TechBytes with Jodie McAfee, SVP, Sales and Marketing, Inscape
SVP, Sales and Marketing, Inscape
The flywheel of Automatic Content Recognition technology is transforming rapidly. TV viewing data is currently at the height of digital advertising analytics. Inscape accesses millions of TVs across a wide range of content sources and delivers the data the same day. This helps adtech buyers and publishers to gain a more granular and comprehensive understanding of audience viewing and engagement across both content and advertising. To better understand how content recognition technology benefit in ‘audience building’ and ‘targeting’, we spoke to Jodie McAfee, SVP, Sales and Marketing, Inscape.
Tell us about your role at Inscape and the team you handle.
As the SVP of Sales and Marketing at Inscape, I lead a small, bi-coastal team that works with agencies, networks, and TV ecosystem companies focused on the buying, selling and measurement of media. I also work with TV manufacturers- from our parent company VIZIO to other OEMs around the world on the proliferation of Automatic Content Recognition (ACR) technologies.
I work with a team of TV technology veterans focused on bringing transparency, accountability, and usefulness to the media market by selling accurate, fast, clean opt-in TV data. We are watching this data disrupt all parts of the marketing industry.
How do you measure TV attention? How do your metrics differ from those of Nielsen, Rentrak or other providers?
Inscape takes a different approach to measurement than Nielsen. We provide a constant stream of activity data gathered directly from the glass of 8+ million TV sets. That data gets promulgated into the ecosystems of agencies, TV networks and feeds a growing ecosystem of next-generation measurement, attribution and cross-platform ad targeting companies changing the industry right now.
Nielsen issues reports taken from panels and they do so much more. It’s a giant company and they are good at what they do. The TV business was built around it, we see Inscape data as complementary to the data Nielsen provides. Other media measurement companies like Rentrak and some set-top providers have some blind spots that our data complements as well.
How does Inscape’s content recognition technology benefit in ‘audience building’ and ‘targeting’?
Automatic content recognition technology uses screen level measurement to identify what programs and ads are being watched and then streams that data in near real-time to Inscape partners. Think of us as a firehose. Inscape’s comprehensive metrics deliver highly accurate, real-time cross-platform viewing behavior at scale. We arm the industry with the jet fuel that enables stakeholders to move faster, smarter and more accurately. We help our customers develop a deeper understanding of audiences, make more intelligent ad-buying decisions and be better prepared for changes in the marketplace.
What are the major challenges and opportunities in the media buying ecosystem? How do partnerships with Data Management Platforms (DMPs) help in overcoming these challenges?
There are many challenges, especially when it comes to TV, mostly to do with the legacy systems the industry was built on. Executives know they need to change their systems of advertising, measurement and targeting to be faster, more accurate, more granular – more like the internet. But changing those systems requires some re-wiring.
Another challenge is moving from heavily modeled systems on simple DMAs to more advanced preference models built on deterministic principles.
Think about the connected TV– the anchor device in a home that unifies the family preferences. Getting the data right there opens up the systems to get it right elsewhere in the marketing value chain. If marketers are using TV data that is heavily modeled, it’s bound to be messy, inaccurate and contain huge inefficiencies. The flip side of that is marketers, instead of building models from a panel from tens of thousands, can now use a huge population of 8+ million single source opt-in TVs. Using a huge panel allows partners, like Lotame, to enrich their other data sets with a solid foundation.
That kind of data quality will not only lead to better engagement, and conversions for brands, but better user experiences for consumers. It’s not enough to just deliver conventional demographics and geography anymore, that’s why Inscape delivers unique identifiers to create a more comprehensive understanding of engagement and viewing habits.
How should marketers better leverage Connected TVs and opt-in TV devices to meet their marketing goals?
One of the great things about working with fast, granular TV data is getting to see first-hand how much disruption it creates and how much value companies derive from the data in different ways. Now that ACR TV data is making its way into the marketplace, we’re seeing some amazing results.
For example –
Partners of ours such as 4C are using the data to inform social media buying and helping large agencies make smarter decisions. While others are using the TV data to inform re-targeting strategies via digital media channels.
iSpot.tv is combining our data with its huge catalog of TV ad metrics to deliver attention scoring for TV advertising– essentially giving brands an accurate read on the interruption rates for all of TV. That kind of insight allows brands and networks to optimize how many and which type of ads to run. iSpot and others are also mapping TV exposures to digital activity with great precision- so brands can finally open the black box on TV spending and get a real sense of ROI.
Sorenson is using granular TV data to help local TV broadcasters keep tabs on how show segments are performing and monitor tune-in. And even more fascinating, it is using the granular data to lay the foundation for addressable advertising.
How do you leverage AI/ML capabilities at Inscape? To what extent do you rely on automation technologies to deliver audience insights to the customers?
Inscape uses advanced machine learning and automation technologies to capture screen level data from millions of televisions and then matches that data to programming and advertisements to identify what viewers are actually watching. This not only helps track what shows people are responding to but whether or not they continue to tune-in during commercial breaks.
Thanks for chatting with us, Jodie.
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