Personalisation at scale: The key to OTT and pay-TV success

This article is part of our Opinions section, where we invite industry professionals to share their views on the most pressing technology questions of our time.

Let’s be honest: today’s TV viewers don’t just want more options. The reshaped expectations surrounding content delivery, as well as the flexibility and control introduced by on-demand video platforms, have led consumers to expect tailored experiences that accurately reflect their preferences, habits and contexts.

Hence, as they enjoy media on their own schedules across various devices, traditional linear viewing feels increasingly outdated. This is particularly evident among younger audiences like Gen Z, who exhibit a strong inclination towards Video OTT platforms and demand nothing short of seamless, individualised user experiences.

Personalisation is central to meeting demand, as it addresses the growing problem of decision fatigue: when faced with endless choices, users struggle to find something to watch. If implemented effectively, smart personalisation surfaces relevant content quickly, increasing user satisfaction, encouraging exploration and time spent on the platform. These features are critical drivers of retention and advocacy, which, in turn, directly translate into revenue growth by generating more opportunities for monetisation through subscriptions, advertising and other methods.

For instance, personalised experiences can drive dynamic ad insertion, upselling to premium tiers or bundled services and improving overall lifetime value per user. As industry giants already shift focus from mere subscriber acquisition to maximising the value of each user, the importance of retaining loyal, engaged subscribers only proves itself even more paramount than ever before. However, to truly unlock these business benefits, personalisation must also be delivered at scale, which is oftentimes easier said than done.

One of the primary challenges to implementing effective personalisation for millions of users is data privacy and compliance. Personalisation relies heavily on collecting granular user data, including on viewing habits, preferences and demographics. And though securely managing this sensitive data is a must, navigating the complex landscape of data privacy laws, such as GDPR in Europe and CCPA in California, while maintaining user trust, is a demanding process.

Leveraging customer data for personalisation requires careful management to balance user needs with interests like promoting brand-sponsored content or using insights from social networks. Furthermore, user data may also contain inaccuracies or require cleaning and enrichment from multiple sources, adding to the complexity of handling this sensitive information.

The inherently fragmented device ecosystem only makes the situation worse. The rise of OTT services has been facilitated by their accessibility across various devices, including smart TVs, tablets and smartphones, ultimately providing a web-like ecosystem. Therefore, users expect a consistent experience regardless of the device they use to view content, which enhances satisfaction and builds trust. Still, delivering dynamic, personalised content and interfaces across this multitude of devices and platforms introduces notable technical complexity. 

Finally, in a world of “watch now” expectations, delays are unacceptable. Historically, content recommendations were shaped by speed-related factors such as proximity to cached content. With the rise of private CDNs and the expansion of global delivery networks, physical proximity is becoming less critical. Instead, system efficiency now hinges on technologies like just-in-time packaging, encryption and the use of object storage for rapid retrieval across vast libraries. These innovations enable the high-speed delivery required for responsive content management.

In this context, delivering real-time personalisation, or “Real-time Relevance”, depends on automated data curation and the optimised processing of user event data.

Given the sensitivity of user data and the value of premium content, personalisation must be built on a foundation of robust security. This involves not only protecting user privacy through secure data management but also implementing best-in-class content protection and cybersecurity measures across the entire platform.

Operationally, this means embedding privacy-by-design principles into all data workflows, enforcing granular consent management, and implementing region-specific compliance logic to align with various regulations. Data retention policies, encryption at rest and in transit, and continuous audit trails further ensure accountability and compliance. In this sense, security isn’t just about compliance; it’s about building the trust necessary for users to share the data that fuels effective personalisation.

To address fragmentation across devices and platforms, streaming services must invest in unified experience orchestration, ensuring that personalisation is not only intelligent but also seamless and continuous across every touchpoint. This requires implementing cross-device identity resolution, enabling a user to start watching on a smart TV, continue on a mobile phone and pick up later on a laptop, all without losing continuity or contextual relevance.

Companies like Disney+ and HBO Max have begun leveraging cloud-native infrastructures and experience management platforms (XMPs) to synchronise recommendations, watchlists and user interfaces across ecosystems. The key lies in decoupling the personalisation logic from any single device or app instance, instead using a centralised, API-driven layer that pulls from real-time user signals and metadata.

As for the need to meet modern users’ demands without delay, leading platforms are shifting toward what can be termed as a ‘living media’ model, one that continuously adapts to audience behaviour in real time through modular, metadata-rich content architectures.

This strategy begins with identity resolution across devices and services, enabling a unified view of the user journey despite the decentralisation of consumption. With AI-powered content tagging and context-aware recommendation engines, platforms can dynamically assemble content experiences tailored to micro-audiences not just by genre or watch history, but by mood, location and even moment of the day. For example, Netflix’s investment in “collections” and micro-genres or YouTube’s adaptive homepage feeds illustrate how smart segmentation can turn fragmentation into a strategic advantage.

As media, tech and telecom continue to converge, mass broadcasting is set on the trajectory of yielding to hyper-personalised, on-demand viewing. And while delivering personalisation at scale definitely comes with real technical and regulatory challenges, it’s no longer an option but a necessary competitive edge. Hence, our responsibility is to optimise platforms of the future – those that can securely, intelligently, as well as seamlessly deliver the right content to the right user at the right moment.