Redis Real-Time Decisioning Demo | Telecom Retention Orchestration

Stage 1: The Architecture

Summit Mobile's Real-Time Decisioning Stack

Five tiers form a continuous loop: ingest, context, decide, act, learn. The winning action depends on service quality, household economics, and recent engagement — all in the same response path. Systems of record stay where they are. Redis becomes the operational context layer that makes the moment actionable.

Data Sources

CRM / Billing Platform

Operational source

Care Interaction Store

Operational source

Network Diagnostics APIs

Operational source

Device Intelligence

Operational source

Offer Catalog

Operational source

Kafka / App Events

Operational source

Ingest Layer

Redis Data Integration (RDI)

CDC from repositories and operational databases with sub-second activation

Redis Feature Form + Streams

Feature serving and live event hydration from Kafka, Redis Streams, and industry APIs

Unified Context Layer

Redis RAM

Hot service session, active network quality signals, live churn triggers, and offer eligibility state

Redis Flex

Warm usage history, service quality patterns, interaction embeddings, and prior save offer outcomes

Feature Store

Churn propensity, save offer fit, ARPU impact, and network satisfaction features

Redis Context Retriever

Assembles the Subscriber 360 — account state, usage history, and churn signals — and exposes it as structured MCP tools for the decision engine

Decision Engine

Eligibility Rules

Policy, compliance, and inventory constraints

NBA Ranker

Contextual ranking weighted by value, risk, and fit

Policy Arbitration

One action selected for the current moment

Output Channels (Act)

Mobile App

Customer-facing activation surface

Agent Desktop

Customer-facing activation surface

Retail POS

Customer-facing activation surface

Push / SMS

Customer-facing activation surface

↺

Learn: Decision logs and outcomes stream back into model training, policy updates, and the Redis context layer.

Decision latency

7.9 ms

Retention lift

+21 pts

ARPU protection

+$27/mo

Stage 2: Trigger Event

Ava Collins creates a decision moment

A subscriber opens the app after a plan comparison search and a spike in dropped-call complaints. Decide whether to save, upgrade, troubleshoot, or escalate before the customer churns out.

Live Trigger

Ava Collins

3 lines | 5-year tenure | recent competitor plan research

TELCO

Eventretention_signal_detected

Customer surfaceMobile app

Decision objectiveSave + expansion

Decision windowBefore the surface finishes rendering

Context requiredHistory + live signals + policy

Business stakes+21 pts

Why This Moment Matters

The winning action depends on service quality, household economics, and recent engagement — all in the same response path.

Without Redis: the application waits on siloed repositories and defaults to a generic next step.

With Redis: the decision surface opens with ranked, contextual action already staged for the moment.

Stage 3: Ingest

Industry repositories and streams flow into Redis

These are common repositories and streaming APIs for telecom decisioning. They remain the systems of record. Redis activates the working set needed to decide now.

Redis Data Integration (RDI)Redis Feature Form

Source Systems → Redis

AMDO

Amdocs / Siebel

Common repository or streaming source for amdocs context

→

KAFK

Kafka / Redis Streams

Event streaming via Kafka or Redis Streams — no separate broker required with Redis Streams built-in

→

COVE

Coverage APIs

Common repository or streaming source for coverage context

→

HAND

Handset telemetry

Common repository or streaming source for handset context

→

DATA

Databricks

Common repository or streaming source for databricks context

→

OFFE

Offer services

Common repository or streaming source for offer context

→

Ingest Pipeline Status

CDC modeReal-time

Streaming lag<100 ms

Feature parityTrain = serve

Custom sync code0 lines

Serving roleOperational context layer

Stage 4: Context Assembly

History and live state converge into one working view

Redis assembles profile history, account or household state, live intent, and policy constraints in one low-latency lookup path. What looked like a simple event in Stage 2 becomes a much richer decision moment here.

Redis RAMRedis FlexRedis Context Retriever

Historical Context

Customer value bandopen

Tenure / relationship depthEstablished

Prior interaction patternKnown baseline available

Eligibility stateResolved in memory

Policy constraintsCurrent and active

Action historyFrequency caps enforced

Live Context

Current intentSave + expansion

Streaming signal stateFresh for this session

Capacity / inventoryAvailable

Risk / complianceWithin active rules

Surface readinessMobile app

Decision scopePlan optimization + device trade-in credit

Key insight: Redis Context Retriever assembles the Subscriber 360 — account state, usage history, and churn signals — so the decision engine has exactly what it needs. The winning action becomes obvious only when historical context and live signals appear in the same response path.

