Show how Redis RAM, Redis Flex, RDI, and Redis FeatureForm turn a viewer session into a ranked next best experience that improves session starts, retention, and monetization timing.
Streaming platforms already have viewing history, session state, content rights, and monetization context. The problem is the next recommendation is usually built on what a viewer watched last week, not what they're signaling right now. What I'm going to show you is a next-experience decision that reads the live session and chooses the best play for engagement and revenue at the same time.
The platform already has models. The harder problem is deciding the right experience for this viewer right now across home screen, player flow, and monetization surfaces. Redis Iris is the context engine — the operational context layer that makes that possible.
The ingest layer has two jobs. RDI handles change data capture and sync from the core repositories. Redis FeatureForm handles the feature pipeline into the Redis context layer. Two tools, two roles, one unified ingest layer.
Redis RAM holds the hot session state. Redis Flex holds the broader viewing history, taste embeddings, and household graph. And the Redis Context Retriever sits below those stores, assembling the Viewer 360 — watch history, content affinity, and session context — and exposing it as structured tools for the decision engine.
Now let me show you the viewer session where that architecture matters.
This is not a simple content relevance problem. The platform has to decide between continue-watching, live sports, short-form discovery, and monetization timing. That is a classic real-time decisioning problem because the right answer changes with the moment.
The next step is assembling the profile, session, and rights context in one place.
RDI synchronizes profile, entitlement, catalog, and campaign state. Kafka streams live session behavior and event changes. Redis FeatureForm keeps the engagement and monetization features online and ready.
Once those feeds are live, the platform can assemble the viewer 360 in one response path.
The system has to combine long-term taste, current session timing, household behavior, ad tolerance, and live rights in one shared operating picture. That is what lets the platform make a better experience decision than static row ordering.
Redis Context Retriever assembles the Viewer 360 — watch history, content affinity, and session context — so the decision engine has exactly the live context it needs.
With the viewer context assembled, the next step is serving the features that drive the experience ranker.
The challenge is not training a recommender offline. The challenge is serving the right engagement and monetization features in milliseconds when the home screen renders. Redis FeatureForm on Redis solves that with train-serve parity and a shared online feature layer.
Now the platform can rank the possible next experiences.
The live sports entry point wins because it has the highest immediate start probability in this exact moment. Then contrast it with the other options. The short-form row is a strong backup. Continue-watching is still relevant, but not the best current-session path.
That ranking matters because it changes session starts, retention, and yield at the same time.
Translate the story into streaming economics. Better next best experience decisions create more starts from home, reduce abandonment, improve premium upsell timing, and lower churn pressure through better first-session outcomes.
Now let's look at how that appears on the actual surface.
Same home screen. Same viewer. Different decision layer. Without Redis, the platform falls back to static row logic. With Redis, the platform chooses the experience path most likely to start a meaningful session now.
And that outcome ties directly back to the architecture we started with.
Close the loop. Profiles, telemetry, catalog, ad, and live-event systems stay in place. RDI and Redis FeatureForm make them operational. Redis RAM and Redis Flex serve the hot and warm viewer context. The decisioning stack turns that into the best next experience before the viewer drifts or churn pressure rises. Position the next step as one cohort, one surface, and one start-rate KPI pilot.