Inscape Smart TV Data: Unlocking Consumption and Competitive Intelligence

Summary

• Viewership insight: VIZIO's Inscape data provides comprehensive linear and streaming TV viewership from millions of connected devices.
• Marketplace access: Inscape data is now available through the Databricks Marketplace with real-time availability and flexible integrations.
• Real-world analytics: Organizations can measure streaming reach, match first/third-party data for ROI analysis, and conduct competitive share-of-voice analysis.

Inscape Smart TV Data: Unlocking Consumption and Competitive Intelligence

Talk by Rich Guinness, Associate Director, Data Licensing, Vizio Inscape
With VIZIO's Inscape viewership data now available in the Databricks marketplace, our expansive dataset has never been easier to access. With real-time availability, flexible integrations, and secure, governed sharing, it's built for action.
Join our team as we explore the full depth of this comprehensive data across both linear and streaming TV - showcasing real-world use cases like measuring the incremental reach of streaming or matching to 1st/3rd party data for ROI analyses. We will review our competitive intelligence through a share-of-voice analysis to provide the seamless steps to success.
this video will show you how to turn Inscape data into a strategic advantage.
Here’s more to explore:
Open data sharing for data, analytics, and AI: https://www.databricks.com/product/delta-sharing
A new approach to data sharing: https://www.databricks.com/resources/ebook/a-new-approach-to-data-sharing
See all the product announcements from Data + AI Summit: https://www.databricks.com/events/dataaisummit-2025-announcements

FAQs

What type of viewership data does Inscape provide?

Inscape provides comprehensive TV viewership data from millions of connected VIZIO devices covering both linear and streaming television consumption patterns.

How can this data improve advertising ROI?

Organizations can measure incremental reach of streaming campaigns, match viewership to first/third-party data for ROI analysis, and conduct competitive benchmarking.

Is this data available in real-time?

Yes—Inscape data is available in the Databricks Marketplace with real-time availability and flexible integrations for immediate actionability.

What is Automatic Content Recognition (ACR)?

ACR technology automatically recognizes content watched on devices, enabling one-to-one relationships between consumers and viewing patterns similar to mobile tracking.

