Olga Zharuk
TeqBlaze / Chief Product Officer
We continue the series of blog posts on alternatives to third-party cookies. This article is about a targeting method that has been around for a long time but was reborn and given new opportunities with the advancement of artificial intelligence (AI) and machine learning (ML) technologies — contextual targeting.
Content-related (or contextual) targeting refers to the range of advertising approaches based on content categories, topics, sentiment, key phrases, keywords, and other taxonomy patterns rather than user data. We suggest improving targeting accuracy by combining cookie-based behavioral targeting and contextual campaigns. TeqBlaze engineers highly recommend combining these tactics. As cookies will still be available for operation, you should use all website visitors’ behavioral habits (search history, page time, purchases, and other data) to improve campaign effectiveness.
Cookieless technologies are integral to the ad tech market evolution, and contextual targeting is entirely cookie-free. Still, relying heavily on contextual targeting alone is a weak idea. Combined approaches lead to success. Here is our roadmap to supplementing contextual with behavioral targeting:
- Identity solutions, SDA, and first-party data — these technologies allow ad targeting based on the data of specific users, using anonymized cross-platform user profiles with compliant zero-party (willingly shared like via surveys or account creation) and first-party data (collected from direct interactions as in-app behavior or purchases) for precise identification.
- Google Privacy Sandbox — an alternative way to target a broad Chrome audience based on content topics that users consume and groups of users united by similar interests. A platform whose potential has yet to be unlocked for wide market participants.
Understanding contextual advertising
Let’s start with the challenging part of the story. As we outline contextual advertising’s weaknesses, its strengths will also become clear.
- There is no single procedure for operating contextual data
Contextual advertising uses intelligence solutions based on algorithms and programmatic platforms to scan websites or app content. These solutions look at keywords, phrases, and themes to determine the most appropriate ads to display.
Contextual intelligence solution tasks:
- Crawl domains and apps of connected supply partners
- Categorize pages and screens according to the chosen taxonomy
- Store this data
- Update this data
- Train the system on this data, utilizing Machine Learning (ML) and Artificial Intelligence (AI) algorithms for better ad campaign performance
The first option is to use one of the many ready-made contextual intelligence solutions. The second option is develop a custom contextual intelligence solution. In this case, the publisher bears all of the cost to design the solution, from renting a server to evaluating the effectiveness of the deployed models.
- There is no single contextual taxonomy
Yes, the IAB Content Taxonomy 3.0 initiative is already addressing the issue of standardization. IAB Tech Lab Working Groups are actively working on the development of the industry, and the combined efforts of many market players bring generous harvests at least several times a year. We encourage you to follow the successes of the working groups to grow your business. However, a comprehensive transition to a single market standard for contextual targeting still needs to be made. The main reason for slowing the transition is that a taxonomy created on behalf of the particular company and adapted to the specifics of target markets may be more effective than a single standard.
- There are technical difficulties with contextual campaign targeting for CTV and OTT
While the IAB Tech Lab initiative works to solve the technical challenges, contextual targeting will continue to be deeply semantic. The development of contextual intelligence solutions will accelerate the quality of streaming content processing by categorizing it according to enriched taxonomic standards and supplementing it with features that enhance user experience. For instance, AI models for detecting scene and shot changes to insert ads with no content interruptions.
Scaling contextual advertising campaigns to specific video platforms is also a bit tough, as the contextual parsers often face restrictions by publishers on connecting to video players. This complicates the process as it requires direct integration of the ad platform.
Prospering with contextual advertising
Reports estimate that the global contextual advertising market will reach $335.1 billion by 2026, growing at a CAGR of 13.3% over the analysis period — relatively modest estimations. In its guide to contextual advertising, the IAB cites that $412 billion will be spent on contextual advertising by 2025.
Switching to context started heavily back in 2021, here we will quote Digiday:
- A number of publishers, including Tastemade and Crackle, have added contextual targeting capabilities for their OTT and CTV inventory.
- IRIS.tv, which operates a contextual targeting marketplace, has been growing 25% per month since the start of 2021 and now handles 28 billion ad requests per month, according to a spokesperson.
- Xandr announced it had launched a contextual targeting capability for CTV inventory, as part of a bid to maintain the momentum behind the video side of its business, which now represents 35% of the spend on Xandr’s DSP.
If you have not yet started your journey in contextual advertising, it’s still worth it. Currently, the most common types of traffic for contextual advertising are Web and In-App. Streaming video content, such as CTV or OTT, and other forms of video content are developing slightly slower. The development of advertising technologies makes integrating ready-made solutions for obtaining contextual data accessible, even based on an already deployed custom model. Programmatic advertising platforms support a vast number of contextual scenarios. Although developing contextual infrastructure is a more complex task, we can compare it to the organic promotion of websites through SEO. It has been a reliable investment for a long time, and we predict that remain so.