Assume the rep or the SA has this script open on a second screen while running the demo. This is not background reading. This is the coaching layer. Use the “Say this exactly” line as the default talk track, then adapt with the framing notes if the room is more executive or more technical.
In streaming media, the wrong ad decision is not just a relevance problem. It is a yield problem, a pacing problem, and a viewer-experience problem. Campaign systems, audience systems, and stream telemetry already exist. The issue is making them act together inside the ad-break budget.
This is built for media platform, ad tech, monetization, and data science teams. The visible demo stays customer-safe. The script is where the presenter carries the detail, the stakes, and the ask.
The data is already there: ad inventory platform, campaign management system, identity graph / CDP, content metadata store, Snowflake customer likes and dislikes, feature lakehouse, and Kafka streams for playback and ad events. The issue is not whether the data exists. The issue is whether it can be assembled and acted on in single-digit milliseconds inside the break while the moment is still live.
That is what this demo shows. Nine stages, one primary decision moment centered on Jordan Kim Household, and a decisioning pipeline that turns scattered operational context, including Snowflake preference data, into the winning action: Premium auto campaign with retail overlay.
Show how Redis gives the ad decisioning stack the live context it needs to choose the right ad impression for yield, relevance, and viewer experience by activating Snowflake customer likes and dislikes at decision time.
This section explains the purpose of the click and why this moment matters in the overall real-time decisioning story.
At the top are the systems of record and the live signal sources for StreamSight Media. Nothing here is being ripped out or displaced.
The ingest layer has two jobs. RDI handles change data capture and operational sync from the core repositories. Redis Feature Form handles the feature pipeline from the analytical and streaming systems into the Redis context layer. Two tools, two roles, one unified ingest layer.
The Redis context layer is the operational working set. Hot data and live session state stay in RAM for sub-millisecond access. Larger history, embeddings, and warm operational context sit in Redis Flex. And the Redis Context Retriever sits below those stores, assembling the Campaign 360 — audience state, inventory context, and bid history — and exposing it as structured tools for the decision engine. This is the layer that makes history and live state usable together.
The decision engine is where rules, eligibility, ML ranking, vector search, and policy arbitration come together. The output channels are where the business sees the result. And the learning loop makes every accepted or rejected action improve the next one.
Snowflake is important here because it holds customer preference data that is usually treated as offline analytics. In this architecture, Redis Feature Form turns those likes, dislikes, and brand exclusions into online features. Redis RAM serves the hot ad-break working set, and Redis Flex holds the broader household history and preference vectors so the ad server can use them while the ad break is still open.
Frame this as one step in the larger real-time decisioning story, with Redis turning scattered data into an action while the moment is still live. Emphasize this point: Lead with Redis as the operational context layer, not a rip-and-replace. The architecture matters because it makes the live decision possible.
The Unified Context Layer is the key tier to reference. Redis RAM, Redis Flex, and Feature Store sit in the top row. Redis Context Retriever sits centered in a second row below them — this is where the Campaign 360 is assembled and exposed to the decision engine.
Practice landing on this transition cleanly: "This is the architecture. Now let me show you what happens when the live customer moment actually starts."
Lead with Redis as the operational context layer, not a rip-and-replace. The architecture matters because it makes the live decision possible.
This is the architecture. Now let me show you what happens when the live customer moment actually starts.
This section explains the purpose of the click and why this moment matters in the overall real-time decisioning story.
Jordan Kim Household is the stand-in for the larger pattern. This is not one edge case. This is the repeatable decision moment StreamSight Media needs to handle every day.
The business stakes are simple: if the platform waits too long or acts on partial context, it loses the moment. If it decides fast enough with full context, it creates better yield, higher completion, and smarter suppression under hard policy and latency constraints.
Frame this as one step in the larger real-time decisioning story, with Redis turning scattered data into an action while the moment is still live. Emphasize this point: Make the business stakes concrete. This is the live moment where latency and context determine whether the company captures value or misses it.
The live trigger centered on Jordan Kim Household, plus the side panel explaining why this moment matters right now.
Practice landing on this transition cleanly: "We have one live moment to recognize Jordan Kim Household correctly and act before the old process falls back to something generic."
Make the business stakes concrete. This is the live moment where latency and context determine whether the company captures value or misses it.
We have one live moment to recognize Jordan Kim Household correctly and act before the old process falls back to something generic.
This section is about how the existing systems stay in place while Redis operationalizes their data. Emphasize additive architecture, not rip-and-replace.
StreamSight Media keeps its existing repositories, models, and applications. Redis is not the new system of record. Redis is the operational serving layer that makes the existing systems act together.
