The journey toward third-party cookie deprecation has been long and challenging, punctuated by Google’s recent shift to a consumer-led cookie opt-out model. Still, publishers need to keep moving. Actions by browsers, regulators, and consumers all bring them to the same inevitable outcome: cookies will end.

For publishers, the loss of cookie data is a problem. Cookies remain a cornerstone of audience insight and targeting. In fact, in a survey we ran a few years ago, half of publishers polled expected layoffs due to the revenue loss from cookies going away. We see that playing out now, as digital media is more challenged than ever.

Some argue that publishers are rich in first-party and logged-in data to make up the difference. However, for the vast majority, first-party data is scarce as authenticated traffic across the open web has topped out at around 20%. 

In response, alternative identifiers have emerged, allowing for continued behavioral targeting and addressability. However, we can’t, once again, put all of our eggs in one basket. So, publishers must diversify their adtech toolkits. This involves leaning on behavioral targeting, in whatever form it exists, and integrating other approaches.

Enter contextual technology. The journey toward third-party cookie deprecation has been long and challenging, punctuated by Google’s recent shift to a consumer-led cookie opt-out model. Still, publishers need to keep moving. Actions by browsers, regulators, and consumers all bring them to the same inevitable outcome: cookies will end.

For publishers, the loss of cookie data is a problem. Cookies remain a cornerstone of audience insight and targeting. In fact, in a survey we ran a few years ago, half of publishers polled expected layoffs due to the revenue loss from cookies going away. We see that playing out now, as digital media is more challenged than ever.

Some argue that publishers are rich in first-party and logged-in data to make up the difference. However, for the vast majority, first-party data is scarce as authenticated traffic across the open web has topped out at around 20%. 

In response, alternative identifiers have emerged, allowing for continued behavioral targeting and addressability. However, we can’t, once again, put all of our eggs in one basket. So, publishers must diversify their adtech toolkits. This involves leaning on behavioral targeting, in whatever form it exists, and integrating other approaches.

Enter contextual technology. Recent advances in artificial intelligence and machine learning have dramatically enhanced contextual targeting, making it more capable – and timely. As a result, every publisher needs to take advantage of the technology.

The rise of contextual 2.0

Historically, contextual targeting involved analyzing a publisher’s content and aligning it with a broad set of content categories. This was tied to the technology’s limitations and made early contextual fairly imprecise – like using a meat cleaver instead of a scalpel. This would result in mismatched ads and content. However, as AI has evolved, so has contextual targeting.

Today’s technology can analyze millions of articles spanning years. Natural language processing (NLP) now works across many languages, greatly expanding contextual targeting’s reach. Computer vision can even evaluate images and videos in real-time. The vast amount of content available for AI analysis has fundamentally transformed what contextual can do, allowing the targeting to go well-beyond simple keyword matching or broad categories. It can be based on more nuanced categorization aligned with IAB taxonomies.

Additionally, AI provides deeper insights into content consumption patterns and engagement. It can uncover advertiser interests in content that might not be immediately obvious to humans and predict future content patterns and consumption trends. These capabilities enable publishers to better monetize their inventory and audiences, identify and sell high-performing or in-demand inventory at a higher CPM, and expand interest with non-endemic advertisers.

Pairing contextual with behavioral targeting

With all of that in mind, contextual and behavioral targeting should not be considered rivals, though it’s easy to think that way. Ultimately, they’re complementary tools. When used together, they can address signal loss that threatens publisher addressability and monetization.

Publishers can create more effective audience segments—whether through available identifiers or AI modeling—by combining it with contextual data about their inventory and content. This integration creates a two-way benefit: audience segments are matched with relevant content, while content is tailored to specific audiences through contextual targeting. This approach allows publishers to better align their content with interested audiences, improving targeting efficiency and ad relevance.

For example, a luxury skincare brand promoting a new anti-aging serum might use contextual targeting to place ads on beauty and wellness websites. However, this approach alone could attract too broad an audience, even with more nuanced categorization. Behavioral targeting can refine this focus by looking into the characteristics of those with a history of buying premium skincare products or visiting luxury brand sites. Further, behavioral targeting can reveal new clues about potential customers to inform incremental reach via contextual targeting. This combined approach, blending the two types of targeting, ensures ads reach those most likely to be interested in and able to afford the product, as well as new prospects never before considered. This, of course, benefits both the publisher and the advertiser.

