Ever feel the irony of being watched when you’re watching? The rise of TV streaming platforms has empowered viewers with a vast array of content at their fingertips. Yet, platforms presenting a daily menu of ‘binge worthy’ TV, all curated into one tailormade, AI infused watchlist or interface, can often feel super-convenient with a dash of intrusive. Add in hyper targeted ads and friendly viewing reminders to press play where you left off - brings in a mix of satisfaction with a hint of consumer overwhelm.  So, with today’s heightened personalisation, how exactly did we get here? 

The media and entertainment landscapes are currently experiencing huge transformation driven by advancements in technology, plus changing consumer appetites and expectations. As the endless choice of shows keeps flowing in, fresh challenges also arise, including: how do we ensure audiences find content they love amidst so much choice? The number one solution is personalisation, emerging as the answer to offer curated, individualised experiences that engage viewers and create streaming platform loyalty. Win, win? Let’s unpack it. 

Here, we uncover the power of personalisation as it continues to gain more sophisticated momentum, the drawbacks alongside viewer privacy, plus how future trends and industry evolution will continue to unfold with AI at the helm. 

How did the world of platform personalisation take hold?

Personalisation within streaming has evolved alongside the digital revolution. Cast your mind back to traditional TV operating on a ‘one-size-fits-all’ broadcasting model, offering the same programming to all viewers. Seems archaic now, doesn’t it? However, streaming platforms such as Netflix, Amazon Prime, and Disney+ have ushered in a new era, where individual recommendations are central to the user experience. This shift was motivated by a simple fact.

But it’s not just about the shows. Today, personalisation encompasses more than just series or film recommendations. It shapes everything from home screen interfaces and curated playlists, to targeted advertisements, and even personalised viewing reminders. It’s no surprise then that by adapting to user preferences, streaming services have enhanced viewer satisfaction and increased engagement, driving long-term loyalty for the big players. In fact, personalised recommendations account for up to 80% of the content watched on major platforms like Netflix.

Decisions, decisions. Platforms are making it more addictive than ever to keep watching through enhanced user experience – and viewers are loving the bespoke curation.

Let’s get into the benefits, kicking off with reducing the ‘paradox of choice’ – something that’s super-prevalent in today’s content-rich world. How, so? Instead of endlessly browsing, viewers receive recommendations based on what they have watched previously, to match their preferences, and even their mood at a specific time of day. 

Meaning, the back and forth associated with decision-making is solved, to create a viewing experience that’s more seamless and enjoyable. Interestingly, studies show that 70% of viewers are more likely to stay on a platform that provides personalised content, rather than spending time browsing through endless options. 

What’s in it for the streaming services?

Boosting elevated personalisation ensures platforms retain their users by keeping them continuously engaged. Data shows that by amplifying personalised suggestions reduces user churn by up to 50%, as viewers are more likely to continue subscribing when they find content that resonates with their interests. Plus, personalisation boosts average viewing times and increases viewer loyalty, making it a powerful tool for subscription-based video-on-demand (SVOD) services.

Discovery, discovery, discovery

The aim of the game is to keep viewers locked in, which is why content discovery is a key challenge for streaming platforms. Personalisation helps to bridge the gap between a platform's wildly vast content library and the viewer, ensuring those undiscovered hidden gems reach the audiences that would be most interested in them. This can lead to a greater diversity of content being consumed, with some platforms reporting a 35% increase in viewership for lesser-known shows, when personalised recommendations are in place. It also drives the popularity of otherwise overlooked shows or films, which ultimately benefits both the platform and content creators. 

And, of course… revenue

Let’s talk advertising, shall we? For ad-supported streaming services, personalisation also allows for ultra-focussed, targeted ads to zoom in on viewers' interests. The proof is in the profits, as targeted advertising has been found to generate 2-3 times higher click-through rates compared to generic ads, ultimately leading to boosted monetisation for the platform and more satisfaction for advertisers, too. 

What about the AI algorithm? It’s all in the tech 

Artificial Intelligence (AI) and Machine Learning (ML) are the superheroes of personalisation strategies for streaming services. These algorithms learn from viewer behaviour - such as watched shows, viewing times, and user ratings, to make ultra-accurate recommendations. For supreme tailoring and epic consumer satisfaction, this type of continuous learning is essential for creating dynamic, ever-evolving user experiences. Right now, over 90% of major streaming platforms rely on AI-driven personalisation to keep users engaged. 

