What is Contextual Targeting? How has it evolved and what has AI’s impact been?
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In the rapidly evolving landscape of digital advertising, where data privacy concerns and the imminent loss of third-party cookies are reshaping the industry, contextual targeting has re-emerged once again as a formidable strategy.
From its humble beginnings of simply aligning ads with relevant page content, contextual targeting has matured into a sophisticated methodology driven by cutting-edge technologies like artificial intelligence (AI). This new advanced approach to contextual targeting is redefining how advertisers engage consumers with relevant and personalized ad experiences.
To understand the full power of today’s contextual targeting capabilities, let’s go over what contextual targeting is, how it (like everything else today) has been impacted by AI, and how marketers stand to benefit. In this post, we’ll cover:
- What Is Contextual Targeting?
- How Is AI Impacting Contextual Targeting?
- How Has Contextual Targeting Evolved to Rival Behavioral Targeting?
- How Can Marketers Benefit from AI-powered Contextual Targeting?
What is Contextual Targeting?
Contextual targeting has been around since the beginning days of the internet. In its early stage, it revolved around a simple idea: displaying ads next to relevant page content. By aligning advertisements with specific keywords or topics on a given page, advertisers aimed to capture the attention of users who were already engaged with related material.
For instance, when reading a pet training article about training a new puppy, a contextually delivered ad may show ads for dog food or pet toys for sale.
When this targeting method was first introduced it was a revolutionary way to deliver relevant ads to consumers. But as the landscape of digital advertising rapidly evolves, expectations for personalization and ad relevancy continue to reach new heights.
How is AI Impacting Contextual Targeting?
Artificial intelligence has been used to enhance traditional contextual targeting in numerous ways. For example, advertisers can now use keyword recommendation engines to expand their standard keyword lists to automatically broaden their reach into relevant categories. New age contextual targeting can also understand page sentiment and better predict trending topics that might be of interest.
While these enhancements have certainly helped elevate contextual targeting beyond its original state, they haven't quite been able to address the desire to target users based on the characteristics and interests available with behavioral targeting.
How has Contextual Targeting Evolved to Rival Behavioral Targeting?
In the latest evolution of contextual targeting, sometimes called contextual 3.0, the technology harnesses the power of AI and machine learning to create targeting solutions that leverage user behavior as a means to predict content consumptions patterns. This type of advanced contextual targeting is built on massive, first-party datasets that are fed into machine learning models which can observe and predict patterns. In the case of contextual 3.0 the models are observing how online browsing and shopping patterns align to the types of content a user consumes across webpages, videos, streaming and everything in between. Using these patterns, the technology can predict a user’s behavioral tendencies based solely on the content they are actively consuming.
This revolutionary approach to reaching users allows advertisers to maintain effective personalized targeting without relying on user IDs and therefore maximizing their reach over traditional ID-based behavioral targeting, or even standard contextual targeting.
To illustrate this, imagine an advertiser is targeting pet owners using traditional ID-based behavioral targeting. If only 50% of available programmatic inventory has an ID associated with it, the advertiser is potentially missing 50% of the pet owner population.
New AI-driven contextual technologies, like Proximic by Comscore’s Predictive Audiences, can help to reach the remaining 50% by leveraging the combination of audience and contextual targeting technologies to deliver reach. To do so, our machine learning models analyze the behavioral patterns of real pet owners from our extensive first-party panel data to understand their content consumption habits. Using this pattern mapping, our Predictive Audience targeting technology can predict if someone is a pet owner by the content they are actively consuming and show them an ad accordingly.
How Can Marketers Benefit from AI-powered Contextual Targeting?
The top 3 benefits of using advanced contextual targeting, such as Proximic by Comscore’s Predictive Audiences, include:
- Reaching users based on behavioral inputs such as demographic characteristics or shopping without compromising user privacy
- Maximizing your targeting reach with the intelligence to deliver hyper-relevant ads outside of endemic content categories
- Boosting your campaign efficiency with greater cost efficiency and return when buying inventory contextually
Proximic by Comscore remains dedicated to pioneering targeting solutions that empower advertisers to thrive in the new age of digital advertising. Contact us today to learn more about our advanced contextual solutions for Media Buyers and Media Sellers, such as our Predictive Audiences.