Full transcript

All right, yeah. Thanks again for taking the time to join our session today. Um my name is Rich Ginnis. I oversee sales here at Inscape. I'm also joined by who's our our lead data analyst here at Inscape. He's going to be coming up here in a little bit after I am I'm done with my spiel. But uh super happy to be here, thankful to Databricks to give us the opportunity to speak to you all today. Uh I've never given a presentation in a situation like this with headphones on, but this this this should be fun.
Looks like everybody's leaned in and ready to go. So um I'll First, I want to kick it off with a bit of like an interactive question. How many people here in the in the audience are familiar or have heard of Inscape data before? All right. You got a few. Okay. I'll do one more. How many people uh have heard of automatic content recognition or A ACR data? Okay. You got a few more. Uh a little bit of education to do. So, our plan is going to be to one, educate you about who Inscape, who we are, the type of data that we have, how you can leverage it, um as well as where you can access it most importantly, which is in the Databricks Marketplace now.
So, um to kick it off, a little bit of context on Inscape and Vizio. Inscape was founded in 2009. We were the pioneers of ACR, automatic content recognition technology, the first to develop it. At the time, we actually partnered with multiple OEMs, TV manufacturers, um to help them understand what consumers are watching on their devices. By 2015, Vizio was like, "You know what? Let me scoop these guys up because Vizio is on a mission to learn uh as much as possible about their consumers to ultimately deliver the best consumer experience."
So, they acquired Inscape. We fast forward a couple of years and we entered the data licensing market, particularly in the media, entertainment, and advertising industry. And you know, at the time it was a bit of a revolutionary thing because we had, you know, data from millions of TVs in terms of what people were consuming. And you know, at that time really TV data sets were really more leaned into like traditional panel-based methods where you use a small panel of people to project to national viewing.
So, you know, we we enabled advertisers and publishers to be able to do more with TV data cuz you can establish that one-to-one relationship with the consumer and the device, similar to like a mobile phone or or desktop. So, you know, it was our tagline at the time was like we can make TV digital. So, that's how we started and we grew the business. As time has went on, we've become more and more impactful in the measurement ecosystem.
So, all of the, you know, the currency providers for TV measurement in the marketplace license our data and build their products services on top of it. We partner with most of the large, you know, advanced measurement companies. So, think of things like, you know, a return on investment ad spend or cross-platform reach and frequency. Um you know, these are use cases where our data is really critical. Um we picked up an along the way, which you can win an for ad tech, if you didn't know.
Uh we actually have two Emmys, one for our excellence in data, the other for our our operating system at Vizio. And then towards the end of last year the transaction with Walmart was finalized, which is really exciting just for the future of Vizio and Inscape and future opportunities. Uh I got a little trigger-happy there. All right. So, what is Inscape data? Inscape data is Vizio's first-party smart TV data. Um it is device-level data.
Vizio's also unique in that we own the hardware, the television, uh Uh software, our operating system, and the data that flows between which is the Inscape data. Uh like I said, the data is at a device level. It's also reported at a session level. So, session meaning every time you're at home and you change the channel. So, if you went from, you know, ESPN to NBC on YouTube TV, those are two different rows of data, right?
And each each row of data is enriched with device identifiers as well as program and ad metadata, as well as information on how how you are watching that piece of content or that ad exposure, right? Were you watching it on YouTube TV? Were you watching it on a set-top box? Did you watch it through an antenna, right? So, we we can give really um granular source information. And the beauty of that and really the beauty of all of our tech and the tech being at the chipset level of the TV is that we're able to connect the dots across linear and streaming, right?
So, we're able to, you know, capture the full viewer journey from when someone turns on their TV to when they turn it off. Uh and like I said, be able to connect all of that viewing cuz it's as content gets more and more fragmented, it becomes increasingly harder to understand how and where people are consuming content. So, that's really where we've made a good um difference in the marketplace. So, how do you how do you work with us and what type of products and services do we bring to the marketplace?
We really have three pillars of our business. First and foremost is our TV data products. So, these are our our core products that our customers have relied on for eight plus years of licensing data to them. Our our two real pillars within this are our content feed and our commercial feed, which are pretty self-explanatory. The content feed reports on data in terms of what content's being played on the TV, and then the commercial feed reports data on the ad exposures with the content associated with that.
So, you know, what what content were they watching before or after they were exposed to the ad. Um so, like I said, these are these are our pillar products from a data licensing perspective. We've also continued to invest in our TV data products to future proof our self, make it easier for people to work with our data, make it more impactful for their use cases. So, you know, we've developed things like our national representative panel, which enables you to project our 25 million TVs to the total TV population.
Um we have our own personification product or our co-viewing product. We developed our own proprietary technology called our tuner data, which actually extracts data from the tuner in the TV to understand what channel the TV is tuned to, which has made our local our local uh measurement and data much more robust. Um so, this is an area we will always continue to invest in. Audience activation is a new and exciting pillar for us.
Um this is something that brands are super excited about. It's something that we haven't supported widely in the marketplace for the last 5 plus years. So, now we can have conversations with brands directly about how they can actually leverage our smart TV data and signals to reach their consumers wherever they may be. And then last but not least is is measurement. We've made great uh strides in you know, with our measurement products and services over the last 12 plus months.
We partner with some of you know, the largest publishers in this space, really helping them um solve solve problems related to that viewer journey I talked about, right? Cuz if you think about like a premium publisher, you know, they they have content broadcasted through over the air, through cable, uh to their direct-to-consumer app. People also consume that consume their content through over-the-top devices, right?
So, we're able to stitch all of those different um platforms and distribution into one holistic data set to help them understand things like, "Hey, what's the total size of my audience across all these different different distribution?" Or what's like the deduplicated reach and frequency of the of the ad exposures across all of this different uh distribution. So, that's where we've been super impactful. Okay, one more here and I'm going to round it out and pass to.
But, speaking of all the innovation we've done, um enter Databricks and our Databricks partnership. You know, Databricks is a great partner of ours. We've been partners for well over a year now, really, when we moved all of our data over into their environment, which was really a no-brainer decision for ours just based on the plat- you know, the unified data and AI platform, the simplified infrastructure. If you think about how massive our data is, you know, TV data from 25 million TVs, we need to be able to move that data around, quickly process it, and get it to our customers as quickly as possible for insights.
