Why Can We Provide Real-time Data Capabilities to Agency AI?
In retail consumer goods, SocialHub connects API, SDK, POS, e-commerce and third-party platform data in real time, turning consumers, products, orders, stores, members and marketing touchpoints into governed customer intelligence.
Real-time architecture turns retail data into customer intelligence.
The architecture keeps system responsibilities explicit: CRM OLTP protects retail business truth, change capture and event gateways feed the event bus, stream processing prepares governed facts, and the MPP database serves analytics as the final data layer.
Reliable intelligence starts with complete retail signal coverage.
The architecture ingests state data, event data, business state and behavior signals from the systems where consumer retail work actually happens.
Business facts
members, orders, points, coupons, products, stores, inventory, payments and returns
POS transactions
store purchases, receipts, refunds, basket lines, cashier/store context and return reasons
E-commerce interfaces
Shopify, Amazon, TikTok Shop, marketplace orders, fulfillment, returns and subscriptions
Product & SKU master
SKU, SPU, category, brand, price, attributes, bundles, merchandising hierarchy and availability
Behavior events
page views, product views, search, clicks, add-to-cart, login, scan, content and conversion events
Customer service
tickets, chat, complaints, refund context, satisfaction, resolution outcomes and service recovery signals
Third-party platforms
ERP, ads, audiences, external membership, attribution, ROAS, logistics and service integrations
Decision feedback
recommendations, approvals, generated actions, guardrail decisions and outcome signals
CRM OLTP keeps retail transactions reliable.
Member, order, point, coupon, product and store records are written to the transactional store first, then exposed to the streaming backbone through change capture.
Stream processing stays scoped to real-time computation.
Cleaning, deduplication, windows, real-time tags, cohorts and feature-ready facts are computed as stream processing outputs.
Analytics serving is the final data layer.
The MPP database serves customer intelligence, cohorts, event detail and governed metrics as the final data-serving layer.
From data movement to decision movement.
The stack is intentionally split into transactional store, event bus, stream processing and OLAP serving. Each layer has a clear boundary and a clear failure domain.
A consumer signal becomes an action before it goes cold.
Business facts from POS, e-commerce, ERP and CRM APIs enter API Access and Data Router, then land in the CRM OLTP database for transactional consistency.
Change capture publishes member, order, coupon, product and inventory changes into the event bus.
Behavior events enter the event gateway, where they are authenticated, validated, enriched and written to the event bus.
The event bus unifies business change streams and behavior events while preserving replay, fan-out, isolation and consumer decoupling.
Stream processing performs cleaning, deduplication, windowing, tag computation, cohort updates and feature generation.
MPP Database serves event detail, transaction facts, customer 360, product affinity, store performance, coupon attribution, lifecycle cohorts, RFM, LTV and repurchase analytics.
The components are familiar. The contract is sharper.
CRM OLTP
Transactional storeThe OLTP store keeps members, orders, points, coupons, products, stores and inventory with consistency, auditability and operational control.
CDC
Database change captureCDC publishes business changes into the event bus; it does not receive raw API events directly.
Event Bus
Real-time event busBusiness changes and behavior events are buffered, replayed, isolated by topic and distributed to processing and serving consumers.
Stream Processing
Real-time stream processingCleans, dedupes, joins, computes windows, tags, cohorts, intent signals and feature-ready outputs.
MPP Database
Real-time OLAP servingServes customer 360, behavior detail, transaction facts, product affinity, cohorts, BI, attribution and lifecycle analytics at low latency.
Build against governed customer intelligence, not raw pipes.
Use APIs to connect retail systems, SDKs to capture behavior, change capture to stream business facts, and the MPP database to serve governed customer intelligence.
Related: the platform loop and web tracking SDK.