We have entered an era of content-centric data triggered by GDPR, CCPA regulations, and the death of third party cookies that is underway. As marketers, we are being forced to stir away from users personal data. This shift might undermine short-term marketing and advertising efforts, but it’s not all bad news. While behavioral data brought a promise of targeting efficiency, it neglected the importance of accuracy. With behavioral targeting at risk of privacy violation, advertisers turn back to contextual.
The contextual prejudice
At its basic form, contextual targeting was about getting a brand in the relevant context by using simple indicators, targeting newspapers and websites sections. However, when moving to video, context became harder to identify. Even with the 2017 IAB Tech Lab Content Taxonomy Version 2.0 update, still, today one of the significant challenges for video advertisers remains – how to accurately categorize video content.
Contextual was always considered to be too broad and blunt. However, when applied with a predictive knowledge graph that accumulates real bid data, you can decide where and when to serve a video ad that would generate ideal engagement and results. While first-party publisher data is probably not as prominent as Amazon, Google, and Facebook’s first-party data, it is a consensus that a publisher’s contextual data is unique by having access to an environment with quality media as well as a deeper understanding of its inventory context.
From user-centric to content-centric targeting
By applying machine-learning-based technology within this unique environment, you can access successful patterns of past video marketing campaigns for similar brands/ads, that can deliver ads at ideal times and placements based on the advertiser’s KPIs. So that Instead of leaving it to probability and conclusion, based on past and eventually interpreted behavioral data, a publisher can now use a technology that can define contextual relevance and tell in real-time what video content resonates the most. By implementing this type of technology, publishers only now understand they’ve been sitting on a data goldmine.
In response to that growing realization, we have started seeing alliances forming, with mid-sized publishers investing their efforts in extracting as much monetary value as possible from their content and context. Publishers Co-oping their contextual data create a more reliable data set that can better serve their advertisers without risking neither user privacy nor user experience. Backed by direct relationship via private marketplaces, advertisers gain access to direct-to-player views that are more relevant to their needs, enabling them to cut out costly intermediaries peddling questionable data.
Eventually, It all comes down to context.
Brand safety and transparency are justifiable of the main concerns when it comes to measurement reliability. However, they lack the understanding that the environment in which your brand is displayed should be examined not just for its “safe context,” but also for the overall content experience.
When looking at targeting across the world wide web, such insights can be attained only by having a player technology coded on the page. The ability to tap into powerful data and facilitate more relevant advertising while providing higher quality viewing experience and more granular foundation for data analysis adds that level of accuracy without undermining users privacy. Ultimately, taking a step back, looking at the data from content rather than a consumer perspective is the key to both a publisher and advertisers success.