Realizing Data’s Potential with Contextual Targeting

In previous posts, [Realizing Data’s Potential: A Guide for Data Owners, Realizing Data’s Potential With Lookalike Models] we have discussed the impacts and disruption from a cookie-free world, with no one more affected than the data owners who help brands and agencies reach target consumers through curated audiences. As these data owners come to terms with the fact that cookies will, eventually, go away, many alternatives have come to light. The one that stands out as the most privacy friendly is contextual targeting.  

Contextual targeting offers a way to place ads based on the content of web pages rather than the personal profiles or data of users, thus diminishing the risk of breaching data privacy laws. By leveraging advanced semantic analysis and natural language processing, marketers can identify the exact context in which to position their financial products or services and - most importantly - reach consumers engaged with content that is contextually relevant. Instead of labeling a user as “high-income” or “actively investing”, contextual algorithms can determine if the user is reading about “dividend income and retirement planning”, “stock market trends”, or “saving for college” and target ads accordingly.

Imagine a world where ads blend perfectly with the content they're on. That’s contextual targeting: making sure ads appear in the right place so they are more appealing to the reader. And, it’s not new, contextual targeting is a tried and tested means of reaching consumers. 

What many marketers might not know is that contextual targeting technology has quietly improved. Semantic technologies like natural language processing (NLP) analyze the content of a page to determine the most relevant words and phrases for a given audience. It’s even possible to transform first-party data or audiences into scalable contextual campaigns!

The combined power of contextual targeting with data or existing audiences holds the key to supercharged targeting. For example: Visualize an extensive heatmap of the internet where each zone corresponds to the concentration of your audience. The 'hotter' a zone, the more likely your audience is to be found there. As a marketer, you're able to determine the 'heat' threshold a page must meet to become a viable target for your campaign.

Advanced predictive modeling  provides a powerful way to make the most of zero- and first-party data, transforming it into untapped consumer segments that improve digital campaign performance. By leveraging this kind of solution, you can expand your target audience, identify high-value content, and stay ahead of the competition. But, it’s important to ensure that any contextual provider selected provides the most up-to-date content indexing possible to reach consumers interacting with relevant content as soon as a page updates. Ideally, key webpages are updated in real-time or near-real-time to capture those “first viewers” and consumers who are highly-engaged with a topic. 

This transformative strategy eliminates the traditional either-or choice between audience and contextual targeting. Instead, you can combine audience precision with the reach and relevance of contextual targeting. By making the most of first-party data and semantic user and page profiles, a universal semantic definition for the target audience becomes attainable. The benefits of extending a known audience contextually include:

• Boosting reach by using a small data set–or seed data–as a proxy for the characteristics of the people you want to reach and where those people are likely to be online. This isn’t just finding look-alike people, it’s determining where those people are likely to be!
• Understanding consumers across their interests, in other words, identifying all of the things the seed audience has in common. By better understanding the target audience, it’s easier to ensure message alignment, create micro-targets for highly personalized offers and inform future product development.
• Improving scale by targeting pages (not people) for highly focused, interest-based advertising that is both cost effective and privacy friendly. By learning from real users, it’s possible to determine where a target audience is likely to be consuming content, making it possible to reach the consumers you know about, as well as the ones you don’t.
• Gaining the maximum value from your data investments by influencing relevant audiences in the moments that matter most and engaging your ideal audience in places your competition hasn’t yet discovered,

As the industry faces times of disruption, reaching consumers contextually, but accurately and efficiently, will become paramount to success. There are very few contextual providers who can seamlessly execute audience to context solutions that predict where consumers are likely to engage online with accuracy, transparency and control.

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For more information read other entries in the Realizing Data’s Potential series.

Realizing Data’s Potential: A Guide for Data Owners.

Realizing Data’s Potential With Lookalike Models.