Navigating the Cookieless Era Part 3: Rethinking Media Planning with Contextual Targeting and AI

Let’s continue our cookieless journey by understanding why we must rethink media planning. In case you missed them, here are our first two posts in the series: It’s Not About Cookies, It’s About Profiling and Facing the User Identification Problem.

What Drives Media Budgets? Reach and Frequency

Why does user identity matter so much to digital marketers? Reach and frequency.

The vast majority of media budgets, across all channels, are planned around these two key variables:

• Reach: how many unique consumers will be exposed to a campaign
• Frequency: how many times each consumer will see the ad 

Reach and frequency cannot be measured without some form of user identification. In digital advertising, the identity data of online users is specific and accurate, supporting precise solutions for tracking and measuring results.

Therefore, the requirement to stop tracking users reaches right into the heart of the advertising industry. Media budgets can no longer be planned the old way, with reach and frequency in mind. That is why the vision of a cookieless future has rocked the industry, and in many cases, the first instinct has been to find a replacement identifier, however flawed or limited in scale it might initially be.  

Non-Identity-Based Solutions: Contextual Targeting & AI-Driven Advances

Traditional in-person marketplace vendors can infer many things from simply observing their potential target customers. The weather has turned, so the newsstand next to the office building now stocks umbrellas instead of sunscreen.

As large sections of the web lose their ability to consistently track user identity, advertisers are turning to “context” as a solution. With contextual targeting, ads are served based on the content and context of the web page, rather than the attributes of the page users. For example, a blog about “Best Restaurants in NYC in Winter” could feature contextual ads for a popular umbrella brand.

Contextual 2.0: Next Generation Contextual Targeting

At Outbrain, our approach to contextual targeting is based on advanced machine learning and deep contextual signals to achieve a human-level understanding of on-page content to unlock new, non-identity-based targeting solutions. Contextual 2.0 is all about expanding the contextual signals at the heart of Outbrain’s ad-serving technology to continue to drive strong results in a cookieless world. 

Outbrain leverages three key contextual data pillars:

• Deep page analysis
• Location-based inferences
• User intent prediction

Furthermore, advancements in AI, particularly Large Language Models (LLMs), present an unprecedented opportunity to address signal loss and offer new, effective solutions for brands and agencies to connect with their ideal audiences without relying solely on user identification. Major industry players are moving toward “prediction over precision”, which rests on probabilistic data much more than in the past. 

Probabilistic data modeling is an advanced methodology that uses patterns and statistical analysis to make educated predictions about audience characteristics, enabling precise targeting without relying on cookies. With AI-driven prediction, advanced algorithms analyze various signals such as user activity, interests, and contextual data points (all collected in full compliance with privacy standards) to predict audience preferences and behaviors. 

Rethink Media Planning, Starting Now

With Contextual 2.0 and AI, we are shifting the conversation away from user identity and targeting towards contextual targeting based on brand identity and brand alignment. 

In part 4, we deep dive into identity-based solutions, including first-party data, universal identifiers, and data clean rooms. Stay tuned!

FAQs

Explain contextual vs behavioral targeting?

Contextual targeting focuses on matching ads to the content of a webpage, considering its topic and context. Behavioral targeting, however, relies on tracking users’ attributes and past behavior to deliver ads tailored to their interests and preferences. Both strategies aim to increase the relevancy of digital ads to the website user, thereby enhancing ad engagement.

How does contextual targeting work?

Contextual targeting matches ads to the content of a webpage based on keywords, topics, or themes present on that page. Advertisers identify relevant keywords or categories related to their products or services. When a user visits a webpage, the ad platform analyzes the page’s content and serves ads that align with the context. The goal is to attract the attention of visitors on the page who are likely to be interested in the displayed ad and interact with it (ie. click or convert).