By Dave Hills, CEO at Advanced Contextual
Most advertisers understand the basic premise behind contextual advertising: pair ads with content to which they’re relevant (think: Nike ads in a sports article). Historically, this was how contextual targeting was defined: match an ad to an entire domain (nytimes.com) or channel (sports.nytimes.com). But to take full advantage of today’s programmatic ecosystem, contextual advertising needs to surpass this historically ham-fisted approach.
And as privacy changes from the walled gardens as well as data regulation from states like Washington and Nevada make behavioral targeting more difficult, advertisers also understand why contextual advertising as a whole is becoming more enticing. You don’t need behavioral data to drive performance.
All of this is why contextual advertising is making its comeback. But that doesn’t mean that it should look exactly like it did 20 years ago. With traditional contextual advertising, brands aren’t able to achieve high enough levels of precision at scale. That’s why the industry shifted to behavioral targeting. So, today’s contextual advertising needs to use content as a signal while being precise enough to drive efficient performance.
So, how can brands revamp contextual advertising for a cookie-less future? Two approaches they should consider are screening out broad pages such as home and channel fronts for targeting and leveraging contextual intelligence to reach audiences across platforms, not just the open web.
Focus only on article pages and never home or channel fronts.
For contextual targeting to scale and perform, targeting should focus only on article pages and never on home or channel pages. While both home and channel pages provide high reach, they are unlikely to deliver the precision a brand needs to hit its KPI targets. In other words, just because an entire publication is relevant to the brand’s category doesn’t mean every page is relevant to the brand’s audience.
By screening out overly broad domains like homepages (think: nytimes.com) and channel fronts (like health.nytimes.com), an advertiser immediately removes high-reach but low-relevancy content.
To illustrate, let’s say you’re a pharmaceutical company advertising an asthma medication. The New York Times is a premium publisher, so it makes sense to want to target their pages. And their health channel front, intuitively, would seem to be a good place to advertise the medication specifically. The only problem with targeting high-reach pages like the NYT health channel page is that they are only relevant when they pertain to a specific campaign (in this case, when there is an asthma article on the channel page). When that isn’t the case, targeting the page results in wasted reach.
So, instead of targeting these types of pages, advertisers should only target individual pages of text or video content that are specific to a given campaign. In the case of the pharmaceutical company advertising asthma medication, prime pages for targeting would be news articles about asthma. The brand should also consider targeting specific pages of content their intended customers are also likely to read, whether they be strictly asthma-related or not (just because you’re interested in asthma medication doesn’t mean you spend all your time on the internet reading about asthma).
Advertising becomes more precise with this kind of granular targeting, ensuring that brands reach ideal customers who are likely to more meaningfully engage with specific pages of content than they would with broader domains.
And this is the best way to leverage the programmatic ecosystem and tools. Engines that listen to millions of impressions a second across billions of pages will have the ability to focus a brand’s ads only on those pages which are about their specific product.
Ensure cross-channel reach
For digital advertising, achieving scale depends on being able to reach audiences on as many channels as possible. But a lot of adtech vendors don’t offer cross-channel reach with their contextual advertising solution: the segments they create for open web advertising can’t be brought over to the big social media platforms.
Failing to enable contextual targeting across channels means that advertisers have to rely on the black-box solutions Google and Meta have developed to reach audiences on their properties. This poses both a performance risk and a brand safety risk: advertisers cannot exactly control where they show up.
So, how can advertisers achieve cross-channel reach without having to sacrifice targeting and brand safety controls? One way would be to identify the list of pages — as well as a list of IDs that visited them — for a given segment. Next, the advertiser identifies other pages the initial cohort of IDs visited. Then, the advertiser can model audiences in walled gardens using lookalike models based on the IDs they’ve collected from the open web.
By filtering out pages like homepages and channel fronts and ensuring cross-channel reach with lookalike models, advertisers will reduce potential waste with their campaign spend, ensuring they get as much as they can out of each ad dollar. Not only that: by using these two methods, advertisers will unlock prime opportunities for getting closer to the exact audiences they want to reach — all with performance comparable to cookie-based targeting, but without having to rely on behavioral data or black-box solutions.
Contextual targeting is resurgent. But that doesn’t mean it’s the same contextual advertising that hit the web two decades ago. Today’s contextual targeting is more granular, it works across channels, and it’s helping secure the future of digital advertising.