So, that's where Databricks has really made a a big impact for us. We're also now supporting Delta Share, which is, you know, Databricks cloud-based sharing delivery, which is saving Customers have leaned into already in just the short time that we started to support it, cuz it really helps them save save time and money in terms of receiving the data, right? Cuz you don't have to do a lot of that unnecessary ETL and processing that our customers have had to do with more traditional-based delivery methods.
And then, third, and certainly not least, is we are now part of the Databricks marketplace, which I'm clicking to. Okay, I'll get there in a second. Um but anyways, so now we're part of the Databricks marketplace. So, you can actually go into Databricks, you can see our listing, you can read up on the data, learn about what it is, what you can do with it. We also have shared some notebooks in terms of how to work with the data, you know, best best practices there in terms of getting to the insights that you need.
And you can actually download a real sample of the data, so you can explore it in your environment and see how it can benefit you for your uh you know, advertising or marketing use cases or whatever they may be. So, super excited about that. Really excited about the partnership. Please check out the listing. Um feel free to, you know, contact me directly after this session or once you get home if you want to talk further about it.
So, at this point I'm going to pass it. going to show you really how to get the to maximize the value of the data and give you a real life illustration of what the data looks like in practice. Thanks. I hope I left you enough time. 10 minutes. All right, you guys can hear me okay? Okay, thanks Rich. Um So, we heard Rich talking about the three pillars of Inscape's offerings. We have the TV data, audiences, and measurement.
In this next section that I'm going to be going through, we're going to be homing in on that measurement piece. Um so, here with talking about Inscape's data measurement capabilities, it's it's really an all-in-one solution for generating insights across the whole campaign cycle. Whether it be pre-planning, mid-campaign, or post-campaign reporting, uh the data can help optimize for waste, duplication, uh efficiency. It can help find untapped audiences, as well as, you know, searching for better allocation between media channels, whether that be streaming, cable, or local TV.
Um but to help provide provide some more context, I'm going to go over a case study that we performed on behalf of a local California-based advertiser um in the form of a pre-planning report um with a specific focus on that question of allocation between linear and streaming. So, here this is our um campaign overview. This is like that bird's-eye view of how a campaign performed. And in this case we're looking at a around 2 months worth of of a previously ran campaign for this California advertiser.
We're just looking here in California. And so, from left to right we can start off just looking at the overall picture of the impression distribution here. Um so, you can see how much we we we detected between linear and streaming, about 71% of the impressions came from streaming. Next here, we can see that the reach and penetration within California overall the campaign got to about 37 and 1/2%. Um majority of that coming from streaming, nearly double that of linear.
And lastly, we have uh these average frequencies broke out between the overall uh period as well as uh the uh the weekly average frequency. So, this is a good starting point to get a sense of, you know, where where we might want to go in terms of uh you know, starting to develop a smarter plan and optimizing for future campaigns. And so, uh you know, with those um you know, two different media channels, right? The streaming or linear.
I I think an an important question we'd like to answer is where was that duplication and how much did it occur, right? Uh and so, if we take a look here on that first slide or that first uh pie chart on the left, between linear and streaming exposed TVs, 15% of them were exposed to both, right? So, that would be uh a good portion of the exposed audience that was actually, you know, uh non-incremental between uh the those two different media channels.
And we can see that represented here in terms of uh an actual reach to the state, where about 5 and 1/2% of the TVs in that state were ex- were exposed to uh uh to an ad uh from the campaign and on both streaming and linear. So, uh you know, whether the you know, this meets um or exceeds their expectations, it's good to be able to see this to you know, understand where to go from here in terms of, you know, maximizing that efficiency and reducing duplication.
In that same vein here, um we're breaking up the linear and streaming uh impressions by their average frequencies. Uh on the left side here, we have TVs that were exposed again to only linear, only streaming, or both, and you can see how that stacks up. And I but I think more importantly here, we can curate this average frequency distribution, right? So, on the bottom here, we have the average frequency for a given TV, and on the Y axis here, we have the share of all TVs exposed, and we can see here over the 2 months of this campaign window that we were looking at, and nearly 30% of TVs were ending up being exposed nearly 10 plus times, right?
Uh you know, depending on the what what their goals were, right? That could That could be something to that might be eye-popping and to be looked into. I think most importantly here, we were able to see that you know, the TVs that were ex- exposed to both the linear and streaming portions of the campaign actually exceeded that of the share of TVs that were exposed only on linear. Right? So, that's a that could be a uh an important stat to keep in mind when, you know, deciding how to better allocate in the future.
Uh this slide is just, you know, specifically focusing on that linear allocation, you know, between local and cable. Um we think this is one that's is pretty powerful here. Um this is the incremental reach by network. Um so, to read this, we graph the uh the uh the linear affiliates, whether they be cable or local, um on the bottom here on that X axis by total reach for the campaign, right? So, in this case, NBC um had the furthest reach within California for this advertiser.
Um second, ABC, and so on. Um and then there on the Y axis is that penetration to the state. Um the entirety of each stacked bar is the total reach for that network, right? So, the the light portion plus the orange portion there would be the total reach for a given network. But, uh as you can see how it diminishes, the orange portion is just what's incremental with each subsequent network. So, for ABC, only 2. 4% uh incremental reach was gained on top of NBC, right?
The rest there, 3 and 1/2% was actually duplicated. These were TVs that were already exposed um uh from the NBC inventory. And subsequently, CBS, 1. 1% were exposed incrementally, while the rest of them were uh were not, and they were duplicated. So, uh this is an important, you know, fact when when buying linear that especially in, you know, this current marketplace um you you actually run out of space real quick. Um there's eventually you really do lose out on incrementality while of course you your average frequencies will continue to rise.
So, that's that green line we have overlaid here that even as incrementality continues to decrease average frequency continues to rise. So, I think this is a you know, a powerful um metric that our clients have been able to take advantage of um to you know, identify those those are specific networks that could be be, you know, right for reallocation here. Lastly, we wanted to be able to highlight um you know, the geographic data that Inscape is able to provide, right?
So, on top of the content metadata that we're that we're collecting, we also have TV information this at the zip code level. And so, in this case we're able to break it out by market and take a look at where you know, the where reach was distributed between linear and streaming. So, you can see here that uh the California's reach saturation was highest in Bakersfield for streaming while it was highest in here San Francisco for linear.
So, you know, this has helped to figure out how to reduce those blind spots and you know, guide smarter geo targeting for future campaigns. And so, with that uh thank you and I hope you guys have a better understanding of what we offer right now.

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