Wurl Finds Issues With CTV Ad Genre Labels
Connected TV ad sellers are not sharing reliable show information, and genre labels are often inaccurate. This lack of standardization may hinder the industry's efforts to provide content signals.
Original reporting · digiday.com
Original: “Future of TV Briefing: CTV advertising has a genre-label problem”
Read “Future of TV Briefing: CTV advertising has a genre-label problem” at Digiday →How this was written. Adtomix wrote this article with a language model from Digiday’s reporting (model: @cf/meta/llama-3.3-70b-instruct-fp8-fast); it describes that reporting rather than adding to it. The original, linked above, is the full story.
Key points from Digiday’s report
- Connected TV ad sellers often fail to provide reliable show information, instead relying on genre labels.
- The lack of standardization in content labeling can hinder the CTV ad industry's efforts to provide content signals.
- The IAB Tech Lab's Content Taxonomy can serve as a foundation for standardization in CTV content labeling.
Connected TV ad sellers have been hesitant to share specific show information with advertisers, according to Digiday. Instead, they often provide genre labels, which can be unreliable. An analysis by Wurl found that only a small percentage of ad requests contained usable show names.
Wurl's analysis, which was conducted over a short period in August 2026, found that many genre labels did not match the company's own classification system. This discrepancy highlights the need for standardization in content labeling. The lack of consistency in labeling can make it difficult for advertisers to target specific audiences.
The issue of inconsistent labeling is not new, and it stems from the fact that content owners are responsible for appending labels to bid requests. However, these labels are often broad or vague, making it hard for advertisers to accurately target their ads. For example, a show like 'The Bear' can be classified as either a drama or a comedy, depending on the context.
Digiday reported that the use of vague genre labels, such as 'entertainment' or 'general variety', is common. This can lead to confusion and make it challenging for advertisers to effectively target their ads. Furthermore, the sheer number of different genre labels can be overwhelming, with Wurl identifying over 3,600 unique labels during its analysis.
The lack of standardization in content labeling can have significant consequences for the CTV ad industry. It may hinder the industry's efforts to provide content signals, which are essential for targeted advertising. To address this issue, a standardized taxonomy is necessary. Fortunately, the IAB Tech Lab has updated its Content Taxonomy to include categories suited to CTV, which can serve as a foundation for standardization.
Wurl uses the IAB Tech Lab's Content Taxonomy to underpin its classification system. The company's CEO, Dave Bernath, emphasized the importance of standardization, citing the historical inconsistencies in metadata around content libraries. He also noted that content owners often rely on outdated labels, which can lead to inaccuracies.
The use of machine learning and content intelligence can help analyze streams and understand metadata, allowing companies like Wurl to coalesce around standard genres. However, more needs to be done to address the issue of inconsistent labeling. The CTV ad industry must work towards adopting a standardized taxonomy to ensure the accuracy and reliability of genre labels.
Questions and answers
Why are genre labels often inaccurate?
Genre labels are often inaccurate due to the lack of standardization in content labeling, with content owners relying on outdated or broad labels.
What is the IAB Tech Lab's role in addressing the issue of inconsistent labeling?
The IAB Tech Lab has updated its Content Taxonomy to include categories suited to CTV, providing a foundation for standardization in content labeling.
How can the CTV ad industry address the issue of inconsistent labeling?
The industry can work towards adopting a standardized taxonomy, using machine learning and content intelligence to analyze streams and understand metadata, and relying on updated and accurate labels.