Get to know content-based & collaborative filtering & natural language processing

To create the most tailored show recommendations, a powerful tech duo combine to gain impressive results for each viewer. In short, the two main models are content-based filtering and collaborative filtering. Let’s break it down. Content-based filtering recommends new content based on attributes similar to the content that the viewer has already watched. 

In contrast, collaborative filtering identifies users with similar tastes and recommends content based on what these peer users have enjoyed. Clever, eh? Combining these methods creates a mighty recommendation engine, with studies showing that hybrid models can increase recommendation accuracy by up to 25% compared to standalone methods.

Next, there’s the more intricate natural language processing (NLP), which enables streaming platforms to understand user reviews, search queries, and social media discussions. This thorough understanding allows for even greater accuracy in recommendations and helps with creating descriptive metadata that drives better search and discovery. NLP also helps platforms analyse real-time feedback, allowing them to fine-tune content suggestions and continually improve user experiences.

That’s the good stuff, now for the concerns… 

Privacy & data misuse

Of course, personalisation relies heavily on user data, meaning companies must tread carefully with growing concerns over privacy and data misuse. To put this into perspective, 83% of consumers currently express concerns over how their data is being used, making it crucial for platforms to maintain transparency in data collection practices, whilst ensuring compliance with data protection regulations like GDPR and CCPA. Streaming services must continue to maintain and build user trust, as it’s crucial that users feel that their data is being handled responsibly and ethically. 

Balancing personalisation & discovery

Sure, personalisation enhances the user experience, but it can also lead to filter bubbles. This is where users are only exposed to content similar to the shows they have previously watched. This can have a knock-on effect, restricting discovery which may cause viewers to miss out on more diverse content. It’s key then that streaming companies are sure to balance personalisation with serendipitous discovery to keep viewers engaged, without boxing them into a narrow content diet. A recent survey indicates that 56% of viewers enjoy discovering content outside of their usual interests, spotlighting the importance of diversity in recommendations.

Content availability & rights management

What happens when a recommendation is available to watch in one country, but not in another due to licensing restrictions? Good question. Streaming platforms face challenges related to content licensing, which can impact the consistency of personalised recommendations across different regions, meaning the quality of global personalisation is ultimately affected. Navigating these issues effectively is essential to maintain the integrity of user experience across regions. We’ll be keeping an eye on movements across this tricky element. 

Let’s look to the future. Where does it all go from here?

Hyper-personalisation

It goes without saying that future advancements in AI will lead to even more refined hyper-personalisation, where platforms can predict not only what users might want to watch next, but also at what point they might disengage. Hyper-personalisation can involve tailoring thumbnails, promotional content, and even genres according to a viewer’s unique preferences. Recent reports predict that by 2025, hyper-personalisation could improve viewer retention by up to 20%. Wow. 

Context-aware personalisation

Thought it couldn’t get any better? Think again, because platforms are also exploring personalisation that factors in the user's environment. Imagine recommendations that could vary based on the time of day, the user’s current activity, or the devices they are using. This type of context-aware personalisation provides even more accurate and enjoyable viewing suggestions, aiming to make content consumption feel as intuitive and seamless as possible. In a nutshell, they’re basically set to know more about our TV taste than our bestie. 

A rise in social viewing & interactivity

Now, this one is extra fascinating… enter the magnitude of watch parties. This is called social personalisation, which considers what friends and family are watching, and it’s gaining traction. As streaming becomes more interactive with features like fun watch parties, personalising experiences based on social groups will become more prominent, enhancing engagement through shared experiences. Platforms that integrate social viewing have reported increases in user engagement by up to 30%, emphasising the power of shared, interactive viewing.

So, that’s the story so far. We’re equally intrigued and excited to see how the power of technology delivers yet more refinements for today and beyond. One thing’s for sure, it’s set to get even more personalised, and we’ll be watching. Happy streaming.

Our sources:

Netflix Recommendations: Beyond the 5 Stars (Part 1)
Netflix Recommendations: Beyond the 5 Stars (Part 2)
Netflix Recommendation System Overview
Deloitte Digital Media Trends Report
Accenture Media and Entertainment
PwC Global Entertainment & Media Outlook
HubSpot Research
McKinsey: The Future of Personalization