User Embeddings - The Key to Personalized Streaming Experiences
Streaming has become the dominant form of media consumption. Platforms like Netflix, Disney+, and Hulu have millions of subscribers who watch billions of hours of content. But with massive catalogs spanning every genre, streaming services struggle to engage viewers in meaningful ways and have a hard time preventing churn. Users feel overwhelmed by the immense amounts of choices and lose interest over time.
According to Antenna, the average churn rate for OTT streaming services is around 30-40% annually. Platforms invest heavily in subscriber acquisition costs to replace cancellations. The core problem is streaming platforms fail to offer true hyper-personalization. Basic recommendations based on generic algorithms lead to repetitive and irrelevant suggestions. This frustrates users and limits content discovery.
Each viewer has unique, nuanced interests that evolve over time. To reduce churn and boost engagement, platforms need to model individual users with precision and adapt recommendations in real-time. User Embeddings achieve exactly that by optimizing every phase of the user experience.
How can you solve these problems with User Embeddings?
Thanks to the customizable structure, User Embeddings enables you to tailor your own algorithm regarding your unique needs and targets. You can also simply plug and play built-in algorithms.
For example, you can Boost Engagement with an algorithm that curates the below stages;
- Discover: Randomized picks to enable open-ended discovery
- Dabble: Category variety to pique interest in new genres
- Engage: Blend of personalized and diverse picks to increase interactions
- Obsess: Hyper-personalized suggestions to trigger bingeing
- Crave: Inject unpredictable picks to renew interest
and below transitions:
- Views, likes and binges trigger movement to more personalized states
- After binge-watching, randomness is added to rediscover forgotten interests
Or you can Reduce Churn with an algorithm including the states below;
- Explore: Completely random titles for serendipity
- Sample - Logical connections between titles and genres
- Embrace - Balance custom picks and randomness
- Loyalty - Personalized suggestions match observed tastes
- Commit - Fully tailored recs to mimic a human recommender
and transitions such as;
- Natural progression through states in each session
- Explicit thumbs up/down rapidly update taste profile
- Periodic randomness interrupts repetitiveness
Start Today.
As seen, the future of streaming is hyper-personalization. Generic recommendations frustrate users and lead to churn. Each viewer wants a journey tailored to their unique and evolving interests. With User Embeddings, you can finally make that vision a reality. Modular states and configurable transitions let you shape flows around your business goals.
It's time to graduate from one-size-fits-all experiences. Bring next-level personalization to your platform and turn subscribers into devoted fans. Transform how you connect viewers with content and unlock streaming's full potential. Get started with User Embeddings today.