airport Network to be a partner in that. We would like CNN Airport Network to give us their input so the end result is something they could use as a metric to evaluate how many [additional] viewers are watching a specific channel at a specific time in a specific location and communicate this to their potential advertisers. This is just the beginning. Eventually, I think that Tunity will enable content creators like Turner to push personalized content to viewers' screens while they are watching TV.  This will be based on who the viewers are, what they are watching at the moment, where they are and their past preferences (or preferences of viewers who belong to the same segment and are in the same context as them). Tunity can help Turner evaluate the effectivity of every piece of content by segment, location context, etc.

Charlene: Will CNN Airport Network get demographic or lifestyle attributes?

Yaniv: There is some direct data that we have -- like age and location -- and there is additional demographic data that can be extracted or added to the data we already have.  For now, we are focusing on location, age and gender, but there is a lot more we plan to add in the future.

Charlene: What makes this unique compared to other out-of-home measurement applications?

Debbie: For CNN Airport Network, it’s not just an issue of out-of-home measurement but constantly improving the customer experience.  If we can be more available to more people regardless of their proximity to a television screen, that’s a benefit to us and to the audience.  We will continue to use our other measurement tools and systems but having Tunity data will give us the best available data using the best technology in as close to real time as possible. This allows us to know who, when and where our audience is. There have been several times I’ve been at an airport, either in a gate-hold area, restaurant or lounge and I’ve seen one person see the promotion we’re running for Tunity that explains how it works.  They download the app and in a matter of seconds the audio is synched to the screen. Another few seconds pass and the person sitting next to them asks how they are able to get the audio and they repeat the download process. You can see the light bulbs go off as people experience Tunity for the first time. They have their phones out constantly; they typically have their headphones in, too. Tying those together with what’s on the screen across the room or seating area is a breakthrough for CNN Airport Network.

Yaniv: This is the first time a major media company is working to solve the issue of measuring out-of-home TV viewership and leveraging a new and unique solution to both solve a real issue for its audience and advertisers as well as extract unique data not available until today. Turner is leveraging the latest technology to statistically and scientifically measure what is a significant portion of their viewership. [This level of Computer Vision and Deep Learning could not have been done four years ago, as computing power was just too expensive and Deep Learning did not really exist.] It also solves an issue for their viewers on the go and the advertisers trying to engage with them.

The opinions and points of view expressed in this commentary are exclusively the views of the author and do not necessarily represent the views of management or associated bloggers. 

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