This article explores how artificial intelligence (AI) is changing content personalization and monetization in the broadcasting industry. Experts discuss AI's role in analyzing viewer behavior to optimize advertising placement and enhance content recommendations.
AI-driven content recommendation, as noted by Siddarth Gupta, principal engineer at Interra Systems, "taps into users’ viewing habits to deliver highly personalized programming and leads to stronger engagement. Suggesting relevant shows or stories can boost watch time and overall viewer satisfaction." This personalized approach fosters loyalty, keeping audiences connected to specific networks or platforms.
Kathy Klinger, CMO of Brightcove, emphasizes that AI "ensures viewers are presented with content they’re most likely to enjoy, keeping them engaged and reducing churn." This creates a personalized experience that resonates with individual preferences, helping audiences discover new content and strengthening viewer loyalty.
Sam Bogoch, CEO of Axle AI, points to the success of Netflix, YouTube, and TikTok, highlighting the importance of AI-driven recommendations for viewer retention and growth. He states that "any successful viewer retention and growth strategy should at least have AI and rich content metadata as a key ingredient."
Noa Magrisso, AI developer at TAG Video Systems, adds that personalized content suggestions reduce decision fatigue and increase engagement. This provides broadcasters and streaming platforms with a significant competitive edge.
Simon Parkinson, managing director of Dot Group, describes IBM Consulting’s “Catch Me Up” system for Wimbledon, showcasing how generative AI can personalize content delivery to enhance viewer satisfaction. He notes that "creating an experience that enhances viewer satisfaction, and subsequently viewer retention, is key in creating a competitive advantage within the industry."
Stefan Lederer, CEO and co-founder of Bitmovin, discusses AI's use in real-time content analysis for contextual advertising and hyper-personalized recommendations. He emphasizes that video providers are using AI to "analyze user behavior at a deep level to make accurate content recommendations."
Costa Nikols, strategy advisor at Telos Alliance, highlights AI's potential for fine-tuning content delivery and deeper audience segmentation by enhancing metadata and supporting multi-language distribution. He suggests that "AI may well help make this level of customization at scale more feasible and economical."
Dave Dembowski, SVP of global sales at Operative, explains how AI can tailor viewer experiences through data analysis and large language models (LLMs) for linear programming. He also mentions the use of AI for creating smart forecasts of viewership to help with more tailored content schedules to specific audiences.
The discussion also addresses ethical considerations, including data privacy and bias mitigation, as highlighted by Kathy Klinger, Simon Parkinson, and Stefan Lederer. They stress the importance of transparency and responsible use of AI in creating personalized experiences.
Experts also explore how AI enhances content monetization through dynamic ad insertion, targeted subscriptions, and optimized content licensing, as discussed by Siddarth Gupta, Yang Cai, and Zeenal Thakare. AI streamlines operations, reduces costs, and unlocks new revenue streams.
Finally, the role of AI in automating digital rights management (DRM) processes, including contract analysis, content usage monitoring, and breach detection, is also emphasized by Stefan Lederer and Yang Cai. This highlights AI’s potential to significantly improve efficiency and compliance.