January 5, 2022
The announcement from Google that it will no longer allow third-party cookies in Chrome from 2023 on has stunned many in the industry. In addition to in-progress ID solutions, contextual targeting appears to be complementary to navigate in a cookieless world.
Since contextual advertising has been around for some time, it has been used complementary to more traditional cookie-based campaigns. With third-party cookies going away, contextual targeting will come to the rescue. According to the research, the global contextual advertising market is expected to reach US$376.2 billion by 2027. (Global Contextual Advertising Industry, n.d.)
A tiresome topic for many advertisers, but mostly only because they lack the appropriate tools and technologies. Personalized advertising without personal data — sounds strange. But actually contextual targeting is not based on cookies or stored user behavior. This is good news for users, as well as it is for publishers and advertisers.
The concept of contextual targeting refers to the execution of advertising within a thematic environment. Some providers analyze keywords to identify this environment. It is, however, not always sufficient. Here’s an example: An article about Corona and hygiene tips might provide an interesting environment for healthcare product providers. But only if the tone of your contribution is neutral or positive. Advertisers will not want to be associated with an article that talks about poor hygiene and associated deaths. So, there are nuances involved.
Contextual targeting allows advertisers to serve ads to users who are already visiting the ad related content. Advertisers can target and block specific topics, keywords & phrases using contextual targeting, such as traveling, sports, entertainment, music, food, and hundreds more. Contextual targeting is achieved by using a Demand Side Platform (DSP) and purchasing ads that match keywords and phrases. Rather than relying on past behavior as performed by behavioral targeting to determine what users are interested in today, using context provides advertisers with information about what users are interested in currently.
Yet, contextual targeting should also be taken with a grain of salt. To better understand, let’s take an example: What if a generic make-up brand’s ad appears on a cruelty-free cosmetic lifestyle website? The topic or keywords would match the targeting criteria however the context details should be taken into account. This arrangement is likely to disturb the audience and put the company’s reputation at risk. Advertisers can take ownership of their online safety by creating “whitelists” and “blacklists” that categorize safe and unsafe sites for your brand precisely.
To improve brand safety with contextual targeting, it is best to provide blacklists and whitelists. This can be included in the ad verification platform you use, and the options are keyword blocklisting and URL scanning via machine learning algorithms. Content categories and industry verticals can also be used to determine your brand’s black and white lists. It is better to deep dive and have as many content avoidance categories as possible. (The Content Verification Guide, n.d.)
It has progressed so much beyond just content and keywords on a page in the past years. Emerging artificial intelligence techniques such as Computer Vision enable the processing, analysis, and interpretation of digital images and videos. Ads can be targeted based on sentiment, for example whether the content is positive or negative, happy or sad, or based on the placement of the ad, the device it’s displayed on, the time of day, type of ad, and many other factors.
Image analysis using such tools is obviously useful for detecting any sensitive or problematic content. However, there are limitations for example they cannot be expected to identify specific individuals, locations or other elements to a reliably high degree. (Golowczynski, 2021) So while they can play an important role, their limitations also highlight the importance of being able to understand images through access to comprehensive metadata.
The Internet is made up of thousands of languages with countless dialects and idiolects. Recognizing meaning and sentiment, understanding acronyms, correctly interpreting words or sentences with multiple interpretations across different languages — all these are major challenges for people who work in the field of semantics. Take, for example, the words “battle” and “dance battle”. The word “Battle” means a conflict with weapons, however “Dance Battle” is a free-style competition.
As mentioned above, contextual targeting has its shortcomings. Special systems are used to identify the safety with text sentiment analysis and can thus make environment targeting more reliable. Is this the future of targeting post-cookies? Only partly. Contextual targeting offers interesting possibilities, such as very sharp target groups. On the other hand, it has limited capability, due to some reasons:
Although artificial intelligence and machine learning have led to advances in analysis, the sole focus on contextual targeting means a step backwards. On the upside, using this approach, no audience targeting is required, hence no sensitive user information will be used, this gives 100% safety with current and upcoming data regulations globally. (Understanding Contextual Targeting | IAB UK, n.d.) The downside is that frequency capping, creative personalisation, and measurement will be limited — unless they are combined with ID-enriching inventory.
*For more information on ID read our blog post ‘The Future of Advertising in a Cookieless World Part ii: ID’
Global Contextual Advertising Industry. (n.d.). Reportlinker. Retrieved December 9, 2021, from https://www.reportlinker.com/p05798251/Global-Contextual-Advertising-Industry.html?utm_source=GNW
The Content Verification Guide. (n.d.). Iabuk.Com. Retrieved December 24, 2021, from https://www.iabuk.com/news-article/content-verification-guide-how-implement-brand-safe-strategy
Golowczynski, M. (2021, September 19). Contextual targeting: How we’re changing the game for online image advertising. SmartFrame. Retrieved December 24, 2021, from https://smartframe.io/blog/contextual-targeting-how-were-changing-the-game-for-online-image-advertising
Marvin, G. (2021, August 28). The new contextual ad targeting works, study says. Search Engine Land. Retrieved December 24, 2021, from https://searchengineland.com/the-new-contextual-ad-targeting-works-study-says-331574
Understanding contextual targeting | IAB UK. (n.d.). Iabuk.Com. Retrieved December 24, 2021, from https://www.iabuk.com/user-identity/understanding-contextual-targeting
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