Goal-Driven AI Decision System Architecture

DATA SOURCES

Current Need (Spend)Store Analytics

Goals & Budgets• Goals: AIP • Budgets: Spends • Netcore Attributes • Net Core Campaign • (Response = OFFER)

Customer Query• Recursive Data • C11 HISTORY • CPaaS Data

DMC Status• Media UTM • Branch (Adobe)

ETL PIPELINE

IngestionKafka/Event Hub Real-time Streaming

Raw ZoneBronze Layer

ETL ProcessingClean & Validate Deduplicate Transform

Curated ZoneSilver Layer

Feature EngRFM, Aggregations Identity Stitching

Feature StoreGold Layer ML-Ready Features

AI DECISIONINGLearning Loop

Personalization:• Judge → MBA • Plot line → TUSKPusher Product Channel→ Segment → Governance → Prediction → Reporting Recomm Lower Funnel → No effect Consequence → Learning Loop

Brag + Campaign Automation

ReportRevenue: • AIP $ • Media Eff $ • CPA $ Cost: • ATS $ • OA $ • ENGAGEMENT $

GOVERNANCEWho Targeted (Segment)Offer Channel PartnerDecision tracking & audit

Key Flow: Raw Data → ETL Pipeline (Clean, Transform, Feature Engineering) → Feature Store → AI Decisioning (Learning Loop) → Personalized Actions → Campaign Execution → Reports → Governance

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