What’s Next for Contextual Targeting? Matching Ads With Sentiments and Emotions
Google recently announced a delay in the deprecation of cookies, but marketers are well aware deprication has already begun. Apple’s Safari and Mozilla’s Firefox no longer support third-party cookies, and Chrome’s impending initiative should further motivate advertisers to develop and implement strategies to succeed in a cookieless future. The entire industry shouldn’t stall to invest in machine learning, contextual targeting solutions, and other tools that will assist in making ads more relevant to the page environment, while respecting privacy on a global scale.
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Advancements in advertisement technology (AdTech) for contextual targeting have greatly increased the ability to target relevant ad placements by matching campaigns with the emotion and sentiment of a page in a scalable manner.
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So why do sentiments and emotions matter when it comes to context?
It matters because brands sell to people who make purchase decisions based on how they feel about or perceive the brand or the product.
Marketers use emotional branding to engage with consumers and to achieve this they create content that appeals to the consumer’s emotional state, ego, needs, and aspirations. Therefore, it’s critical that the technology that’s responsible for discerning content and context is able to effectively discern emotions, sentiments, and cultural nuances to capture the consumer’s frame of mind.
Research proves customers care about sentiment and emotions. In our recent survey, 72% of consumers agreed that the sentiment of an article impacts their opinion of the brand advertising alongside it. The answer is clear: as consumer data privacy is prioritized, and brands’ reputations are on the line, contextual targeting will become increasingly crucial for future campaigns.
What if the contextual targeting technology was able to read the page as a human would?
Well, then it would understand the mood and feelings contained in a piece of content and help marketers keep emotions at the heart of their campaigns. In turn, this ensures that ads are well placed in the “right emotional context.” This manner of semantic targeting lets brand values resonate in the most appropriate emotional context.
True sentiment and emotion can only be detected with natural language processing that can read the page the way the author intended it. For example, an advertisement for engagement rings may not work best beside an article about struggling to find love in the modern world. Keywords may pick up “marriage” and “engagement” when the author talks about what they are looking for, or the stage of life their friends are in, but it is unable to pick up the true sentiment of the page that is negative. This information has proven to be invaluable for marketers, and is the best pivot away from cookies.
The combination of machine learning and cutting-edge technology couples natural language understanding with semantic analysis which allows for a human-like comprehension of the content.
Leveraging sentiment and emotion allows marketers to control the context with a level of granularity that can rival the use of cookies, providing immense scale and preventing over-blocking from occurring. It’s important to understand how this analysis works so that marketers can take full advantage of the technology in their advertising strategies.
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