Regulations introduced by governments worldwide have forced businesses to reevaluate their approach to user data. Tech companies are launching new privacy features and policies restricting third-party tracking. As a result, the cookieless world is coming closer.
Though Google has postponed the end of cookies several times, advertisers relieved by the 2024 delay shouldn’t get too comfortable. Popular advertising methods, like behavioral targeting, have become less reliable and sustainable.
Marketers and advertisers are now shifting focus to acquiring first- and zero-party data, where consent is given knowingly, as well as revisiting methods such as contextual targeting. It has been known since the dawn of online advertising and is now circling back in a completely new form.
It’s time to reshape your advertising strategy to serve ads that grasp users’ attention while not invading their privacy. Is contextual targeting the silver bullet?
What is contextual targeting?
Contextual targeting is a form of targeted advertising that focuses on aligning ads with the content of a particular page rather than targeting based on user data.
This method examines factors like keywords, topics, language, and location to match ads with relevant content and gain visitors’ attention without invading their privacy.
The essence of contextual targeting is ensuring that ad content is relevant and timely for viewers while respecting their privacy rights.
The return of contextual targeting
Contextual targeting is hardly a new form of advertising – it has been around since pretty much the dawn of digital advertising.
Initially, it worked by matching the content of a webpage with the content of an ad based on keywords.
Given the questionable effectiveness of this method and the lax approach to privacy rights on the Internet, businesses shifted toward behavioral targeting to serve more personalized and, as a result, more effective ads.
The rule was simple: the more personalized the ad is, the more impact it has.
90% of leading marketers say personalization significantly contributes to business profitability.
Back then, it all made sense. First, before the enforcement of stringent privacy laws like GDPR, user data was a low-hanging fruit. Second, personalization was and still is a valid way of driving conversions.
This pitch-perfect theory, however, didn’t hold up when tested in the real world, which changed significantly in the meantime.
Behavioral targeting and privacy issues
The problem with behavioral targeting was that marketers rarely bothered to align with the particular stage of the buyer’s journey. For example, it meant that a one-time coffee-maker purchase labeled you a coffee-maker lover indefinitely.
When users were being followed on a massive scale by highly personalized but often not very relevant ads, intrusive remarketing became annoying for them. Eventually, this led to so-called banner blindness, which was unacceptable for companies.
Just 29% of RSA’s respondents agreed that handing over their data resulted in better products or services, down from 31% the previous year. More than half of the US Facebook users were “not very” or “not at all comfortable” with Facebook tracking their activity to compile their “ad preferences”.
Banner blindness was followed by multiple data breaches and misuses, such as Cambridge Analytica and the so-called Russia Gate, to name but a few, that exposed how maleficent data-based business models could be. Users were confronted with the harsh reality that “if something is free, you are the product”.
The rise in privacy laws and consumer concerns around data privacy necessitated a remodeling of Big Tech’s strategies. Thus, contextual targeting re-emerged on the runway, albeit in a more sophisticated and refreshed version.
Contextual targeting is a safer alternative to behavioral targeting, providing a privacy-compliant way to monetize websites and apps amidst tightening data privacy regulations such as GDPR and CCPA.
How does contextual targeting work?
The modern iteration of contextual targeting has been fueled by AI algorithms that are fully capable of understanding the nuances of the content, summarizing it, and extracting the most relevant information. This allows marketers to match ads to content more precisely and place ads where they are more likely to resonate with the audience. Content matching no longer relies solely on keywords.
What is more, AI enables the use of first-party datasets to predict how users will consume content. AI can find behavioral patterns based on previous user behavior, bridging the gap between contextual and behavioral targeting techniques while prioritizing privacy, as it doesn’t require personal information.
The last aspect is contextual data. Previously, contextual targeting was based on a limited amount of data, which significantly impacted its effectiveness. Now, the range of available contextual data is broader. It can include local weather, time of day, local current events and trends, and the content that is being presented to viewers.
AI techniques that support modern contextual targeting
Natural language processing (NLP)
Natural language processing (NLP), a subset of AI focused on enabling computers to understand text or speech in a human-like way, is crucial in revitalizing contextual targeting.
