Document Type: Specification
Status: Active
Version: v1.0
Authority: HeadOffice
Applies To: All HeadOffice-level decision surfaces using attribution and performance intelligence
Parent: HeadOffice Brain
Last Reviewed: 2026-04-26
Purpose
The HeadOffice Attribution Decision Engine Dashboard Specification defines the structure of the central control interface used to monitor, interpret, and act on attribution-driven intelligence across MWMS.
This dashboard is not a reporting surface.
It is a decision control surface.
Its purpose is to:
- unify cross-brain attribution signals
- display decision-relevant insights
- highlight optimisation opportunities
- surface risks and conflicts
- enable rapid and controlled decision-making
Core Principle
The dashboard must prioritise decisions over data.
Data without decision context creates noise.
The system must present:
→ what is happening
→ what it means
→ what should be done
Dashboard Architecture
The dashboard is structured into five core panels:
- Attribution Intelligence Overview
- Channel Role And Contribution Panel
- Opportunity And Scaling Signals Panel
- Risk And Constraint Panel
- Decision Action Panel
Each panel must support real-time or near-real-time interpretation.
🔴 Attribution Intelligence Overview Panel
Purpose:
Provide a unified view of attribution performance across all channels.
Displays:
- total attributed revenue
- channel contribution breakdown
- attribution confidence levels
- macro vs micro conversion distribution
- trend over time
Must highlight:
- shifts in contribution patterns
- inconsistencies across channels
- sudden performance changes
🔴 Channel Role And Contribution Panel
Purpose:
Show how each channel contributes to the journey.
Displays:
- channel role classification (awareness, consideration, conversion)
- attributed value by channel role
- position in journey
- assist vs final conversion contribution
Must prevent:
- direct channel comparison without context
- overvaluation of end-of-journey channels
🔴 Opportunity And Scaling Signals Panel
Purpose:
Identify where growth can be increased.
Displays:
- high-performing journeys
- high-value touchpoint sequences
- under-scaled channels
- emerging opportunity signals
- conversion path improvements
Must highlight:
- where to scale
- where to test further
- where performance is improving
🔴 Risk And Constraint Panel
Purpose:
Surface financial and operational risk.
Displays:
- attribution confidence levels
- signal reliability indicators
- capital allocation constraints
- CAC vs LTV signals
- volatility indicators
- channel dependency risks
Must highlight:
- high-risk decisions
- unstable performance areas
- over-reliance on specific channels
🔴 Decision Action Panel
Purpose:
Translate insights into actions.
Displays:
- recommended budget shifts
- scaling decisions
- test recommendations
- partner adjustments
- UX or journey interventions
Each action must include:
- rationale
- confidence level
- expected impact
🔴 Decision Confidence Layer
All insights and actions must include:
- confidence level
- signal strength
- data completeness
Confidence must be visualised clearly.
Low confidence must reduce decision weight.
🔴 Cross Brain Integration Layer
The dashboard must integrate signals from:
- Data Brain (event and attribution signals)
- Ads Brain (channel performance)
- Affiliate Brain (partner value)
- Customer Brain (journey behaviour)
- Finance Brain (capital constraints)
No panel operates in isolation.
🔴 Time And Lag Visualization Layer
The dashboard must display:
- time to conversion
- lag between interactions
- delayed conversion impact
This prevents premature optimisation decisions.
🔴 Attribution Conflict Detection Layer
The dashboard must detect:
- conflicting attribution signals across systems
- discrepancies between platforms
- inconsistent channel reporting
Conflicts must be flagged for review.
🔴 User Segmentation Layer
The dashboard must allow segmentation by:
- traffic source
- behavioural stage
- device
- campaign type
- customer type
Segmentation must update all panels dynamically.
🔴 Alert And Trigger System
The dashboard must generate alerts when:
- performance drops suddenly
- attribution confidence decreases
- channel dependency increases
- CAC exceeds acceptable range
- LTV signals weaken
Alerts must trigger investigation or action.
UI Behaviour Rules
The dashboard must:
- prioritise clarity over density
- highlight actions over metrics
- use consistent visual language
- minimise cognitive load
- allow rapid scanning
Avoid:
- clutter
- excessive metric display
- ambiguous signals
Failure Modes Prevented
- decision paralysis due to data overload
- misinterpretation of attribution
- delayed response to performance shifts
- conflicting optimisation across brains
- scaling based on incomplete data
Drift Protection
The system must prevent:
- dashboard becoming reporting-only
- panels drifting from decision relevance
- inconsistent interpretation across views
- overloading dashboard with low-value metrics
Architectural Intent
The HeadOffice Attribution Decision Engine Dashboard ensures MWMS operates with:
→ centralised decision control
→ aligned cross-brain intelligence
→ rapid response capability
It transforms MWMS into:
→ a controlled performance system
Final Rule
If the dashboard does not drive decisions:
→ it is not fulfilling its purpose
Change Log
Version: v1.0
Date: 2026-04-26
Author: HeadOffice
Change:
Created dashboard specification for attribution-driven decision control.
Introduces:
- panel-based decision architecture
- cross-brain signal integration
- decision action layer
- confidence-based interpretation
Change Impact Declaration
Pages Created:
HeadOffice Attribution Decision Engine Dashboard Specification
Pages Updated:
None
Pages Deprecated:
None
Registries Requiring Update:
HeadOffice Page Registry
MWMS Architecture Registry
Canon Version Update Required:
No
Change Log Entry Required:
Yes
End of Specification