CTV is on a surge as an advertising landscape, the projected growth for 2023 is 13.2% and $25.9 billion in revenue.
CTV advertising has disrupted linear tv advertising, between Q4 of 2021 to Q4 of 2022, a global media and technology company lost over 2 million subscribers. Also, as per the predictions the US household with traditional TV is expected to drop.
With linear TV advertisers were buying ad slots for a specific show to run their ads. This method of advertising lacked precision and relevancy. Linear Tv ads have often been irrelevant to the audience at the time they reached them, resulting in significant ad wastage.
Well, Connected TV advertising has emerged as a robust solution with its capabilities surmounting the linear TV challenges. But all that glitters isn’t gold!
While its potential is undeniable, navigating the complexities of this nascent landscape requires a keen understanding of its limitations and opportunities. This blog will delve into both sides of the CTV coin, exploring its strengths and weaknesses, and providing insights into how advertisers can leverage its power while navigating its challenges. So, buckle up and join us as we journey into the exciting, yet complex, world of CTV advertising!
Challenges of Traditional CTV advertising
1. Limited Precision
Traditional CTV advertising relied on broad demographic targeting. Advertisers faced the challenge of reaching the intended audience and failed at offering personalized ads to the audience.
2. Ad Placement
Advertisers have grappled with challenges related to low engagement stemming from ineffective ad placement strategies, leading to the onset of ad fatigue among viewers. The lack of contextual alignment resulted in advertisements being perceived as irrelevant to the target audience. Compounding this issue, the repetitive display of the same ad to a particular audience further exacerbated viewer disinterest.
Furthermore, concerns about brand safety escalated, as these ads were frequently positioned adjacent to inappropriate content.
3. Measurement and Analytics
Traditional CTV advertising did not offer measurement tools to the advertisers. This was a huge challenge as advertiser’s were unable to optimize their campaign in effectively.
CTV advertising was indeed in need of a robust. This is when the CTV was integrated with AI. But first let’s understand the role of AI in advertising.
AI in Advertising
AI is permeating every domain, simplifying human efforts in the advertising industry is no exception.
The integration of AI technologies continues to shape the future of advertising, making it more targeted, efficient, and responsive to the dynamic nature of consumer behavior. This integration has been playing an active role in every sphere of the campaign, from audience targeting to optimization.
AI increasing the efficiency of CTV Ads
The efficiency of CTV ads was significantly boosted when integrated with Computer Vision, Natural Language Processing and Machine Learning.
Computer vision helps in comprehending and interpreting the visual data, extracting valuable data for advertisers. This included recognizing scenes, objects, and even specific brands or products featured in the content. Advertisers can leverage this capability to ensure contextual relevance when delivering ads.
By leveraging visual data, advertisers can create more engaging and personalized experiences for viewers, ultimately optimizing the impact and efficiency of their CTV advertising campaigns.
Another crucial role is played by NLP. Natural language processing (NLP) is essential for deciphering the textual elements of CTV content, including speech-to-text, closed captions, subtitles, video titles, video descriptions, and other metadata, whereas computer vision concentrates on the visual elements of the material. Machines can now comprehend and derive meaning from human language because of NLP approaches, which makes it easier to categorize CTV inventory.
It is possible to identify subjects and even emotions in the textual data related to CTV content by using NLP algorithms. Analyzing a TV show’s dialogue or a movie’s synopsis, for instance, can reveal important details about the asset’s genre, plot, target market, IAB Category, and brand compatibility.
Apart from this AI has also helped advertisers in audience segmentation and precise targeting. Machine learning analyzes the already historical data (viewing habits, preferences, and demographic information) of the user to predict the likelihood of their actions that the user would take. This helps advertisers in offering a much personalized ads to the user by being contextually relevant.
Conclusion
CTV offers advertisers a promising tomorrow with the capabilities it holds. Not just advertisers, even consumers are inclined towards the platform. In 2023, there were 88% of households in the US that owned at least one connected device.
The market behavior suggests that CTV will be on the rise and with advancement in technology advertisers can make the most out of the opportunities the platform offers. Analyzing the valuable insights and available data through AI can help advertisers in creating data-driven strategies to boost their CTV campaign performance.
