The ongoing deprecation of third-party cookies, and, in particular, Google’s decision to phase out third-party cookies, will revolutionize the $330 billion digital advertising industry, Bloomberg Businessweek recently declared.
Evidence of this revolution is mounting as companies announce sweeping changes to their policies and platforms. By September 15th, for example, Oracle will no longer offer third-party targeting services across Europe. And Facebook has said Apple’s new IDFA restrictions could reduce Facebook Audience Network’s ad revenue by 50%.
Yet new research suggests audience targeting may not be as integral to powering a free, ad-supported Internet as was previously assumed. Although advertisers pay nearly triple for behavioral targeting across programmatic buys, the associated increased layers of complexity result in publishers earning just 4% more revenue, according to The Wall Street Journal.
And while conventional wisdom dictates that behavioral segments help advertisers reach their audiences, a recent DV/Sapio study has found when it comes to connecting with consumers, context matters. Specifically, DV’s findings indicate 69% of consumers are likely to view an ad if it is next to contextually relevant content, and 44% of consumers say they have tried a new brand after viewing it alongside relevant content.
Brands that are using updated contextual strategies are already seeing the benefits. Ted Oh, Digital Marketing Specialist at FanDuel, a sports-tech entertainment company using DV’s custom contextual solutions, explains, “Implementing a contextual targeting strategy early makes sure we are prepared for cookie deprecation, and it lets us realize the benefits of finding our audience when they’re consuming relevant content.” To successfully execute contextual marketing, advertisers should familiarize themselves with the sophisticated, new tools that go beyond traditional keyword targeting.
Ontology vs. Keyword Targeting: Not All Contextual Targeting Is Equal
Over two decades ago, marketers began using keywords as a proxy for content to help drive relevant, successful campaigns. The maker of an espresso machine, for instance, might want to target recipes for at-home lattes or news about the benefits of drinking coffee. This technology, though, has drawbacks that can limit its effectiveness for brands without the expertise or time to finely optimize long lists of keywords.
In response, ontology, the grouping of concepts based on their properties and relationships, and semantic science, which combines human expertise with automated textual analysis to understand the meaning of language, emerged.
Addressing Keyword Targeting Challenges with Ontological Solutions
Traditional keyword targeting presents three major challenges that can be solved by taking an ontological approach.
How to Prepare for Cookie Depreciation with Contextual Targeting
By eliminating the need to tie user-data to relevance, contextual targeting offers a solution to reach users without relying on third-party cookies. DV now offers custom contextual categories that can be used alone or in conjunction with other segments and targeting parameters to increase reach and relevance.
Semantic Science, which leverages ontology and machine learning to drive accurate content classification and ensure accurate coverage, is at the core of DV’s custom contextual targeting. In our next post, we’ll explore how when the focus shifts from keywords to relevant content, segments can be built that make inferences about the reader/viewer.
Until then, to learn more about contextual targeting, read our recent guide, The Resurgence of Contextual Targeting.