The surge in programmatic curation is also expanding to contextual and behavioral targeting, thanks to new technologies. New AI-driven machine learning models analyze historical online content consumption to identify audience trends. Then, a recommendation engine identifies high-indexing affinity groups for predictive contextual targeting. These affinity groups are then mapped to specific content categories, curated across premium inventory, and packaged into Private Marketplace (PMP) deals. This takes the synergy between contextual and behavioral to a whole new level. Plus, it’s SSP and publisher-led versus DSP and demand-led, a welcome shift for publishers..

AI and a brighter contextual future

To fully leverage these opportunities in contextual and contextual + behavioral targeting, publishers must invest in robust data infrastructure and collaborative capabilities. It’s crucial to identify gaps with partners and enhance existing systems. This proactive approach will help publishers adapt to the ever-evolving digital landscape, maintaining relevance and gaining a competitive edge in an ecosystem that often seems to work against them.As a result, every publisher needs to take advantage of the technology.

The rise of contextual 2.0

Historically, contextual targeting involved analyzing a publisher’s content and aligning it with a broad set of content categories. This was tied to the technology’s limitations and made early contextual fairly imprecise – like using a meat cleaver instead of a scalpel. This would result in mismatched ads and content. However, as AI has evolved, so has contextual targeting.

Today’s technology can analyze millions of articles spanning years. Natural language processing (NLP) now works across many languages, greatly expanding contextual targeting’s reach. Computer vision can even evaluate images and videos in real-time. The vast amount of content available for AI analysis has fundamentally transformed what contextual can do, allowing the targeting to go well-beyond simple keyword matching or broad categories. It can be based on more nuanced categorization aligned with IAB taxonomies.

Additionally, AI provides deeper insights into content consumption patterns and engagement. It can uncover advertiser interests in content that might not be immediately obvious to humans and predict future content patterns and consumption trends. These capabilities enable publishers to better monetize their inventory and audiences, identify and sell high-performing or in-demand inventory at a higher CPM, and expand interest with non-endemic advertisers.

Pairing contextual with behavioral targeting

With all of that in mind, contextual and behavioral targeting should not be considered rivals, though it’s easy to think that way. Ultimately, they’re complementary tools. When used together, they can address signal loss that threatens publisher addressability and monetization.

Publishers can create more effective audience segments—whether through available identifiers or AI modeling—by combining it with contextual data about their inventory and content. This integration creates a two-way benefit: audience segments are matched with relevant content, while content is tailored to specific audiences through contextual targeting. This approach allows publishers to better align their content with interested audiences, improving targeting efficiency and ad relevance.

For example, a luxury skincare brand promoting a new anti-aging serum might use contextual targeting to place ads on beauty and wellness websites. However, this approach alone could attract too broad an audience, even with more nuanced categorization. Behavioral targeting can refine this focus by looking into the characteristics of those with a history of buying premium skincare products or visiting luxury brand sites. Further, behavioral targeting can reveal new clues about potential customers to inform incremental reach via contextual targeting. This combined approach, blending the two types of targeting, ensures ads reach those most likely to be interested in and able to afford the product, as well as new prospects never before considered. This, of course, benefits both the publisher and the advertiser.

The surge in programmatic curation is also expanding to contextual and behavioral targeting, thanks to new technologies. New AI-driven machine learning models analyze historical online content consumption to identify audience trends. Then, a recommendation engine identifies high-indexing affinity groups for predictive contextual targeting. These affinity groups are then mapped to specific content categories, curated across premium inventory, and packaged into Private Marketplace (PMP) deals. This takes the synergy between contextual and behavioral to a whole new level. Plus, it’s SSP and publisher-led versus DSP and demand-led, a welcome shift for publishers..

AI and a brighter contextual future

To fully leverage these opportunities in contextual and contextual + behavioral targeting, publishers must invest in robust data infrastructure and collaborative capabilities. It’s crucial to identify gaps with partners and enhance existing systems. This proactive approach will help publishers adapt to the ever-evolving digital landscape, maintaining relevance and gaining a competitive edge in an ecosystem that often seems to work against them.