The source systems in this use case are: Ad inventory platform, campaign management system, identity graph / CDP, content metadata store, feature lakehouse, and Kafka streams for playback and ad events.
RDI handles operational sync and change capture. Redis Feature Form handles train-serve parity and online feature delivery.
In this stage, Snowflake is not replacing the ad server or the CDP. Snowflake remains the repository for preference history, while Redis makes those likes and dislikes operational for this impression.
Frame this as additive architecture. Existing systems remain the systems of record; Redis makes their data usable in the live decision path. Emphasize this point: Reinforce additive architecture. RDI and Redis Feature Form make existing systems operational in the moment without replacing systems of record.
Industry repositories and streaming APIs flowing into Redis through RDI and Redis Feature Form, with pipeline status visible on the right.
Practice landing on this transition cleanly: "Redis does not replace the existing stack. RDI and Redis Feature Form make that stack operational in the live decision window."
Reinforce additive architecture. RDI and Redis Feature Form make existing systems operational in the moment without replacing systems of record.
Redis does not replace the existing stack. RDI and Redis Feature Form make that stack operational in the live decision window.
This section is about the unified context layer. Slow down here and show how live signals and durable history come together to produce decision-ready context.
The left panel is who the customer has been over time. The right panel is what is happening right now. The decision quality depends on both.
Use the left side to explain durable context, then the right side to explain the live trigger. Bring the room back to why the winning action is Premium auto campaign with retail overlay and why the alternatives are weaker in this moment.
Redis Context Retriever assembles the Campaign 360 — audience state, inventory context, and bid history — so the decision engine has exactly the live context it needs.
The important point is that this is not just personalization. It is contextual intelligence. History without the live state is stale. Live state without the history is shallow. Redis is the layer that serves both together at request time.
The key difference is that the system is not only asking what campaign pays the most. It is also asking what this household has liked, disliked, avoided, or responded to before, and it can answer that inside the live decision path.
Frame this as the heart of the demo. If the audience remembers one thing, it should be that better decisions come from better live context, not from more static rules. Emphasize this point: Slow down here. This is where unified context becomes tangible: history, live signals, policy, and situational awareness in one decision path.
Two panels: historical context on the left and live context on the right, merged into one working view.
Practice landing on this transition cleanly: "A profile tells you who the customer is. Context tells you what the business should do next."
Slow down here. This is where unified context becomes tangible: history, live signals, policy, and situational awareness in one decision path.
A profile tells you who the customer is. Context tells you what the business should do next.
This section is about why the model or rules engine can act in real time. The message is that online features arrive fast, consistently, and with train-serve parity.
This stage is for the data and ML stakeholders in the room. The point is not the specific feature names by themselves. The point is that the same features used to train the model are available online at the moment of decision with the same definitions.
Explain train-serve parity clearly. Most teams can train a model. The hard part is serving the right features fast enough in production. Redis Feature Form on Redis closes that gap and removes the drift between the notebook and the application.
Tie it back to the visible demo. These features are what allow the system to choose Premium auto campaign with retail overlay instead of defaulting to Generic brand spot or surfacing Competing travel ad at the wrong time.
Redis Feature Form turns the Snowflake preference signals into online features. Redis RAM serves the hot decision state, and Redis Flex keeps the broader preference vector available, so the ranker can see that auto has a strong positive preference and travel has a negative preference before the ad is selected.
Frame this as the bridge between models and production outcomes. The point is not model training; the point is serving the right features inside the latency budget. Emphasize this point: Differentiate analytics from execution. The model is not the hard part; serving trustworthy online features in milliseconds is the hard part.
Online feature cards plus the feature-serving performance panel.
Practice landing on this transition cleanly: "Your model is only as good as the features you can serve in milliseconds, not the features you can describe in a slide deck."
Differentiate analytics from execution. The model is not the hard part; serving trustworthy online features in milliseconds is the hard part.
Your model is only as good as the features you can serve in milliseconds, not the features you can describe in a slide deck.
This section is about the actual decision. The audience should understand that this is not a generic recommendation; it is ranked next-best-action arbitration based on live context.
Now the decision is visible. Walk the room through the winner first: Premium auto campaign with retail overlay. Explain why it wins on relevance, economics, and policy fit for this exact moment.
Then compare it to the alternatives. Generic brand spot is usually the path the legacy process would take because it is simple or generic. Competing travel ad is the kind of action a model might surface if it saw only part of the context or ignored policy and operational constraints.
The point of this stage is to show that Redis is not just ranking what is most likely to be clicked. It is arbitrating across policy, economics, relevance, and timing in one place.
