“On TV & Video” is a column exploring opportunities and challenges in advanced TV and video.
Today’s column is by Christian Dankl, co-founder and chairman of Precise TV.
In the digital age, we’re often reminded about how far we’ve come from our prehistoric internet past. We got a nice reminder in June 2022 when Internet Explorer officially retired as a web browser.
A similar shift is happening in traditional advertising and the way consumers build relationships with brands.
Back in the day, when most video ads ran on television, advertisers did not have a lot of data on who watched their ads and how they responded to them. This limited the ability to measure effectiveness. Compared to the specifics we can gather now, the prehistoric methods of advertising were a shot in the dark.
Today, advertisers have much more insight. But even modern-day advertisements are only effective when brands understand the specific, data-driven relationship between a brand and the consumer.
Here’s what that looks like in action.
The impact of brand lift
Defining a person’s relationship with an advertisement isn’t that complex. More often than not, your favorite video ad may have used one of your favorite songs or tugged at a childhood memory. That nuanced, specific relationship we all have with our favorite ads is an aspect of brand lift.
Brand lift analysis monitors key performance indicators (KPIs). These are usually measured and provided by third-party research organizations on behalf of social platforms like YouTube or TikTok.
With the KPIs, brands can monitor how well they are driving awareness, consideration and purchase intent with target consumers.
Digging deeper with brand lift
Data science can help scale brand lift. Through machine learning designed for full-funnel marketing, brand marketers and media buyers can become performance marketers, identifying and learning from brand intent signals at every stage of the shopper life cycle.
Brands that receive shopping behavior data from retailers – or, better yet, that same data from their own websites and ecommerce platforms – can input buyer intent signals into a machine learning model to predict a brand or product relevancy score for a certain audience. This score helps evaluate audiences to hypothesize the likelihood of specific video-level ad conversions, eliminating media wastage along the way.
If a ski equipment brand is trying to reach active skiers that purchase equipment each year, the brand’s approach to placing an ad will be completely different than if the brand was trying to reach a first-time skier.
While intent signals from traditional behavioral targeting are focused on the past with a lookback window of several weeks, contextual intent signals are in real time. They ensure brands deliver the right ad on the right video at the right time to the right audience.
No brand is the same and no persona/demographic type is the same. But you can bank on the importance of applying these sophisticated methods to your media buying strategies to improve video ad performance. Hypothesizing the likelihood of a sale-securing ad or the amount of time between an ad and a conversion – that’s a winning strategy.
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