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.

Eliminate Waste from Media Buying with Advanced Contextual

Not all media buying amounts to high-quality advertising. Every campaign manager has spent budget without being sure that he’s doing so in the most efficient way possible. This isn’t entirely on the people who orchestrate media buying. It’s a function of the way most advertising tools are set up.

Three common sources of waste in digital advertising are targeting based on homepages and channel pages, keyword ambiguity, and made-for-advertising sites. Let’s break down how each of these factors drives waste — and how you can eliminate that waste and get more bang for your buck with Advanced Contextual’s approach to audience modeling and targeting. 

Poor contextual targeting

Most media buying strategies involve some level of contextual targeting — meaning the advertiser pairs their message with sites that relate to it. To do this, most contextual advertising platforms and media buying tools categorize content based on a site’s homepage or a channel page, as well as article or video level pages. Solely focusing on domains (like nytimes.com) or the channels that most immediately come to mind (like health.nytimes.com) leads to broad, imprecise targeting. On the other hand, focusing on individual health articles and video pages at nytimes.com, for instance, will provide you with a robust, accurate signal.  

For example, let’s say a pharmaceutical company wants to advertise its medication for a rare liver disease. A channel page such as health.nytimes.com could seem like a good place to start. However, the content that appears on a channel page like this is too broad to reach that advertiser’s exact intended audience (unless there happens to be coverage of liver disease on a given day when ad is running). 

Instead, advertisers should be targeting granular, individual pages of content rather than a homepage or channel front. In the case of our hypothetical pharmaceutical company, news articles about rare liver disease — or other specific pages of content the company’s intended audience likely reads — would be prime targets for advertising. 

That’s why Advanced Contextual’s platform ignores homepages and channel fronts. By only targeting specific, individual pages of content for our audience models, our platform enables advertisers to achieve the level of targeting precision necessary for connecting with the most relevant audiences. 

Ambiguous keywords

In addition to relying on homepages and channel pages, most media buying strategies only use keywords. This is intended to help the advertiser find content, and by extension audiences, relevant to their product and message. But keyword targeting goes awry when the keyword is too ambiguous to ensure brand suitability and relevance.

Consider a hospitality brand targeting “travel” pages. While the advertiser might have articles about a weekend in Mexico City in mind, targeting “travel” and related keywords might land their ads next to articles about refugees fleeing war or natural disasters. That’s not the right mindset for a reader to be in when they encounter an ad for a hotel chain.

Advanced Contextual eliminates this ambiguity by checking the surrounding environment of content first so you’re sure the words you’re targeting mean what they should and that they’re in an environment you want your brand to be associated with — not one that will hurt it. We take a topics-first, keywords second approach. Our engine always checks the topics of the content before determining if the words are appropriate to target with ads, performing deeper topical analysis and measuring tone, sentiment, and psychographics to boot. 

This means a hospitality advertiser never ends up next to content about politically motivated travel. Their ads run next to content that is truly relevant to the vacation-minded consumer they want to reach.

MFA sites

Most media buying strategies use tools that direct some spend to made-for-advertising (MFA) sites, or pages that pose as enticing targets for advertising but are really sites with low-quality content, stuffed with ads. In fact, the ANA recently found that MFA sites accounted for 21% of impressions.  

Despite their high reach, MFA sites lack the content quality to generate prime advertising engagement. Another way to look at it: most readers only wind up on MFA sites by accident, often leaving them after (at best) minimal engagement. 

Advanced Contextual’s platform religiously screens out MFA sites. For instance, a page crammed with cheap inventory and a slideshow is usually a dead ringer for an MFA site, and our platform rules it out for targeting. 

By filtering out MFA sites, homepages, and channel fronts and taking a topics-first, keywords-second approach, Advanced Contextual’s custom audience models enable advertisers to target only the pages most suitable for their brands. The custom segments we build don’t just help advertisers cut out waste. They unlock prime opportunities for getting closer to the exact audiences they want to reach.