It’s estimated that investments in Connected TV (“CTV”) grew 40.6% year over year in 2020. Advertisers and marketers have their eyes on this as a hot possibility for connecting with their target audience and rightfully so as more people have moved away from legacy linear broadcast television viewing habits.
In Data Point of View, Episode 102, Mobilewalla CEO Anindya Datta and VP of Sales-CPG, Jim Mahoney, discuss the new nature of this tactic and the innate challenges with data capture and analysis that will prove problematic for advertisers. While the movement of eyeballs to CTV has driven budgets to follow that movement, it’s not an equal transition of accurate data. There are many more complex factors to think about when advertising through CTV.
Since it’s not comparing apples to apples, marketers must consider unique practices and influencing factors when evaluating ad spend and performance. While it’s likely that new methods of capture and measurement will emerge, for now there are more questions than answers. At the end of this post, we share how Mobilewalla helps address several of the CTV data challenges outlined below.
Key Insights
Scale should be the first concern for users of CTV data to consider going forward
The number of signals and unique devices that exist in this space are much lower than from other digital sources such as mobile, for example. This presents a unique obstacle for users of this data who now have a smaller pool of useful information to use in their decision-making and evaluation processes.
The ability to disambiguate users in the CTV world is much more challenging than in other digital landscapes
Because it’s so much harder to break down who is using what with CTV, targeting through CTV marketing will get much more difficult. It is much harder to know that the household where this TV exists has four individuals. And what is the nature of those individuals.
The future might include combining CTV data with other things
Asking and answering some other questions might be key for the future of data usage in this space. Things like what household does the CTV belong to? What other digital channels are being used in the household? And if you can do that, then you should slowly be able to combine CTV signals with mobile signals to create really powerful insights.
There are three critical differences between CTV and other data that every marketer should be mindful of when committing ad spend. The closest data to CTV is from the mobile context.
The first is with respect to ID in mobile. In mobile data, you see a unique addressable ID for the device that the consumer is using. If you use 10 different apps, when data comes out of each of those 10 apps, they refer to the device using the same ID. CTV IDs have heavy fragmentation on their own. If you are viewing content on a specific TV unit from two different apps, chances are very likely the two apps refer to the TV using two different IDs
The second difference is that there are far more apps on mobile devices that you can gather data from than on CTV. But there are many more device models in CTV than in mobile.
It’s also harder to tell who is in the households and who remains there as a consumer at a certain point in time. Both Jim and Anindya believe that household occupancy is a serious obstacle in CTV right now. In the mobile world, locations change much more than in CTV because people don’t carry their TVs with them the way they carry their phones. In the CTV world, because of fewer apps, you have a harder time disambiguating people because of lack of variation in location data.
Footfall attribution is a top-of-mind concern due to the breakdown in evaluation opportunities with CTV. CTV data on its own doesn’t currently allow helpful capture in this way, it must be connected to other data to understand subsequent behavior. If you can prove the coexistence of a mobile device and a CTV, and that they belong to the same household, then attribution becomes possible and more accurate.
One of the core components of any marketing campaign is coming back after the fact and determining if the campaign worked. But that’s harder now because some exposure data is unavailable behind large walled gardens, and transactional data (in CPG) is typically limited to panel data that may not have sufficient scale for statistical reliability.. This is a big challenge especially as it relates to scarcity and fragmentation because the data must be analyzed separately from measurement with other campaigns or avenues of distribution.
How Mobilewalla Can Help
Mobilewalla can now help clients and partners better understand CTV app behavior at the household and individual level, facilitating better targeting, audience creation, activation, and measurement (including attribution). This unique data helps CPG clients and other verticals do more effective marketing.
Mobilewalla has access to one of the largest connected TV (CTV) data sets in the United States, consisting of over 500M unique CTV devices across 80M different households.
For each unique CTV device, we have access to:
- The IP addresses it is attached to, and the time periods of such attachments
- The apps on it through which content can be consumed include:
- Video Content: Pluto TV, Tubi TV
- News: Newsy, Foxnews
- Sports: Flosports, FuboTV
- The usage of these apps while on the CTV device.
- Device type:
- TV: Samsung, Vizio, LG, Philips, etc.
- Gaming Console: Xbox, Playstation etc.
- Set Top Boxes: Apple TV, Roku, Chromecast etc.
- Various digital IDs of this TV as manifested via different Apps
- The household the device is attached to and information about the household:
- Location (lat/long)
- Household Members:
- 100+ attributes about each individual member (demographic, behavioral, etc.)
- For each signal received from the device, the likelihood that a given household member was watching
