Over the last six months, mobile ad tech firm AppLovin climbed to the top of the ad tech pack on the strenth of its AI-driven mobile monetisation platform. Now CTV ad tech business tvScientific is looking “to do what AppLovin has done” but on TV, bringing an outcomes-based model to CTV environments.
The US-based company raised $25.5 million in a Series B funding round earlier this year, which Jason Fairchild, Co-Founder and CEO of tvScientific, says will fuel the development of its outcome optimisation technology for TV. Currently 40 percent of campaigns (by dollar volume) run on the platform are fully automated. “With continued investment, we’ll get to closer to 100 percent by the end of this year,” Fairchild tells VideoWeek.
The model mirrors that of AppLovin in mobile environments, whereby the company is paid on user conversions or monetisable events post-exposure, which enables outcomes-based advertisers to tie advertising activity directly to sales on the platform. But Fairchild says AppLovin operates in a “black box” environment. “We want to do it on TV with total transparency,” he comments. “And that requires a big investment, because it’s a big idea.”
Lessons from Big Tech
That idea is based on the argument that the TV market is undervalued by a factor of ten, at least from where tvScientific sits. eMarketer forecasts the CTV market to be worth $45 billion in 2028, when digital advertising reaches $450 billion. “We’re really not going after the TV market; it’s the digital advertising market that we’re going after,” says Fairchild. “And we speak their language. We speak the outcome language. We talk about ROAS, we talk about cost per action, or cost per app install.”
The company measures incremental outcomes including website visits, website purchases and app installs, or for retailers, metrics such as foot traffic and cash register transactions. This is achieved by building a “technical feedback loop”, whereby tvScientific delivers an ad to a househould and assigns that household an ID, while using device graphing to map devices to that household. “So when we show a Domino’s Pizza ad and someone picks up a phone and orders a pizza, we know that it’s from the same household that saw the pizza ad,” explains Fairchild. The ad tech firm can then use its patented AI technology to optimise towards those outcomes.
In doing so, the company hopes to replicate “what’s worked for Google and Facebook, and take it to TV,” but again with transparency aiming to resolve the controversy that can come from machine-driven optimisation. This is achieved through log-level verification, according to Fairchild, enabling advertisers to “override the algorithms” if they want to remove an ad from a certain channel – even if it means impacting performance.
“If the universe has learned anything from Google and Facebook, in terms of what their advertisers like and don’t like, they don’t like the lack of transparency and the lack of control,” he notes. “So we’re trying to take everything that’s good about Google and Facebook and apply it to TV advertising, and also learn from the things that aren’t well received by Google and Facebook advertisers.”
A deal with the devil
By targeting the top 10-15 percent of Google and Facebook advertisers that can spend at least $100,000 per month on advertising, tvScientific hopes to unlock a “Google-esque” market opportunity. However, weening these advertisers away from the Big Tech platforms requires undoing what Fairchild calls “25 years of cost-per-click indoctrination.” Not only is this outmoded as an attribution methodology, he argues, it enables last-click channels (such as search) to “claim credit for TV’s impact”; a phenomenon he likens to giving FedEx credit for the product you ordered from a retailer.
And while it is hard to measure the longer-term impact of TV ads, tvScientfic measures lower-funnel outcomes for up to 30 days, and sees “massive spikes” in activity even in the first two hours of showing an ad. “Search claims 70 percent of the credit for that spike,” comments Fairchild. “So from a marketer point of view, they look at their Google Dashboard and they just attribute it to Google. And we can educate the market around that, but that is the dynamic that we need to evolve away from.”
But until advertisers end that reliance on Google and Facebook as marketing channels, Fairchild says they’re “making a deal with the devil,” given the black box nature of the decisions being made with their budgets. “You’re building dependence on something that makes you dumber over time,” he argues. “You don’t know anything about what they’re doing, so you don’t build the institutional knowledge around what works and what doesn’t work on your most important growth channels. So I think that’s fundamentally bad for marketers.”
By giving control and transparency to advertisers in TV environments however, tvScientific is aiming to help them “become smarter as a marketer” – and with AI reducing the cost of producing and running campaigns, Fairchild predicts the cost of delivering a high-quality TV ad to come down to $5,000 in the next 18 months. “The full democratisation of TV advertising is upon us,” he says. “It’s just a question of, is it going to take three years or two?”
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