Browsers are blocking third-party cookies in the name of privacy, but this alone won’t protect personal data within the digital advertising ecosystem. There is a far better and simpler solution for achieving this goal, in the form of contextual targeting.
Until now programmatic has relied heavily on user-based matching to build strong conversion funnels, using third-party cookies to target audiences based on online behavior and socio-demographic data. But with ongoing cookie deprecation expected to culminate with Chrome’s withdrawal of support in 2022, the industry has just a year to come up with an alternative targeting solution. A lack of data from cookies, combined with users choosing to opt-out of data collection is already significantly impacting programmatic’s user matching abilities.
The industry doesn’t currently have an actionable universal identity solution and possibilities in the pipeline don’t necessarily look promising just yet. Any solution that relies on personal data and user-based matching is going to run up against privacy concerns and compliance issues in an evolving regulatory landscape that already includes the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Marketers will need to learn how to effectively operate in a world of unidentified users.
The solution to delivering relevant, timely advertising without risking privacy lies in contextual targeting, achieved through non-user segment matching. Contextual targeting, which leverages publishers’ first-party data to match advertising with the content on the page, is not a new concept. But improvements in contextualized machine intelligence mean it is taking huge leaps forward in precision and granularity. Rather than relying on generic classification at the vertical level, advertisers can now match their target audience against hundreds of content categories or segments. There are multiple benefits to contextual targeting through non-user segment matching:
Natural privacy protection
Because non-user segment matching relies on publisher first-party contextual data, rather than personal data, it is a privacy-first solution and complies with current regulations without the need for user consent. This form of targeting relies on categorization and understanding of web content to gauge the immediate interests and intent of the user. It combines content types, topics, and entities such as people and products, with sentiment analysis of the information on the page, and subsequently sorts audiences into segments based on interests, not personal information. It enables advertisers to deliver experiences that match user interests without breaching privacy.
Relevance based on intent
The goal of digital advertising is to deliver relevant, timely and engaging messaging that resonates with user intent – and I firmly believe contextual targeting can achieve that aim more effectively than behavioral targeting. Intent can be captured while users are actually consuming the content on which the advertising is served, meaning messaging is more likely to match with current intents than ads based on past online behavior. Put simply, a web visitor is more likely to make a purchase based on what they are viewing right now than something they looked at six months ago.
What’s more, contextual advertising is highly engaging because the creative works with the surrounding content. Almost 70% of consumers say they are more likely to look at ads that are relevant to the content they are viewing, while 44% have already tried a new brand after seeing a relevantly placed ad. Advances in contextual targeting technology, including deep text classification and machine learning, also enable campaign optimization by identifying patterns in past data, evaluating alternatives and making targeted recommendations.
The benefits to advertisers are clear, with ads served in a context that is relevant and engaging for the brand-boosting ROI. But there are also benefits for publishers, with those switching to contextual advertising often seeing strong revenue growth. Publishers can also understand how specific elements of content, including page sentiment, visual components and language nuances, impact the probability of users interacting with similar content.
Guaranteed brand safety
When blunt tools such as keyword targeting are used for contextual advertising, there may be concerns around brand safety. An ad may inadvertently appear alongside a negative news story about the brand’s sector, for example. But with more sophisticated techniques for contextualizing and classifying content, these brand safety breaches are very quickly becoming a thing of the past.
Non-user segment matching uses techniques such as semantic analysis and natural language processing to understand the true meaning and sentiment of the words on the page to ensure content is suitable for brand messaging. In addition, advertisers can choose to blacklist certain topics from their campaigns, or only target pages with the most favorable sentiment.
Cookie restrictions are already in motion and will be complete by next year, but they won’t solve privacy issues in the programmatic ecosystem. Contextual targeting solutions provide a simple answer and, with the sophisticated technologies available today, these can significantly improve the accuracy, relevancy and usability of targeting models while driving higher levels of engagement, ensuring brand safety and safeguarding user privacy. With no universal identity solution on the horizon, contextual intelligence will be the most effective targeting strategy for the post-cookie reality.
By Michael Myslinski, Head of Publisher Development MGID NA