Have you ever seen an ad that had nothing to do with the page you were viewing or what you were interested in? Almost like it was meant for someone else entirely? 

This mismatch usually traces back to a behind-the-scenes process called “content categorization,” or the system that decides what a web page is about so advertisers can match their ad creative and messaging to the right audiences.

In theory, content categorization should be simple. A page about last night’s game should fall under Sports. A recipe for apple pie? Food and Drink. But in practice, these labels are often assigned automatically, and not always accurately. 

At Cognitiv, we have been working on a smarter, more reliable way to categorize digital content. By using deep learning and large language models (LLMs), we are able to scan entire web pages, not just a page’s URL or a keyword here and there, gathering a true human-level understanding of the page. 

This in-depth understanding of webpage sentiment allows us to assign refined content categories to pages that actually reflect what the page is about, leading to smarter ad targeting for advertisers (and a more resonant experience for the audiences they serve their ads to).

After many interviews, coffee convos, and lunch-table TED talks with some of the brightest minds at Cognitiv, here is what I learned about the content categorization process and how deep learning makes it smarter, more precise, and far closer to how humans actually interpret a page.

Introducing IAB Categories

Most ad targeting online relies on IAB categories, which are the building blocks of a digital labeling system created by the Interactive Advertising Bureau (IAB) to help advertisers decide where their ads should appear. 

Think of these categories like those atop a news site, like Sports, Finance, or Health & Wellness to name a few.

The Problem With How IAB Categories Are Applied Today

Supply Side Platforms (SSPs) often assign IAB categories automatically, while publishers tend to apply them manually. Both approaches rely on surface-level signals like URLs, headlines, and/or keywords to interpret what an entire page is about. However, those signals alone cannot determine sentiment or intent. 

Why Keyword Tagging Falls Short

A keyword, for example, can appear in many different contexts, and without reading the full page, the system cannot distinguish whether the content is positive, negative, neutral, or even relevant to the topic at all.

For example, if the word “Apple” appears on a web page, how could a keyword-based system know if it is about new iPhone features or a pie recipe? If a page mentions “Genesis,” is it discussing a luxury car or a section of religious text? 

Without context, systems guess; and this guesswork leads to inaccurate content categorization.

Read More: The Big Difference Between ContextGPT and Keyword Targeting 

More Categories ≠ More Accuracy

Sometimes, in an effort to make their inventory more appealing, publishers and SSPs pass category labels that are a bit too general, or even too generous, resulting in pages being tagged with multiple categories that do not quite fit. 

This “category stuffing” makes it seem like a single page belongs to more content types than it actually does.

A blog post about “meal prep ideas for accountants during tax season” might get labeled as Food & Drink, Personal Finance, Business, and Lifestyle… even though the content is really just a collection of recipes.

Ultimately, this blog looks versatile to advertisers, but the various labels dilute what the content is actually about.

The Impact of Miscategorization

It is not that IAB categories themselves are the problem, it is the categorization process. These inaccuracies happen for a variety of reasons, from technical limitations and outdated classifiers to the sheer scale of the open web. 

Small miscategorizations snowball. Advertisers waste spend, publishers miss out on revenue, and readers gloss over ads that feel out of place… because they are.

Cognitiv Offers Smarter Inputs for Partners

Cognitiv’s goal is not to replace IAB categories, but to improve how they are applied, bringing more accuracy, nuance, and transparency to the process so every impression counts.

The way we do this is by having our deep learning models scrape and score web pages based on category likelihood, giving advertisers and Demand-Side Platforms (DSPs) a clearer view of where their ads appear. 

These refined categorizations are generated using the same deep learning embeddings that power ContextGPT™, our contextual intelligence engine, making them more reliable.

For example, if a page contains a recipe that happens to mention “interest rates” in a blog post about first-time home buying, traditional systems might label it broadly as Real Estate. Whereas our deep learning models understand the page is actually about Personal Finance, a nuance that matters when deciding which brands should appear there.

These improved signals help reduce waste, cut down on fraud, and make targeting more relevant. 

The Future of Content Categorization

Content categorization was never supposed to be guesswork. It was supposed to be a bridge between what a page is actually about and the advertisers who want to show up there. But for years, categorization has relied on shortcuts: keywords, URLs, and assumptions. Those shortcuts created mismatches, wasted spend, and irrelevant ad experiences.

Deep learning changes that.

Instead of treating pages like buckets of keywords, it reads them the way people do: with an understanding of sentiment, nuance, and intent. 

Refined IAB categorization is not a new system; it is merely a more accurate application of the existing categorization processes. When the label finally matches the content, everything down the chain improves from targeting, insights, and performance, to the experience of the person on the page.

The future of content categorization is not about creating new taxonomies. It is about applying the existing ones with more accuracy and more intelligence.