Enterprise Affinity & Engagement Architecture
Designed and implemented an enterprise-scale behavioral intelligence architecture within Salesforce Salesforce Marketing Cloud to transform fragmented campaign engagement into reusable, actionable first-party intelligence across 35+ brands.
The framework operationalized affinity capture, engagement enrichment, lifecycle segmentation, and scalable personalization infrastructure—creating a reusable CRM intelligence layer that powered both always-on automation and future downstream activation use cases.
Executive Summary
At Diageo North America, CRM data existed primarily as isolated campaign activity tied to individual sends and disconnected brand programs. Subscriber behavior was not being consistently translated into reusable intelligence.
I architected and implemented a scalable Affinity + Engagement data framework that standardized how behavioral signals were captured, stored, enriched, and operationalized across the SFMC ecosystem.
The solution introduced:
- centralized affinity data structures
- engagement intelligence tables
- reusable segmentation architecture
- automated nightly enrichment processes
- lifecycle-aware behavioral tracking
- modular personalization readiness
This transformed CRM from a channel used primarily for outbound campaigns into a continuously learning behavioral intelligence system.
The Business Problem
The existing CRM ecosystem faced several structural limitations:
Fragmented Behavioral Data
Engagement signals lived inside individual journeys or campaign reporting and were rarely reusable.
No Unified Intelligence Layer
Brands operated independently without a scalable framework for affinity capture or behavioral enrichment.
Personalization Was Operationally Expensive
Each audience or personalized send required manual segmentation logic and duplicated effort.
Lifecycle Journeys Could Not Learn
Journeys sent emails, but subscriber behavior was not systematically feeding future targeting or content decisions.
Scaling Across 35+ Brands Was Unsustainable
The organization needed a reusable architecture capable of supporting both enterprise governance and brand-level flexibility.
My Role
Lead CRM Architect / SFMC Product Lead
Owned:
- enterprise data modeling strategy
- SFMC architecture design
- automation framework design
- affinity taxonomy structure
- segmentation logic
- SQL enrichment processes
- CloudPage behavioral capture strategy
- operational deployment model
- lifecycle intelligence framework
Worked cross-functionally with:
- CDP teams
- data engineering
- CRM operations
- activation teams
- global platform stakeholders
- external agencies
Architecture Overview
Core Framework Components
1. Welcome Entry Layer
Brand-level welcome tables captured acquisition source and entry metadata.
Examples:
- {Brand}_Welcome
- API-driven acquisition sources
- multi-entry handling using composite keys
2. Master Sendable Layer
Standardized sendable records across brands.
Examples:
- Master_Sendable_{Brand}
Contained:
- subscriber identifiers
- consent status
- send eligibility
- core profile attributes
3. Affinity Intelligence Layer
Centralized reusable behavioral indicators.
Examples:
- {Brand}_Affinity
Tracked:
- product preferences
- flavor affinities
- lifestyle interests
- occasion signals
- sports/music/culture interests
- premiumization indicators
- engagement-derived behavioral categories
Examples of affinity fields:
- Affinity_Recipes
- Affinity_Sports
- Affinity_Hosting
- Affinity_Events
- Affinity_Futbol
- Variant_Blanco
- Variant_1942
4. Engagement Intelligence Layer
Persistent engagement scoring and behavioral activity tracking.
Examples:
- {Brand}_Engagement
Tracked:
- opens
- clicks
- recency
- engagement velocity
- behavioral enrichment
- interaction timestamps
- source attribution
5. Automation & Enrichment Layer
Nightly automation framework:
- synchronized new entries
- enriched affinity tables
- updated engagement states
- standardized segmentation logic
- maintained reusable intelligence
Built using:
- SQL automations
- Journey Builder update activities
- AMPscript data writes
- API integrations
- CloudPage submissions
Technical Strategy
Scalable Behavioral Modeling
The architecture intentionally separated:
- sendable identity
- behavioral intelligence
- engagement activity
This prevented:
- bloated sendable models
- duplicated engagement logic
- journey-specific silos
It also enabled:
- reusable segmentation
- modular personalization
- scalable reporting
- future CDP integration
Enterprise Personalization Readiness
The framework was designed to support:
- dynamic content rotation
- affinity-driven messaging
- journey branching
- behavioral suppression logic
- audience modeling
- paid media enrichment
Without requiring brands to rebuild segmentation logic repeatedly.
Operational Flexibility
The system balanced:
- enterprise governance
- local brand autonomy
- reusable architecture
- scalable deployment velocity
This allowed:
- smaller brands to onboard quickly
- large brands to support more complex personalization
- CRM operations to scale without equivalent headcount growth
Outcomes & Impact
Enterprise Scale
Supported:
- 35+ brands
- millions of sends
- reusable lifecycle infrastructure
- ongoing affinity capture across campaigns and journeys
Net-New Behavioral Intelligence
Generated hundreds of thousands of reusable affinity signals through lifecycle engagement programs.
Created a persistent intelligence layer instead of one-time campaign reporting.
Operational Efficiency
Reduced manual segmentation overhead through:
- reusable DE structures
- standardized enrichment logic
- modular automation frameworks
Enabled faster deployment timelines and reduced dependency on one-off builds.
Personalization Enablement
Created the foundational infrastructure for:
- affinity-based personalization
- dynamic content systems
- behavioral audience segmentation
- downstream CDP activation
- future paid media lookalike modeling
Why This Matters
Most CRM organizations operate campaign systems.
This architecture transformed CRM into an intelligence system.
Instead of treating engagement as temporary reporting data, the framework operationalized behavioral signals into reusable enterprise assets that continuously improved targeting, personalization, and lifecycle orchestration over time.
That shift enabled CRM to function not simply as outbound marketing infrastructure—but as a scalable behavioral intelligence engine.