Stage 5: Feature Serving

Online features hydrate the ranker in milliseconds

Redis Feature Form serves model-ready signals from Redis with train-serve parity. No fan-out calls to downstream systems during decisioning. No stale fallbacks.

Redis Feature FormRedis Feature Store

churn_risk

Risk and policy factor used to gate the action.

high0.3 ms

network_score

Model or ranking signal pulled online at decision time.

mixed0.4 ms

price_sensitivity

Real-time feature served from Redis with train-serve parity.

elevated0.5 ms

device_upgrade_eligibility

Eligibility and readiness signal.

open0.6 ms

household_margin_band

Business value weighting for this moment.

healthy0.7 ms

offer_fatigue

Real-time feature served from Redis with train-serve parity.

clear0.8 ms

Feature Serving Performance

Features Hydrated

186

P99 Lookup Latency

<10ms

Train / Serve Parity

100%

Stage 6: Ranking

Candidate actions are ranked for this moment

Rules, policies, vector similarity, and business weighting combine to rank the best action now. The winner is selected because it fits the moment, not because it is the easiest generic fallback.

Redis SearchNBA RankerRules Engine

#1 Winner

PRIMARY ACTION

Plan optimization + device trade-in credit

Addresses price sensitivity without over-discounting

NBA score0.94

#2 Candidate

ALTERNATIVE

Flat bill credit

Immediate relief but weak long-term retention economics

NBA score0.79

Suppressed

POLICY

Add-a-line campaign

Suppressed because churn risk is still unresolved

Suppressed0.24

Stage 7: Business Impact

The value of choosing the right action now

Fast decisions matter, but the real value is choosing the right action while the moment is still open. Redis improves both latency and outcome quality.

Decision Economics

Decision latency7.9 ms

Retention lift+21 pts

ARPU protection+$27/mo

The key insight: value compounds when the decision surface uses live operational context instead of generic fallback logic.

Moment Outcome

Generic

Siloed systems
lower relevance

→

Redis

Context-aware
higher impact

Stage 8: Outcome

Same surface. Different decision layer.

On the left is a generic or delayed path. On the right is the Redis-powered experience with ranked action already staged. Same customer moment. Different outcome.

Generic Experience

Ava Collins

Telecom workflow

What the system knows

Partial profile, delayed retrieval, and limited live context.

Action shown

Flat bill credit

Outcome risk

The action fits broadly, but not the full moment.

Lower

relevance

Higher

fallback use

Siloed

context

Redis-Powered Experience

Ava Collins

Decision-ready profile

#1 Next Best Action

Plan optimization + device trade-in credit

Addresses price sensitivity without over-discounting

Why it wins

Combines history, live signals, business value, and active policy constraints in the same low-latency response.

Visible impact

Retention lift: +21 pts.

High

relevance

Low

fallback use

Ready

before render

Stage 9: The Architecture, Proven

Architecture first. Architecture last. Outcome in the middle.

The same five tiers you saw at the start now tie directly to a measurable decision outcome. Common repositories and streaming APIs stay in place. Redis remains the operational decisioning layer that makes the customer moment work.

Data Sources

CRM / Billing Platform

Operational source

Care Interaction Store

Operational source

Network Diagnostics APIs

Operational source

Device Intelligence

Operational source

Offer Catalog

Operational source

Kafka / App Events

Operational source

Ingest Layer

RDI

CDC and activation from systems of record

Streams + Features

Live event hydration and model parity

Unified Context Layer

Redis RAM

Hot service session, active network quality signals, live churn triggers, and offer eligibility state

Redis Flex

Warm usage history, service quality patterns, interaction embeddings, and prior save offer outcomes

Feature Store

Churn propensity, save offer fit, ARPU impact, and network satisfaction features

Redis Context Retriever

Assembles the Subscriber 360 — account state, usage history, and churn signals — and exposes it as structured MCP tools for the decision engine

Decision Engine

Rules

Eligibility, compliance, and policy

Ranker

Propensity, value, and fit

Arbitration

One action selected now

Output Channels

Mobile App

Customer-facing activation surface

Agent Desktop

Customer-facing activation surface

Retail POS

Customer-facing activation surface

Push / SMS

Customer-facing activation surface

↺

Learn: Outcomes feed retraining, policy updates, and future decisions without changing the systems of record.

Decision latency

7.9 ms

Retention lift

+21 pts

ARPU protection

+$27/mo