The ad tech industry opens up new frontiers of the privacy-first future where third-party cookies, IDFA and some other user identifiers are no longer present. The advertising tactics that they power, such as audience-based targeting, will soon become obsolete or changed at their core. How should publishers react?
The loss of cookie-based targeting without viable alternatives can harm publishers’ CPM and growing ad revenues. As it becomes more difficult to reach target audiences without cookies, advertisers may either opt for spray-and-pray tactics or walled gardens with substantial first-party data pools. According to the study conducted by Google, publishers may lose 52% of revenues after disabling 3rd party cookies.
First-party data strategies and private identity plans may solve the need for user-based targeting but will require significant resources from publishers. Even though bigger publishers can and should make strategic investments to flow in these waters, this approach would lack scalability and seamless transitions from one supply source to another.
However, another alternative supported by many ad tech players is targeting by context, where targeting decisions are made based on the on-site web page content rather than behavioral user data. Today’s post will take us into a deeper look at contextual advertising, how native ads integrate into this advertising model and what MGID offers to bridge the gap.
Why contextual targeting is beneficial for publishers
Contextual advertising has evolved dramatically. Two decades ago, contextual targeting was only available at the domain level. Now with the advent of Machine Learning and Natural Language Processing, in particular, it became possible to investigate individual pages, use much more detailed categorization, and assess pages based on topics such as keywords, tone of voice, negative or positive sentiment. Contextual intelligence algorithms can look at not only text but also scan images and videos, differentiating their meaning and conveyed emotions.
These advances allow advertisers to pinpoint the exact places where a user is interested in a topic relevant to the advertised product or shares particular sentiment to the subject. Publishers and platforms can then serve ads on these particular pages to an audience that is actively consuming relevant content.
A recent experiment conducted among Dentsu clients showcases contextual ads outperforming behaviorally targeted impressions using cookies while costing 48% less on average. In another case for a financial client, switching from third-party tracking to the contextual platform by Insider Inc. drove 11% higher click-through rates. Dutch public broadcaster Nederlandse Publieke Omroep (NPO) stopped using third-party cookies and switched to contextual targeting in January 2020; as a result, ad revenues increased by 60–70% YoY. The majority of buy-side professionals also confirm that they would be spending more money on contextually targeted campaigns after the end of the third-party cookie.
When using third-party tracking ad units, it does not matter where you reach the users once they exhibit certain surfing behaviors. Relying on context to make targeting decisions, advertisers give more power to publishers. Contextual targets constitute individual web pages, and publishers can take into account advertisers’ demand when planning content.
Сontextual advertising is privacy-compliant and transparent; it relies on the ability to analyze the content and quality of web pages rather than cookie tracking of website visitors. Users self-identify themselves by showing their real-time interest in the content that is highly relevant for these advertising messages, either by empirical performance or by settings.
Efficacy of native ads
Native ads adjust their appearance and match the surrounding environment in terms of design and layout. Most native ads are displayed within the content recommendations widgets and then refer to brand messages shown within editorial content.
Historically, this form of advertising started long before the Internet, in the form of sponsored articles, infomercials, and advertorials. These old-day promotions also showed the first signs of contextual alignment as they were typically found in contextually relevant printed magazines. For example, as you read a car magazine, you would find promotional articles about new models, concept design, etc.
Today, with targeting by context, advertisers can find relevant web pages and blend in native ads even more, aligning brand messages with the content visitors are actively seeking out. This advertising format also provides more content to viewers, incentivizing advertisers to become more creative and entertaining. So far, native ads combined with contextual targeting strengthen the age-old ideals of relevance, and can potentially add an entertainment factor and improve user experience altogether.
Native advertising engines can add scalability, precision and seamlessness to this approach. Smart execution of contextual solutions will give advertisers the ability to contextually understand the content that the user is currently reading, learn about users’ historical reading behavior through first-party data, and optimize targeting approaches across publishers’ websites. With the real-time optimization, contextual targets will autonomously update to include new environments that proved to be effective.
Contextual advertising solution by MGID
Contextual Intelligence by MGID, uses a proprietary AI algorithm to distinguish meaningful content of the article and evaluate its context and sentiment. The content is identified and labeled based on IAB Tech Lab Content Taxonomy 2.2 or specific requests from advertisers. Our NLP machine learning algorithms have been pre-trained on a large set of articles. They rely on much more than just a few keywords to identify mood sentiment. Worth mentioning that the BERT NLP model that we employ is also used by Google search algorithms.
The resulting categorization of the article context is used for selecting the most relevant ads for users. Contextual Intelligence by MGID also allows advertisers to collect and target interest-based audiences. For example, it is possible to target users who read about sports in articles on other topics.
Conclusion
The death of the third-party cookie is a new beginning for the ad tech industry that opens up new opportunities for major players, including the untapped potential of contextual targeting. While behavioral targeting has relied on user past behavior on the web, the context is focused on actual, current behavior.
Native ads can be perfectly optimized towards this advertising model, creating a more powerful marketing environment and better user experience. Contextual Intelligence by MGID is the effective and smart toolkit to reach publishers’ monetization goals while ensuring an even better ad experience for users in a privacy-complaint and transparent way.
