“Contextual targeting goes back to the early days of advertising online — the early 2000s — where it evolved into a form of targeting that used mostly keywords and content on the page without any sort of sophisticated AI or machine learning,” said Lior Charka, vice president, product at Outbrain. “It was a very basic way of understanding what was on the page and then using keywords to target.”

However, as ad tech grew more sophisticated — and complex — and the industry shifted toward behavioral targeting, for many advertisers, contextual fell by the wayside. And with the industry likely to become even more regulated in the near term — contextual targeting is appearing on advertiser’s strategy roadmaps as a future-proof tactic in the face of increased restrictions.

The new era of contextual targeting focuses on sentiment. Marketing teams identify moments throughout the day that tell them about users’ context, such as what devices they are using, whether they are commuting and other elements of their online behavior. To best take advantage of this approach, marketers are building out their first-party data strategies to gather the information they need to inform sentiment analysis and plug into predictive modeling. As they’re gaining traction, marketing teams are unlocking pathways to specific product and activity recommendations, such as the next article to read or area of a site to visit.

Semantic and sentiment analysis are helping advertisers fine-tune placements

Typically, when people refer to contextual targeting, they speak about deep page analysis — what the page or ad is generally about based on understanding the language through categorization. However, in 2023, using semantic analysis, it’s possible to dive even deeper by interpreting the text and deciphering the meaning behind it.

With semantic analysis, teams can identify when and where brands, organizations and people are mentioned online and use natural language processing to recognize how meaning changes when certain words or phrases are present within those mentions. Then, AI processing can recognize the sentiment tied to these signals.

From there, sentiment analysis helps uncover the tone and emotional value of the content. Insights like these can help advertisers who might assume audience cohorts don’t want to appear next to political content identify political articles with a positive tone alongside which they would be comfortable placing ads.

Location and weather data increase relevancy and provide insight into sentiment

When anonymized signals provide the time and location of a page view — and so when and where a user sees an ad — they help advertisers better understand their audience in a privacy-safe manner. With these signals and data, marketers can then infer things about the user, such as what the weather is like, where they are or what relevant e-commerce feeds might be tied to …read more

Source:: Digiday

Author: Aaron