The winning ad is not simply the highest-paying campaign. It wins because it clears policy, fits the content, respects the household's Snowflake preference signals, and still delivers strong yield.
Frame this as decision arbitration. The system is not just surfacing options; it is choosing the best action for this exact moment. Emphasize this point: Show that Redis is not just scoring content; it is helping the decisioning stack rank actions in the real business moment.
The ranked candidate actions, with Premium auto campaign with retail overlay as the winner and Generic brand spot / Competing travel ad as lower-ranked or suppressed alternatives.
Practice landing on this transition cleanly: "We are not surfacing random recommendations. We are ranking the actions the business already cares about and choosing the one that fits this moment best."
Show that Redis is not just scoring content; it is helping the decisioning stack rank actions in the real business moment.
We are not surfacing random recommendations. We are ranking the actions the business already cares about and choosing the one that fits this moment best.
This section translates the technical story into business value. Tie the decision quality back to revenue, retention, risk reduction, or operating efficiency.
The winner here translates directly into business language: better yield, higher completion, and smarter suppression under hard policy and latency constraints.
The value is not one click, one claim, one quote, or one call. It is the compounding effect of getting these moments right at scale.
The visible economics are the reason the next step is a pilot, not just a technical evaluation of whether Redis is fast.
Frame this in business terms only. This is where the rep should own the room and make the value feel measurable. Emphasize this point: Translate the technical story into measurable business outcomes. This is where the architecture earns the right to exist.
The decision economics panel and the side-by-side business impact summary.
Practice landing on this transition cleanly: "The math is not the single transaction in front of us. It is what happens when this decision gets repeated across the full book of business."
Translate the technical story into measurable business outcomes. This is where the architecture earns the right to exist.
The math is not the single transaction in front of us. It is what happens when this decision gets repeated across the full book of business.
This section is the visible before-and-after. Keep it simple and let the audience see the difference between a generic or legacy experience and a Redis-powered one.
The left side shows the legacy experience: generic, delayed, or incomplete. The right side shows the same customer moment with the right action already staged. It is the same surface and the same business flow. What changed is the decision layer underneath it.
The winner is Premium auto campaign with retail overlay. The real product is not the UI redesign. The real product is the ability to put the right action into the existing UI before the moment passes.
Frame this as the payoff slide. Keep it simple: same customer or user, same surface, different decision layer. Emphasize this point: Keep the contrast visual and simple: same surface, different decision layer, very different outcome.
The side-by-side comparison of the generic or delayed path versus the Redis-powered path on the same end-user surface.
Practice landing on this transition cleanly: "Same surface. Same moment. Different decision layer. That is the product."
Keep the contrast visual and simple: same surface, different decision layer, very different outcome.
Same surface. Same moment. Different decision layer. That is the product.
This section closes the loop. Re-state the architectural lesson and remind the audience that the visible output is only possible because the context layer works in real time.
Come back to the architecture now that the room has seen the story end-to-end. The same five tiers are still there, but now the audience understands what each one contributed to the outcome.
Summarize the three takeaways. First, this is not a science project — it is a practical architecture for StreamSight Media. Second, it is additive, not disruptive — the existing systems stay in place. Third, it is a business story first and a platform story second — the reason to do it is better yield, higher completion, and smarter suppression under hard policy and latency constraints.
Close on the ask. The next step is not to admire the demo. The next step is a focused working session to map this reference architecture to the customer's actual environment and scope one ad pod, one premium inventory slice, and a pilot against the current ad-decision path.
Frame this as the close. Re-state the architectural lesson and the next logical step to pilot the approach. Emphasize this point: Close the loop on context and real-time decisioning. End with a pilot-oriented ask tied to one segment, one workflow, and a clear KPI.
The architecture returns with the proven latency, outcome, and scale callouts visible.
Practice landing on this transition cleanly: "You already have the systems and the data. What you need is the layer that lets them act together in the live decision window. That is Redis.
## Anticipated objections
- Our ad server already ranks ads.
- Acknowledge the existing investment first. Then explain that Redis is additive: the current system stays in place, and Redis becomes the low-latency context and decisioning layer on top of it.
- This sounds like another personalization layer.
- Tie the answer back to the architecture. The existing tool or process may do part of the job, but the gap is bringing history, live state, policy, and low-latency serving together in one decision path.
- How do policy and pacing stay under control?
- Make it clear that policy remains upstream and explicit. Redis executes the approved rules, audit trail, and model versions in real time; it does not replace governance.
