The use of Artificial Intelligence (AI) is rapidly changing how broadcasters personalize content and generate revenue. This article examines how AI analyzes viewer behavior, optimizes advertising placement, and enhances content recommendations, ultimately boosting viewer engagement and maximizing profits.
Experts like Siddarth Gupta, principal engineer at Interra Systems, highlight AI-driven content recommendations as key to increasing watch time and viewer satisfaction: "AI-driven content recommendation 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 also fosters loyalty, ensuring audiences remain connected to a particular network or platform."
Kathy Klinger, CMO at Brightcove, emphasizes AI's role in reducing churn and strengthening viewer loyalty: "By analyzing vast amounts of data, AI ensures viewers are presented with content they’re most likely to enjoy, keeping them engaged and reducing churn. This creates a seamless, personalized experience that resonates with individual preferences, making it easier for audiences to discover and connect with new content. In turn, it strengthens viewer loyalty and empowers creators and platforms to deliver greater value to their audiences."
The discussion also covers the successful implementation of AI in major platforms like Netflix, YouTube, and TikTok, illustrating its impact on market share and profitability. Sam Bogoch, CEO of Axle AI, notes: "The most successful OTT platform, Netflix, relies heavily on AI-driven content recommendation and has clearly benefited from it in terms of market share and profitability. Likewise, in the world of media websites and apps, YouTube and TikTok are also leveraging AI-driven recommendations to further their success. It’s clear that any successful viewer retention and growth strategy should at least have AI and rich content metadata as a key ingredient."
Beyond recommendations, AI is used for rights management and compliance monitoring, improving operational efficiency and revenue generation. The use of predictive analytics is also highlighted for its ability to optimize content licensing and subscription models, resulting in increased profitability. The importance of ethical considerations, data privacy, and bias mitigation in AI development is also stressed by several experts.
Several experts shared their insights on leveraging AI for targeted advertising and subscription models, along with its impact on operational efficiency and revenue generation. The discussion also covered the balance between personalization and privacy and the ethical considerations involved in using AI for content recommendation and ad placement.