5 Ways Agencies Can Benefit from Contextual Advertising in 2024

2024 presents unprecedented challenges for advertisers in the form of third-party cookie deprecation. The way agencies have helped brands optimize and measure advertising for years is finally disappearing. So, agencies need new methodologies to deliver results for clients — whose expectations won’t dip as a result of technological change.

Enter contextual advertising. By leveraging contextual signals — what people read and watch, not who they are — brands and agencies can achieve efficient marketing performance without third-party IDs.

But this isn’t your father’s contextual advertising. It’s not just putting Nike ads on articles about running. Here are five capabilities you should look for from contextual advertising and intelligence in 2024.

1. Increased signal across verticals

Contextual intelligence should help brands and agencies build audience and inventory models in place of the audience targeting and measurement the third-party cookie facilitated. To ensure the strongest contextual signal possible for those models, we’re rapidly increasing the list of sites our platform analyzes so that agencies can tap into as many verticals and audiences as possible. 

2. Brand suitability

While they may facilitate cheap reach, made-for-advertising (MFA) sites — which are stuffed with ads and low-quality content — fail to generate meaningful engagement and risk harming your brand by association. That’s why our contextual platform leverages a data-driven formula to rule out MFA sites so brands’ ad dollars only go toward premium, brand-suitable advertising opportunities. 

3. Multi-layered targeting

With contextual advertising, agencies can gain deep understandings of their audiences. In addition to identifying topics a brand’s target customers are interested in, Advanced Contextual’s platform analyzes the tone, mindsets, and sentiment (among other psychographics) of the text and video audiences are consuming. So, you target audiences who won’t just be interested in your message but receptive to it.

4. Look-alike audiences 

With the third-party cookie’s phase-out and resulting ID deprecation, how can agencies leverage look-alike audiences to reach a broad array of prospects beyond the small set of customers they’ve already identified? By using our contextual engine, agencies can understand which pages and topics resonate with their ideal customers and precisely target an accurate cohort of IDs. Advanced Contextual takes the segment data agencies already have and extrapolates from it to generate lists of high-value targets and pages. Agencies can take these lists to walled garden look-alike models, reaching their audience on both social and the open web.

5. Page-level targeting 

The basic form of context works like this: enter a bunch of keywords into a walled garden or DSP interface, and they’ll target those words across their inventory. But this approach doesn’t consider the various connotations those words may have, and can lead an airline’s vacation ads to show up on news coverage about refugees escaping war or natural disasters. 

Enter programmatic contextual. Advanced Contextual’s platform avoids keyword ambiguity by checking the surrounding environment of content to ensure the content is truly relevant to the brand’s message. That way, agencies only bid programmatically on desirable inventory. Your travel ads don’t end up next to upsetting news stories; they target the hospitality content where you’re most likely to convert prospects. 

Scale marketing performance in the new cookieless ecosystem

With third-party cookies finally going away, agencies need new privacy-safe ways to help brands reach their audiences cost-effectively at scale. Advanced Contextual exists to solve that problem. And we’re already solving it for dozens of agencies, which are using AC to find scalable and performant audiences on both social platforms and the open web.

By precisely targeting your highest-intent customers on the open web and social in a way that protects consumer privacy, agencies will be able to deliver results for their clients using contextual advertising — no matter the latest change from Google or Apple. 

Why Advertisers Need Custom Audiences Built Individually for Them

Every marketing data provider creates audience segments for advertisers. Providers often brand their audiences as custom, suggesting that the audiences are tailored to an individual advertiser.

This is usually misleading. In practice, custom segments are often repurposed collections of IDs based on prebaked taxonomies that providers then apply across client media buys. So, the segments fail to account for the exact ideal customer profiles of their clients as well as the nuances of their business.  

For example, let’s say a pharmaceutical company advertising a treatment for a rare form of diabetes approaches a data provider for a custom segment. Given how uncommon that form of diabetes is, the pharmaceutical company expects a small number of potential customers. The data provider might pull a collection of hundreds of thousands of IDs associated with diabetes off the shelf, try to hem it in with a couple keywords, and hand it off to their client as “custom.” But, because the starting collection of IDs is from a cookie-cutter taxonomy, the so-called custom segment is far too broad and imprecise, resulting in wasted reach. 

