In our “Ask the iSpot Expert” blog series, one of our very own at iSpot walks us through their area of expertise in TV and video ad measurement.
Traditional TV ad buying and measurement systems were built on the premise that TV viewing occurs at a household level. And it did, in the beginning, when there was one screen per family and only a few channels to choose from. TV viewing habits have radically changed since then, but measurement has been slow to keep up with viewership. That is, until iSpot introduced person-level, cross-platform TV ad measurement.
Michael Bardaro, VP of Data Science at iSpot, and his team worked to ensure that person-level, cross-platform TV ad measurement would bring marketers the real-time, accurate insights necessary for efficient media buys that maximize ROAS. Below, Michael explains the benefits of person-level measurement, from verifying and maximizing impression delivery to ensuring optimal frequency and driving conversions with cross-platform campaigns.
Q: Can you explain person-level data?
A: Truth is — as an industry, we’re used to thinking of TV as this household-based ad delivery system. However, we know that TV is a very personal experience. We all have our favorite shows and genres we love to watch and our own ways of watching. Of course, these viewership habits don’t always line up between members of a household, which is why person-level data is crucial for accurate, actionable TV ad measurement.
Q: How does it impact TV ad measurement?
A: Person-level data represents an important paradigm shift regarding how advertisers understand their media buys. Data at this level really allows you to understand how people with certain demographic traits or behaviors view your ad. So rather than delivering ads to a household, you can uncover if you’re reaching the right individual.
Q: What insights can marketers gain from person-level data that doesn’t exist at a household level?
A: If you think about TV ad data at a household level, your insight is missing out on an extremely important degree of granularity. That is, which individual in a household is actually the person in front of the TV.
Imagine a scenario where there’s a 20-year old living with a 55-year old. That household will get flagged as a target for arthritis pain reliever. However, when the media plan is built, it doesn’t take into account that two people with very different traits and very different needs are in that household together. This household is now considered in target, and so programs like Ridiculousness or Floribama Shore are pulled into the media plan.
How effective is it to show an ad for arthritis pain reliever to the main person watching that TV when it’s the 20-year old? How about a whole group of people who are primarily under the age of 35? Do any of those people need, or care about relieving their arthritic pain? We all know that irrelevant ads are not effective and a waste of a marketing budget.
Now, what if 20-25% of your buy is targeting the right household, but delivered to the wrong person? Marketers need to know who is watching TV, not just the demographic makeup of the household itself.
Q: Why is person-level data important for cross-platform ad measurement? What challenges do marketers face without it?
A: Now we’re talking about making sure the ad is getting in front of the intended target, at the person-level across two delivery systems. Just as you want to optimize your budget as much as possible on linear TV, you have to make sure you’re spending efficiently on the OTT side as well. Having insight into person-level data across both platforms allows marketers to understand if they’re picking up incremental reach on that same target, or if they’re piling on additional frequency. Having these insights ensures efficient media buys.
Q: How does iSpot handle identity? What partners do we work with and how do those partnerships work?
A: We work with virtually all of the major DMPs on the market — companies like Neustar, Oracle, Experian — and we maintain active mapping between our TVs and their identity graphs. iSpot also maintains its own identity graph, where we’re anchoring the household itself onto the TV devices, so we really get the best of both worlds.
Through our partnership with Epsilon, we’re able to map demographic traits to the individuals present in the TV household. This unlocks traits like age, gender, income, education, for the people in the TV households in our panel. At this point, we’re still at a household level.
It’s not until we add in the data from TVision that we’re able to convert that household-level data into person-level insights. The TVision panel is closer in size to Nielsen’s, which is substantially smaller than the iSpot household TV panel. In order to maintain scale, we layer in TVision data to determine which person (and their associated demographic traits) is in front of the TV when an ad plays. We then combine probabilities with our household data to generate our person-level measurement.
Q: How can we pinpoint which activity stemmed from which member in a household?
A: This comes back to our person-level data. It allows us to probabilistically identify who is watching the screen. Of course, it can be more than one person and our data will reflect that. iSpot person-level metrics are generated based on representative panels at the household and person-level. We use the household-level to pinpoint the occupants and their traits, which gets coupled with the person-level data to determine which ones were most likely in front of the glass at the time an ad appeared.
As is true for every player in the industry, iSpot relies on panels to drive its analytics. No one panel has the full picture for linear TV, nor tracks viewing for everyone in the US. iSpot leverages the best data in the market to transform household level metrics into person-level insights. Truth be told, if we discover something new, we add it into our methodology and make sure any new learnings are taken into account.
Are you measuring and optimizing TV and streaming ads based on person-level data? If not, get a demo of iSpot Unified TV Ad Measurement today.
About the Author
David Coletti is the Vice President of Sports Research & Insights at iSpot, where he spearheads advancement in sports measurement capabilities. With over two decades of related experience at Disney/ESPN, Mr. Coletti is a distinguished figure in the media research community. As Vice President of Research and Insights at Disney, he established the Media Distribution Insights group to improve negotiation strategies with distributors. At ESPN, he was instrumental in incorporating streaming audiences into viewership metrics and helped establish the ESPN+ consumer research unit. His expertise in storytelling and delivering actionable insights make him a pivotal figure in sports media innovation.