AI equipped with natural language processing (NLP) algorithms can understand content not only by scanning keywords, but also by analyzing the overall sentiment and intent of the content. By understanding the content’s intent, marketers can make ads more relevant to users’ needs.
Image and video recognition
For years, images were a blind spot for search robots. They could not go beyond analyzing meta descriptions such as ALTs and titles and could not “see” the content of the graphics. It was especially problematic due to the rise of video content, widely praised as the most engaging format.
Advancements in image recognition technology have enabled AI to comprehend imagery and video content on a page seamlessly in real time, filling a huge gap and supporting better ad targeting.
Machine learning (ML)
Machine learning (ML) enables a continuous learning cycle where the system improves as more data is collected and analyzed over time.
This leads to a better understanding of which combinations of contextual data contribute to improved ad performance. This way, advertisers can leverage a potent combination of contextual data, machine learning, and AI for smarter advertising strategies.
Contextual vs. behavioral targeting
| AI-driven contextual targeting | Behavioral targeting | |
|---|---|---|
| What is it? | AI-driven contextual targeting analyzes content using various AI technologies such as natural language processing (NLP), machine learning (ML), and image & video recognition to assess text, images, page structure, and other content elements and serve relevant ads. | Behavioral targeting utilizes browsing history, clicks, purchases, and other behavioral indicators to show targeted ads. |
| How does it affect user privacy? | Contextual targeted ads don’t rely on personal data. Thus, they are unlikely to be impacted by privacy concerns. | Behavioral targeting is based on personal browsing data, and as such, it can be perceived as intrusive. |
| How did it develop over time? | Contextual targeting has been considered less effective than behavioral targeting due to limited real-time adjustment capacities, but thanks to AI this belief is changing. With AI capable of “understanding” a website’s content, contextual ads can be both precise and privacy-friendly. | Behavioral targeting, once a marketer’s gem, gradually lost its sheen due to overuse. Incessant personalized ads began to unnerve users, making them feel stalked across the web. Although these ads were personalized, their relevance decreased over time. |
| What does it look like in practice? | If a user is reading a blog about gardening, they may see ads for gardening tools. | If a user often shops online for gardening tools, they may see ads for similar products on different websites. |
Contextual targeting in advertising
The latest iteration of contextual targeting, supported by AI models, has grown into one of the most promising privacy-first targeting methods in the advertising industry. This targeting method resonates with users’ need for personalized experiences and, simultaneously, with their reluctance to share their data too easily.
Only 33% of Americans believe that companies are using their personal information responsibly.
Contextual targeting in Google Ads
What does contextual targeting look like on one of the most popular advertising platforms in the world? The process is quite standard.
Setting up campaign parameters
During the setup phase of their ad campaign, advertisers pinpoint the specific contextual categories and/or keywords they wish to bid on, referred to by the platform as “Topics.” Once the campaign parameters, including reach preferences, have been set, the campaign is ready for launch.
Matching ads with relevant content
Google then sifts through the Google Display Network (GDN) to identify publishers that resonate with the campaign’s contextual benchmarks.
Placing the ads
The creative assets provided by the advertiser are then showcased on the website(s) selected by Google. The scope of ad placement within the GDN is guided by the advertiser’s reach preferences.
Contextual targeting – pros and cons
Pros
- Privacy compliance: Adheres to data privacy regulations like the GDPR.
- Relevancy: Ads are relevant to content, enhancing user experience.
- Less intrusive: Seen as less annoying compared to behaviorally targeted ads.
- Trust building: Helps build trust with the audience.
Cons
- Limited personalization: Lacks personalization as it doesn’t utilize user data.
- Time-consuming: Requires time and attention in selecting appropriate keywords and content for targeting.
- Scaling challenges: Difficult to scale, especially in branded contexts.
- Broad content issues: May not be effective on sites with broad or generic content.
Conclusion
Contextual targeting is emerging as one of the most promising methods of privacy-conscious advertising, facilitating more organic and less intrusive ad deliveries without compromising performance. The modern iteration of contextual targeting is significantly enhanced by advancements in AI that enable a more nuanced understanding and analysis of content regardless of the device on which it is displayed.
The impending phase-out of third-party cookies, assisted by Google’s initiative to sunset them, accentuates the relevance and timeliness of contextual targeting as a viable, privacy-friendly alternative.
