Opinion: what can OTT learn from social media?

Bleuenn Le Goffic, VP business transformation at Accedo, explores the opportunities available to video services through emulating social media models

By Contributor

Published: February 12, 2025 Updated: February 13, 2025

Bleuenn Le Goffic, VP business transformation at Accedo, explores the opportunities available to video services through emulating social media models

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Social media used to be a place to keep in touch with old and new friends, but its role has evolved organically over time. Many users are increasingly leveraging these platforms to access everything from news, politics, dating and business, to daily communications and public announcements. And, as more of the world's population become digitally active, social media's reach is continuing to grow. According to analyst firm Statista, there were 5.52 billion internet users worldwide in October 2024, which equated to approximately 67.5 per cent of the global population. Of those users, 5.22 billion were also actively using social media, amounting to around 63.8 per cent of the world's population. Given this exceptionally high number of users worldwide, social media clearly has a winning strategy. So, with that in mind, what can OTT learn from this rapidly evolving sector?

Bleuenn Le Goffic, VP Business Transformation, Accedo Social media's attraction Social media platforms provide a user-centric service that is personalised, immediate, and engaging. Users get instant gratification through bite-sized, entertaining content, and the high level of interaction makes them feel highly invested in the platform. Unlike most other forms of media, where content has traditionally been consumed in a more leaned-back fashion, social media allows users to actively shape and influence narratives. The ability to comment, like, and share content in real time makes them feel part of the conversation.

Providers of OTT services are also well positioned to foster this kind of community buzz', applying data-driven strategies and harnessing AI-powered behavioural insights to identify shared interests, fandoms, and content consumption patterns among viewers. These insights can be used to develop and personalise community-centric features, such as real-time chat during live events, discussion boards for popular shows, or user-generated content hubs where fans can share their thoughts, reviews, and creative works. AI-driven sentiment analysis and predictive modelling can further enrich these efforts, enabling streaming companies to anticipate trending topics or emerging communities, and proactively introduce features or content that resonates with specific audience segments.

Serving the Gen Z audience Rightly or wrongly, social media is seen as a trusted source by many, particularly Gen Z. According to Deloitte's Digital Media Trends 2024 report, over half of Gen Z and millennials prefer to get long form content recommendations from social media rather than SVoD services. Video providers can leverage this behaviour by showing snippets of content on social media platforms like TikTok, Twitter and Instagram. By harnessing the power of generative AI to pre-create editorial prompts for their content teams, they can streamline the creative process and enhance efficiency while ensuring that the right snippets of content are developed and served to the right subset of the audience.

This involves implementing a robust system that integrates media asset management (MAM) with generative AI capabilities. The technology uses advanced machine learning to create rich embeddings of video assets, enabling content teams to search for and retrieve clips using natural language text descriptions rather than manual processes. By training the AI on brand-specific models, it can suggest prompts tailored to align with the video service's unique voice and audience preferences. The benefits are significant: content creation workflows are dramatically expedited, reducing hours-long tasks to mere minutes. This allows editors to focus on high-level creative strategies and delivering personalised, engaging content at scale. Moreover, the ability to mine and repurpose overlooked content provides new opportunities for user engagement while maintaining brand consistency.

Dynamic content feeds Social media platforms are inherently built on personalisation. Users see dynamic content feeds which change depending on preferences and behaviour, and it is often this that keeps them coming back for more because there's always something new to look at. Spotify, while not strictly social media, has demonstrated the power of curated personalised content feeds with features like Discover Weekly which is a personalised playlist specific to individual users, and Blend, which allows users to create a playlist that combines their music tastes with others.

Video providers could adopt a similar approach, using user and catalogue data, and leveraging analytics tools and generative AI. This enables them to anticipate user preferences and deliver a daily or weekly reel showing personalised highlights from viewer's favourite shows, or a curated playlist of episodes or clips based on trending topics. This can be achieved by employing technologies such as collaborative filtering, natural language processing, and reinforcement learning. By creating a rapid editorial flow which ensures building frequent touchpoints with the audience, users always have something new to check in and engage with, whether that be entirely new content or contextual add-ons relating to their favourite shows. Providing these micro-engagement touchpoints can help services keep users coming back between episodes or seasons.

Gamification and shopping Personalisation can also extend to gamified elements, such as recommending

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