Why will AI shape the contextual advertising of tomorrow?
Artificial intelligence to remove the end of third-party cookies
Deleting third-party cookies is prompting advertisers and publishers to end a system that has been declared dead for several years. Cookie-enabled demographic targeting in particular is – if not always – problematic and outdated. Why? Predetermining the target audience leads to stereotypes and prejudices. It’s a form of targeting that automatically blocks a subset of relevant internet users, ignoring users who don’t match a predefined profile.
More than a simple matter of changing legislation, the disappearance of cookies appears to be a real opportunity for brands to change their approach to their target audience. Instead of reaching people, they now have to try to identify times when an individual indicates they might be interested in their offering, based on what they’re reading, watching, or doing at that particular time. So what better target for a car brand than an unknown Internet user reading a comparison of the best electric cars?
But this concept of contextual advertising is possible only when three pillars are combined: quality content, personalized creativity and proprietary technology. Therefore, brands should look for end-to-end solutions that deliver the most relevant inventory, deliver consumer-centric creative, and have the most advanced contextual targeting capabilities that can build brand-specific machine learning models.
Towards unified understanding and increased customization
Because a contextual approach to advertising requires an extremely detailed ability to understand the content distributed on the Internet. Over the past decade, companies in the sector have introduced technological innovations that have significantly improved the analysis of digital content. The evolution, which has accelerated dramatically with the announcement of the progressive end of third-party cookies by major browsers and legislators, is forcing advertisers to give up, at least in large part, personal data in order to target their campaigns.
Until around 2010, contextual targeting was in its infancy and was still limited to selecting certain keywords and excluding others in order to target advertising. Today, Natural Language Processing (NLP) models can provide accurate sentiment analysis of content that goes beyond keywords to understand the true meaning of text. NLP tools can, for example, identify different uses of the same word “conservatives,” whether it’s used in an article about politics or consumerism.
Beyond the contextual, new paradigms of digital advertising
These tools have dramatically increased the effectiveness of contextual targeting by helping ads not appear next to inappropriate content, as well as aligning creative with the tone and mood of the article. The only developments that allow us to reach levels of brand security that no longer have anything to do with old technologies and make it the most suitable alternative to contextual cookies and, unlike cohort systems, do not use any personal data. links or use of first-party data.
Rather than simply analyzing specific URLs and storing them in predefined categories, the most advanced contextual technologies are able to actually examine all URLs of published articles and develop a rich understanding of the network. This allows them to identify key content trends and topics that audiences are engaging with at any given time.
How does this work? Starting from the customer’s request, AI based on advanced machine learning models scans the entire URL network to find articles that are semantically closest to the short text. This allows us to offer personalized segmentation of each campaign to reach the true core of the relevant target.
This new contextual technology takes the concept of campaign affinity to another dimension. This ensures that ads always appear alongside the most relevant brand content. A necessary change to be able to reach consumers who are truly interested in the topic and when they are interested.
Today, it is possible to match the message and creativity with the tone and feel of each article’s content. In this way, brands can finally personalize their campaigns without relying on stereotypes and respecting user privacy.
