Redis Real-Time Decisioning Demo | E-commerce Checkout Decisioning

Stage 1: The Architecture

Northfield Commerce's Real-Time Decisioning Stack

Five tiers form a continuous loop: ingest, context, decide, act, learn. Redis lets the retailer decide margin-aware promotions and risk-aware checkout actions in one pass. Systems of record stay where they are. Redis becomes the operational context layer that makes the moment actionable.

Data Sources

Commerce Platform

Operational source

Order Management System

Operational source

Customer Data Platform

Operational source

Fraud Service

Operational source

Inventory / Fulfillment APIs

Operational source

Kafka / Clickstream

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 cart state, active session signals, live inventory availability, and payment readiness

Redis Flex

Warm purchase history, browse embeddings, household context, and prior checkout patterns

Feature Store

Fraud risk, conversion propensity, margin optimization, and offer eligibility features

Redis Context Retriever

Assembles the Shopper 360 — cart state, purchase history, and checkout policy — 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)

Web Checkout

Customer-facing activation surface

Mobile App

Customer-facing activation surface

Store Associate Tablet

Customer-facing activation surface

Email / Push

Customer-facing activation surface

↺

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

Decision latency

9.8 ms

Checkout conversion lift

+9 pts

Margin protection

+$6.70/order

Stage 2: Trigger Event

Priya Shah creates a decision moment

A shopper reaches checkout with a high-value cart and mixed fulfillment options. Choose the right promotion, fraud posture, and fulfillment promise without slowing conversion.

Live Trigger

Priya Shah

Loyalty member | $286 cart | mixed apparel + home goods

RETAIL

Eventcheckout_started

Customer surfaceWeb checkout

Decision objectiveConversion + margin

Decision windowBefore the surface finishes rendering

Context requiredHistory + live signals + policy

Business stakes+9 pts

Why This Moment Matters

Redis lets the retailer decide margin-aware promotions and risk-aware checkout actions in one pass.

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 retail 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

SHOP

Shopify / CommerceTools

Common repository or streaming source for shopify context

→

OMS

Common repository or streaming source for oms context

→

LOYA

Loyalty platform

Common repository or streaming source for loyalty context

→

FRAU

Fraud and payment APIs

Common repository or streaming source for fraud context

→

KAFK

Kafka / Redis Streams

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

→

FULF

Fulfillment network APIs

Common repository or streaming source for fulfillment 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, shopper or cart 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 bandsame-day partial

Tenure / relationship depthEstablished

Prior interaction patternKnown baseline available

Eligibility stateResolved in memory

Policy constraintsCurrent and active

Action historyFrequency caps enforced

Live Context

Current intentConversion + margin

Streaming signal stateFresh for this session

Capacity / inventoryAvailable

Risk / complianceWithin active rules

Surface readinessWeb checkout

Decision scopeFree expedited shipping with no discount

Key insight: Redis Context Retriever surfaces the Shopper 360 — cart state, purchase history, and checkout policy — via MCP tools the decision engine calls directly. 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

cart_value

Business value weighting for this moment.

$2860.3 ms

fraud_risk

Risk and policy factor used to gate the action.

low0.4 ms

inventory_fit

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

strong0.5 ms

shipping_eligibility

Eligibility and readiness signal.

same-day partial0.6 ms

promo_elasticity

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

high0.7 ms

customer_ltv_band

Business value weighting for this moment.

gold0.8 ms

Feature Serving Performance

Features Hydrated

186

P99 Lookup Latency

<12ms

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

Free expedited shipping with no discount

Best conversion lift with minimal margin loss

NBA score0.94

#2 Candidate

ALTERNATIVE

10% basket coupon

Higher conversion but lower contribution margin

NBA score0.79

Suppressed

POLICY

Buy-now-pay-later prompt

Suppressed because fraud and basket mix do not warrant it

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 latency9.8 ms

Checkout conversion lift+9 pts

Margin protection+$6.70/order

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

Priya Shah

Retail workflow

What the system knows

Partial profile, delayed retrieval, and limited live context.

Action shown

10% basket coupon

Outcome risk

The action fits broadly, but not the full moment.

Lower

relevance

Higher

fallback use

Siloed

context

Redis-Powered Experience

Priya Shah

Decision-ready profile

#1 Next Best Action

Free expedited shipping with no discount

Best conversion lift with minimal margin loss

Why it wins

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

Visible impact

Checkout conversion lift: +9 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

Commerce Platform

Operational source

Order Management System

Operational source

Customer Data Platform

Operational source

Fraud Service

Operational source

Inventory / Fulfillment APIs

Operational source

Kafka / Clickstream

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 cart state, active session signals, live inventory availability, and payment readiness

Redis Flex

Warm purchase history, browse embeddings, household context, and prior checkout patterns

Feature Store

Fraud risk, conversion propensity, margin optimization, and offer eligibility features

Redis Context Retriever

Assembles the Shopper 360 — cart state, purchase history, and checkout policy — 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

Web Checkout

Customer-facing activation surface

Mobile App

Customer-facing activation surface

Store Associate Tablet

Customer-facing activation surface

Email / Push

Customer-facing activation surface

↺

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

Decision latency

9.8 ms

Checkout conversion lift

+9 pts

Margin protection

+$6.70/order