Brand advertising has been commercial TV’s bread and butter for the last 70 years, but the long-term benefits of TV advertising are notoriously hard to measure. While brand lift studies provide insight into consumer perceptions, these effects can be difficult to tie back to TV ads, as survey responses inevitably come loaded with respondents’ preconceived notions about a brand. Even asking if they have seen an ad can draw unreliable responses, since consumers are exposed to hundreds of ads every day.
To address this flawed methodology, measurement firm On Device has developed passive technology that understands consumers’ exposure to the advertising the company measures, rather than relying on the memory of individuals.
“The idea of asking people, ‘Have you seen this ad?’ is actually a ridiculous way to try and work out if somebody’s been exposed to that ad,” Alistair Hill, Co-Founder at On Device, tells VideoWeek. “The people who say ‘yes’ are very much biased towards existing customers. If you saw a British Gas bill on your kitchen table in the morning, and you’re asked if you’ve seen this ad for British Gas in the afternoon, you’re highly likely to say yes, just because it’s in your head.”
Get real
Instead of asking people if they have seen an ad, On Device’s consumer-facing app passively tracks when individuals have been exposed to an ad, alongside those that have not. By surveying these control and exposed groups, the business can then start to understand the effects of an ad campaign.
A critical part of this methodology is making sure the consumers are real people and their responses are genuine. For example, On Device can ensure the respondent is who they say they are, by having them hold up their driver’s licence, and using facial recognition technology to confirm they match their photo ID.
“The survey world is rife with fraud and bots and all of that stuff, and people using ChatGPT to basically answer surveys and so on,” explains Hill. “So you need a significant amount of defensive technology to understand that that person is a real person.”
The other key component is balancing the control and exposed groups so the two look the same, in order to remove bias from the sample. And by understanding the consumers’ purchase history, On Device can make sure the two groups have the same prior relationship with the brand in question. That could mean ensuring no one in the sample has owned that brand before, to guarantee that the only difference between control and exposed is their exposure to the advertising, rather than any other bias that could occur in the sample.
In it for the long-haul
By surveying these groups, the company can measure brand awareness, consideration and purchase intent, metrics that Hill calls the “leading indicators” of future performance. “There’s a correlation between, for example, brand consideration and sales six months down the line,” he says.
Moving these metrics can therefore help build the long-term growth of a brand by priming customers for future sales, according to Hill, instead of relying on short-term sales from existing customers. He notes that if five percent of consumers are in a buying mindset, incremental sales will come from focusing on the other 95 percent, by making sure the brand is prominent in the customer’s mind when they get into that buying cycle.
“You’re going to buy a car perhaps every five years, probably every 10 years,” comments Hill. “And so when it comes to that moment, and you happen to have a kid in that five-year period, you want a safe car. What’s the car that has consistently told you that it’s going to be safe? Then when you’re in that mode, you’re going to go to the Volvo garage, for example. But that’s because of five years of advertising that has put you in that frame of mind.”
A happy byproduct
While most of the industry acknowledges the need to invest in brand-building, Hill argues that the recent emphasis on outcomes measurement and attribution models has “put people into a short-termist mindset”, meaning they sell less down the line. “They might sell more in that quarter, but over time, the power of their brands has deteriorated,” says Hill.
This does however depend on the brand; the largest advertisers (the category On Device tends to work with) are heavily invested in brand, whereas smaller advertisers are less likely to have the budget for brand advertising, nor necessarily the need for a brand – until they reach the next level of growth. And as CTV publishers and sales houses pitch themselves as performance channels in order to bring in those smaller advertisers, Hill notes that those advertisers will also be able to move their brand metrics as a byproduct.
“What video ads, and particularly TV ads, are incredibly good at, is moving those really hard metrics,” observes Hill. “They can tell a story, they can take you on a journey. And all of our data shows that CTV is great at moving consideration, which is actually the biggest driver of a brand’s future growth. That’s going to be more accessible to more advertisers, which is a great thing.”
By working out which ads were more or less effective at moving those metrics, advertisers can use that data to determine the type of creatives, channels or media partners to dial up or down, in order to increase the effectiveness of future campaigns. On Device has also launched APIs that enable ad tech systems to link survey responses to ad impressions, enabling granular insights into the effectiveness of impressions based on their placement on a page, the TV show they appeared in, and time of day.
Competing with walled gardens
Hill says that this level of data and granularity is necessary to enable CTV to compete with walled gardens, which have grown highly effective at targeting campaigns, by feeding campaign data back into their systems, and using that data to target ads in the future. “TV companies absolutely need to do that,” Hill remarks.
But that data asset can only be built on legitimate, transparent data, argues Hill, which is why the project has to start with robust measurement.
“There is a perception of measurement as a box-ticking exercise,” he comments. “And you can get away with the box-ticking exercise by not being transparent, but you can’t then use that for building out the data asset and optimising based on that, because it doesn’t make sense. What we are really trying to do is to take measurement away from being a box-ticking exercise, and into being useful for improving future performance, to make sure that our clients can actually be really competitive against walled gardens.”
Follow VideoWeek on LinkedIn.
