Why You Need to Fix the Foundation Before Personalization Can Work

Viewing time up by 54% year over year.

Over a third of on-demand content is consumed through personalized recommendations, promotions and delivery.

These are the numbers a well-executed viewer’s personalization strategy can produce. But this will happen only if you start with the right step: fix the foundation first.

In today’s crowded SVOD and AVOD landscape, personalization isn’t just a nice-to-have feature—it’s essential for survival. With the average household subscribed to 4-6 streaming services, standing out means delivering a truly personalized experience that connects viewers with content they’ll love before they even know they want it.
In fact, it hasn’t always this way.

“Personalization doesn’t work.” “We tried it, but the outputs were generic.” “It’s just not worth the effort.”

We’ve heard these objections from content providers across the industry. From tier-1 broadcasters to emerging OTT platforms, the frustration is palpable when personalization initiatives fall short of expectations.
Sometimes, this is a fair reaction. If the groundwork isn’t there, personalization won’t deliver. This isn’t about tools or hype. It’s about readiness.

The Invisible Problem Undermining Your Personalization Strategy

Personalization doesn’t fail because the idea is flawed, it fails because the foundation is weak. Without quality metadata, even the best algorithms are flying blind.

Think of metadata as the DNA of your content catalog—it’s what makes each title uniquely discoverable, contextual, and connectable. Without robust metadata, your AI-powered recommendation engine is essentially trying to match viewers with content using incomplete information.

Most platforms have the potential, but too many jump ahead in the journey, skipping the essential first step: understanding what’s in their catalog. And not just in terms of content per se, but how and if every title is described in a way anyone can understand.

For example, here is how a main title like Harry Potter & the Deathly Hallows should look. 

Imagine a comprehensive metadata structure showing fields like:

• Multiple genres (Fantasy, Adventure, Coming-of-Age)
• Actors (Daniel Radcliffe, Emma Watson, Rupert Grint)
• Director (David Yates)
• Mood tags (Suspenseful, Emotional, Epic)
• Themes (Magic, Friendship, Good vs. Evil)
• Content ratings
• Descriptive synopsis
• Contextual information

If the majority of your titles already have this metadata structure, then bravo! You are ready to personalize.

Otherwise, before you fine-tune algorithms, redesign the UX, or invest in thousands of titles no one will ever discover, zoom out and look at your catalog. 

The Reality Check Your Platform Needs

If you run a catalog analysis, this is probably what you would find: big gaps across key fields (actors, writers, directors, genres), inconsistent labelling from one title to the next, thin or missing data that made the recommendation logic fall flat.

We see this pattern repeatedly across providers—enthusiasm for implementing advanced UX personalization without first addressing the underlying metadata infrastructure. It’s like building a smart home without proper electrical wiring; no matter how advanced your devices, they simply won’t function correctly.

The system isn’t underperforming. The input just isn’t there.

Catalog analysis gives you a clear picture of what’s working and what’s missing. It is the only way to know whether your platform is ready for personalization.

From Metadata Chaos to Personalization Success

The good news? You don’t need to wait to rebuild everything. 

If your metadata is scattered or shallow, you can level up with a few proven methods:

• Keyword extraction: fast, easy to scale, cheap, not always effective
• Enrichment via metadata provider: more effective, but also more expensive
• From existing textual information (via GenAI): add semantic understanding
• Multimodal AI extraction: more nuanced information

Each method has pros and cons, but the point here is that you can start improving without slowing down your operation.

The key is to prioritize. Focus first on enriching the most-watched 20% of your catalog, which typically accounts for 80% of viewing time. This creates immediate impact while you gradually address the rest.

You could deploy a first “Recommended for you” carousel using a collaborative-filtering algorithm while you nudge the metadata with the semantic enrichment.

Beyond Basic Recommendations

And once your metadata backbone is in place, you can go beyond providing advanced use cases like chapter and short recommendation…

With proper metadata infrastructure, personalization evolves from basic “more like this” recommendations to sophisticated, contextual experiences that adapt to viewing habits, time of day, device, and even mood.

Imagine dynamically adjusting your entire UI based on viewer preferences—reshuffling carousels, highlighting specific genres during weekend evenings, or surfacing family content after school hours. These capabilities aren’t futuristic; they’re immediately available once your metadata foundation is solid.

Metadata is the silent engine driving discovery, engagement, and monetization. It turns a static catalog into a dynamic content ecosystem.

Your platform becomes more relevant, more personal, and more sticky. And viewers feel it. They may not understand the technical work happening behind the scenes, but they experience the difference between a platform that truly understands them and one that doesn’t. In today’s competitive streaming landscape, that difference is what turns casual viewers into loyal subscribers.

The path to exceptional personalization starts with a single step: fix your metadata foundation first. Everything else follows.