Document Type: Architecture
Status: Active
Version: v1.1
Authority: Affiliate Brain (Operational Intelligence Layer)
Applies To: Historical campaign analysis, cross-campaign pattern detection, and future testing intelligence
Parent: Affiliate Brain Canon
Linked Canon: MWMS Campaign Intelligence Archive v1.0
Last Reviewed: 2026-03-15
Purpose
The MWMS AI Campaign Intelligence Reviewer is a system designed to analyse historical campaign data stored in the Campaign Intelligence Archive.
Its role is to identify patterns, insights, and performance signals across campaigns.
The system transforms raw campaign results into actionable marketing intelligence.
Scope
This architecture applies to:
• historical campaign analysis across Affiliate Brain records
• pattern detection using archived campaign data
• hook, traffic source, and funnel performance review
• strategic intelligence generation for future campaign planning
• advisory insight production before new tests or scaling activity
This document defines the analysis layer that sits above the campaign archive and converts stored campaign outcomes into reusable intelligence.
It does not govern:
• live capital allocation
• direct campaign launch decisions
• velocity rulings
• test-structure approval by itself
• stage progression decisions by themselves
• direct override of Affiliate Brain governance
Those remain governed by the Velocity Decision Engine, Testing Definition Protocol, Stage Progression Protocol, and related Affiliate Brain governance systems.
Definition / Rules
Architectural Role
The AI Campaign Intelligence Reviewer acts as an analysis layer above the Campaign Intelligence Archive.
It reviews accumulated campaign records and extracts strategic insights.
These insights improve future decision making in:
• offer selection
• hook creation
• funnel structure
• traffic source allocation
• scaling decisions
Position in Affiliate Brain Flow
Offer Intake
↓
Offer Intelligence
↓
Market Context
↓
Tracking Governance
↓
Research Intelligence
↓
Authority & Narrative Intelligence
↓
Structural Signal Audit
↓
Intent Declaration
↓
Velocity Decision Engine
↓
Testing Definition Protocol
↓
Phase 4 – Structured Testing
↓
Stage Progression Protocol
↓
Controlled Scaling Protocol
↓
Campaign Review Protocol
↓
Campaign Intelligence Archive
↓
AI Campaign Intelligence Reviewer
Core Principle
Individual campaign results provide limited insight.
However, when multiple campaigns are analysed together, patterns emerge that reveal deeper truths about market behaviour.
The AI Campaign Intelligence Reviewer identifies these patterns.
Analysis Objectives
The system evaluates historical campaign data to detect:
• high-performing hook types
• high-performing traffic sources
• profitable funnel structures
• common failure patterns
• audience response trends
These insights inform future testing strategy.
Hook Performance Analysis
The reviewer analyses hook performance across campaigns.
Questions evaluated include:
• Which hook categories produce the highest CTR?
• Which hooks generate high-intent signals?
• Which hooks attract low-quality traffic?
• Which hooks consistently fail?
The system generates recommendations for future hook development.
Traffic Source Analysis
The system evaluates performance differences between traffic sources.
Example sources include:
• YouTube Ads
• Google Video Ads
• Google Display Ads
• Direct-to-VSL
• Advertorial funnels
The reviewer identifies which traffic sources produce the strongest signal ladder behaviour.
Funnel Performance Analysis
The reviewer analyses funnel performance patterns.
Questions evaluated include:
• Which funnel types generate the highest intent signals?
• Which funnel structures produce the lowest CPA?
• Where does funnel friction commonly occur?
The system identifies optimal funnel structures.
Failure Pattern Detection
The reviewer identifies repeated campaign failure patterns.
Examples:
• hooks with poor CTR
• landing pages with weak intent signals
• funnels with high drop-off rates
• offers with weak conversion behaviour
Identifying repeated failures prevents wasted future testing.
Pattern Discovery
When enough campaign data exists, the system may detect patterns such as:
• audiences responding strongly to specific emotional triggers
• certain hooks performing better on specific traffic sources
• funnel structures performing better for certain offer types
These discoveries refine future strategy.
Intelligence Output
The AI Campaign Intelligence Reviewer produces strategic insights including:
• hook recommendations
• traffic source recommendations
• funnel optimisation suggestions
• creative testing ideas
• offer selection insights
These insights guide the next generation of campaign experiments.
Review Frequency
The system should review campaign archive data:
• monthly
• quarterly
• before launching large campaign tests
• before scaling campaigns significantly
Drift Protection
The reviewer must not override Affiliate Brain capital allocation rules.
The reviewer provides advisory insights only.
Final campaign decisions remain governed by:
• Velocity Decision Engine
• Testing Definition Protocol
• Stage Progression Protocol
Architectural Intent
The AI Campaign Intelligence Reviewer converts historical campaign data into strategic intelligence.
Over time the system improves the effectiveness of MWMS affiliate marketing campaigns.
The longer the system operates, the more intelligent it becomes.
Final Rule
Historical campaign intelligence may inform future decisions, but it may not replace governed decision systems.
Pattern recognition supports judgement.
It does not replace governance.
Change Log
Version: v1.1
Date: 2026-03-15
Author: MWMS HeadOffice / Affiliate Brain
Change: Rebuilt page to align with the locked MWMS document standard for this cleanup pass. Preserved the original purpose, architectural role, flow position, analysis objectives, hook analysis, traffic analysis, funnel analysis, failure-pattern detection, pattern discovery, intelligence outputs, review frequency, drift protection, and architectural intent. Added Document Type, Applies To, Scope, Definition / Rules structure, Final Rule, and Last Reviewed format.
Version: v1.0
Date: 2026-03-07
Author: Affiliate Brain
Change: Initial creation of AI Campaign Intelligence Reviewer. Defined AI analysis layer for campaign archive data including hook analysis, traffic analysis, funnel evaluation, and pattern detection.
END – MWMS AI CAMPAIGN INTELLIGENCE REVIEWER v1.1