## Pacing guidance
- Total runtime: 12 to 16 minutes end to end. Budget roughly 60 to 90 seconds per stage, with a little more time on Stages 1, 4, 7, and 9.
- Pacing Guide
- Stage 1: 90 to 120 seconds. Orient the room and establish the additive architecture pattern.
- Stage 2: 60 seconds. Introduce the person and the stakes.
- Stage 3: 60 to 90 seconds. Keep it light for business audiences, deeper for technical audiences.
- Stage 4: 90 to 120 seconds. Slow down. This is where the contextual-intelligence story lands.
- Stage 5: 60 to 75 seconds. Go deeper only if the room wants ML detail.
- Stage 6: 75 to 90 seconds. Walk the winner, then contrast the alternatives.
- Stage 7: 90 to 120 seconds. Translate the demo into business math.
- Stage 8: 60 to 90 seconds. Let the visual comparison land.
- Stage 9: 90 to 120 seconds. Recap and close on the pilot ask.
## Audience calibration
- If the room skews executive, spend more time on Stages 1, 7, and 9 and compress the detailed ingestion and feature content.
- If the room skews technical, spend more time on Stages 3, 4, and 5 and let the SE take the lead on RDI, Redis Feature Form, latency, and train-serve parity.
- If the room is mixed, have the rep own the framing and close, and let the SE step in for the technical middle of the story.
## Closing reminder
Keep the close simple: the customer already has the data and the decisioning ambition. Redis is the context layer that makes those signals usable in the live moment so the business can improve higher yield, better ad relevance, stronger viewer retention."
Close the loop on context and real-time decisioning. End with a pilot-oriented ask tied to one segment, one workflow, and a clear KPI.
You already have the systems and the data. What you need is the layer that lets them act together in the live decision window. That is Redis.
## Anticipated objections
- Our ad server already ranks ads.
- Acknowledge the existing investment first. Then explain that Redis is additive: the current system stays in place, and Redis becomes the low-latency context and decisioning layer on top of it.
- This sounds like another personalization layer.
- Tie the answer back to the architecture. The existing tool or process may do part of the job, but the gap is bringing history, live state, policy, and low-latency serving together in one decision path.
- How do policy and pacing stay under control?
- Make it clear that policy remains upstream and explicit. Redis executes the approved rules, audit trail, and model versions in real time; it does not replace governance.
## Pacing guidance
- Total runtime: 12 to 16 minutes end to end. Budget roughly 60 to 90 seconds per stage, with a little more time on Stages 1, 4, 7, and 9.
- Pacing Guide
- Stage 1: 90 to 120 seconds. Orient the room and establish the additive architecture pattern.
- Stage 2: 60 seconds. Introduce the person and the stakes.
- Stage 3: 60 to 90 seconds. Keep it light for business audiences, deeper for technical audiences.
- Stage 4: 90 to 120 seconds. Slow down. This is where the contextual-intelligence story lands.
- Stage 5: 60 to 75 seconds. Go deeper only if the room wants ML detail.
- Stage 6: 75 to 90 seconds. Walk the winner, then contrast the alternatives.
- Stage 7: 90 to 120 seconds. Translate the demo into business math.
- Stage 8: 60 to 90 seconds. Let the visual comparison land.
- Stage 9: 90 to 120 seconds. Recap and close on the pilot ask.
## Audience calibration
- If the room skews executive, spend more time on Stages 1, 7, and 9 and compress the detailed ingestion and feature content.
- If the room skews technical, spend more time on Stages 3, 4, and 5 and let the SE take the lead on RDI, Redis Feature Form, latency, and train-serve parity.
- If the room is mixed, have the rep own the framing and close, and let the SE step in for the technical middle of the story.
## Closing reminder
Keep the close simple: the customer already has the data and the decisioning ambition. Redis is the context layer that makes those signals usable in the live moment so the business can improve higher yield, better ad relevance, stronger viewer retention.
## Objections handling
- We already have the data or the model.
- Great — the point of this demo is not that the data or the model is missing. The point is that Redis makes them operational in the live decision path.
- Why Redis instead of just another cache?
- Because the story here is not page acceleration. It is live context assembly, feature serving, and low-latency decisioning across systems that already exist.
- Do we have to replace our existing systems?
- No. The systems of record stay in place. Redis sits in the operational path so those systems can act together in real time.
## Pacing guidance
- Spend extra time on Stage 1, the context stage, the decision stage, and the business impact stage with executive audiences.
- Spend extra time on ingest, context, and feature serving with technical audiences.
- In a mixed room, let the rep own the business stakes and let the SE translate how the architecture makes the decision possible inside the latency budget.