You might better call this typical approach customized, not custom, audiences. At Advanced Contextual, we provide fully custom audiences — built specifically for you — and can add them to your DSP or social network ad account within one day.

The custom audience problem boils down to audience modeling and targeting. How do you reach the exact customers who will be most profitable for your brand? And how do you accomplish that as efficiently as possible?

Advanced Contextual solves precisely that problem through an approach that we call programmatic contextual. Here’s how it works — and how it can help you reach your audience as efficiently and scalably as possible.

Create your custom audience model from scratch

Rather than pulling an audience segment from a broad taxonomy and narrowing it down from there, Advanced Contextual compiles each of its audience segments from scratch. 

For each campaign, we analyze billions of individual pages of content and categorize them by topic. We screen for topics before using your keywords to ensure that the content is actually consistent with your product. Then, we use the high-performing keywords you’ve leveraged on other channels, such as search, to find pages that prospects likely to be interested in your product are visiting. 

Finally, we feed these example pages into our platform to find all the content that’s relevant to your prospects and suitable for your brand. In addition, we pull the IDs that are linked to those pages. Altogether, this results in a custom segment: content and IDs tailored to the specific topic on which you want to advertise.

Forget broad taxonomies — target with precision and scale

The tailored approach we take helps you eliminate waste from media buying. So, if you’re that rare diabetes drug manufacturer, you won’t waste your ad spend targeting a million IDs when your product only has 15,000 potential customers. 

By building audience models from scratch, Advanced Contextual enables advertisers to precisely target only pages proven to resonate with customers. Because whittling down cookie-cutter taxonomies can only get advertisers so far. 

Audience Modeling in the Aftermath of the Third-Party Cookie

The third-party cookie gave us predetermined audience models most of the time. Brands and agencies trusted that a segment was about what it was named and that they could easily monitor their campaign’s performance and scale goals using those segments.  

This all goes away in 2024 when Google retires the third-party cookie. What replaces it is modeling from a smaller number of IDs for walled gardens and inventory modeling in digital. 

Audience modeling of converters is fairly straightforward. A brand has a customer segment that has achieved a certain KPI, and they want to expand their reach. That cohort can be applied to native lookalike models that exist today in the walled gardens. They’re good models and are built for the environment they exist in, and they help with very low-funnel KPIs. Think of it as the new retargeting.

But you also need to build prospecting models. That isn’t as easy as the lower-funnel use of converted IDs. Enter programmatic contextual. 

Advanced Contextual’s solution powers both audience and inventory models. It all starts with using topics to identify example pages that a prospect is likely reading or watching. Instead of domains or channel fronts, we prioritize article and video pages that represent topics.

We understand the topic of the content, not just the keywords . Because without precise content classification, you risk appearing on pages that are unsuitable for advertising. For instance, imprecise categorization could lead to your travel ads winding up on a news article about people fleeing a natural or manmade disaster. Our proprietary page reader carefully classifies content to prevent these mishaps.

After we put seed articles, or articles verified to genuinely represent given topics, into our platform and hit go, we ask our platform to find all the content that matches the seed topics from the 3.5 billion pages in our contextual index. And since we’re built in front of auctions, we also pull back any IDs attached to the content that has the right topic.

Our segments contain a list of pages as well as a list of IDs that visited that content. The IDs can be exported to walled garden models in a privacy-safe fashion so you can put prospects in the top of the funnel. As for the open web, because you know the exact pages you want to target, you can push those to your DSP and use our model to bid on those pages.

We can take this same data and use it to build audience extension models. By understanding the topic that led us to target a cohort of IDs for modeling and seeing what other topics those IDs visited, we can create a segment-level consumption graph. This is a simple query into our search engine with 3.5 billion pages: we ask the engine what other topics (and specific pages) a cohort of IDs visited in the last 30 days. We then receive a list of pages and segments ranked by comp index so you can see consumption concentration. This list can be ported over to DSPs for an audience extension buy. You can also take those same high comp index targets and model audiences using walled garden lookalike models.

In short, in 2024, the mechanics of programmatic will shift from largely deterministic targeting to probabilistic models. We can use context to build those models, both for the open web and the walled gardens. So, audience data may be going away, but scalable advertising performance doesn’t have to follow suit.