Telecom providers already have billing data, product eligibility, app behavior, service quality signals, and care history. The problem is legacy decisioning can't assemble all of that fast enough while the churn moment is still live — so the offer arrives after the customer has already decided. What I'm going to show you is a save decision that fires while the moment is still actionable.
This demo shows the alternative. Redis Iris becomes the context engine that brings together live and durable context so the provider can make the right retention decision in the app, in care, or in any digital channel while the customer is still deciding.
In most telecom environments, the customer data already exists across billing, CRM, service assurance, digital events, feature pipelines, and care systems. The issue is that legacy vendor-managed decisioning systems like Pega sit in the middle and try to turn all of that into a retention action, but they do it with static rules, fragmented context, and too much latency. What Redis changes is that it becomes the operational context layer underneath the decision. RDI keeps operational systems synchronized, Redis FeatureForm serves online features, Redis RAM handles the hot path, Redis Flex holds broader customer history and embeddings, and the Redis Context Retriever assembles the Subscriber 360 — account state, usage history, and churn signals — and exposes it as structured tools for the decision engine. The decisioning stack can finally act while the moment is still live.
Do not rush. This is the stage where you establish that Redis is not being presented as a UI accelerator. It is the layer that makes the retention decision possible.
“Now we move from architecture to the actual business moment. Ava Collins has not canceled yet, but the churn moment is already visible. She has plan comparison behavior, recurring service-quality pain, and digital signals that say she is evaluating alternatives. This is exactly where legacy systems struggle. They may know she is at risk in a general sense, but they cannot assemble the full live picture and act fast enough while Ava is still in-session. This is the moment we are trying to save.”
This stage should feel like a real customer moment. Make it human, not analytical.
“Here is where the architecture becomes practical. Billing, CRM, network telemetry, digital engagement streams, and feature pipelines all stay where they are. Redis Data Integration keeps operational data synchronized into the live serving path, and Redis FeatureForm makes the online features available with train-serve parity. So this is not a rip-and-replace of the source systems. It is a replacement of the slow, fragmented decisioning layer that sits on top of them today.”
Make sure you say clearly: systems of record stay in place.
Retention is never just about a churn score. Ava’s historical context tells us who she is over time — product mix, household economics, prior cases, prior offers, and long-term behavior. The live context tells us what is happening right now — service degradation, app behavior, comparison actions, and session timing. Legacy systems tend to be good at fragments of this picture. Redis brings the full operational context together in one path so the next action is based on the complete moment, not a partial snapshot.
Redis Context Retriever assembles the Subscriber 360 — account state, usage history, and churn signals — so the decision engine has exactly the live context it needs.
This is one of the most important stages. Rehearse this until it sounds simple and natural.
“A lot of teams think the hard part is model training. In practice, the hard part is feature serving at decision time. If the retention stack cannot get the right features in milliseconds, the decision either slows down or falls back to static rules. Redis FeatureForm on top of Redis solves that by serving the same feature definitions used for training, but now in the live decision path. That is what lets the provider move from retrospective analytics to real-time action.”
Keep this stage practical. Do not over-explain modeling. Explain why features must be online.
“Now we get to the decision itself. The system is not just asking whether Ava is at risk. It is asking what action has the highest probability of keeping her while also making business sense. In this case, the winning action is plan optimization plus device trade-in credit. Why? Because the full context says that price pressure, service frustration, and upgrade timing are all part of the same moment. A generic offer would miss that. A Redis-powered decision can see it and rank the action that best fits the live context.”
Always answer the implicit audience question: why did this action win?
“This is where we connect the technical story back to operator value. If you catch the customer while the churn moment is still live, you improve save rate, lower voluntary churn, and create more efficient retention spend because the offer is more targeted. The key point is that the uplift does not come from showing more offers. It comes from making a better decision earlier in the moment.”
Do not turn this into a KPI dump. Keep it causal: better context leads to better decisions, which leads to better outcomes.
“The visual surface looks familiar, but the decisioning underneath it is completely different. On the legacy side, the experience is driven by slower, vendor-managed logic and generic treatment paths. On the Redis-powered side, the same channel can react to the full context of the moment and surface the action most likely to retain Ava. This is the simplest way to see the difference between the old model and the new one.”
Keep this clean and visual. Do not re-explain the whole architecture here.
“Let’s end where we started. The problem we are solving is not that telecom providers lack data. The problem is that legacy systems like Pega and similar decisioning platforms cannot assemble and act on that data fast enough in the live churn moment. Redis changes that by becoming the operational context layer for real-time decisioning. The sources stay in place, the features stay online, the hot and warm customer context stay available, and the decisioning stack can finally respond while the customer is still deciding.”
We already use Pega or another NBA platform
“That is the point of the story. Systems like Pega represent the old operating model — vendor-managed, rules-heavy, and not designed to assemble full live context in a sub-10 millisecond decision path. Redis is not trying to be another campaign tool. It is the operational context layer that makes the replacement architecture possible.”
We already have churn models
“A churn model tells you who is risky. It does not solve the live decisioning problem by itself. You still need the operational layer that can assemble context, serve features, and rank the next action while the session is live.”
We already have the data in our lakehouse
“Exactly. The challenge is not whether the data exists. The challenge is whether you can operationalize it fast enough in the customer moment. That is where Redis comes in.”
Read the “Say this exactly” sections out loud until they sound natural. Do not memorize every word. The goal is to internalize the sequence: explain the problem, show the live customer moment, show how context is assembled, show how the decision is made, connect it to business value, and close on the operating model shift.