Document Type: Protocol
Status: Active
Authority: Data Brain
Parent: Data Brain Architecture
Applies To: All MWMS environments where attribution data is used for optimisation, reporting, or decision-making
Version: v1.0
Last Reviewed: 2026-04-23
Purpose
The Data Brain Attribution Validation Protocol defines the mandatory process for verifying the reliability of attribution data before it is used within MWMS.
This protocol ensures:
• attribution signals are understood
• platform discrepancies are identified
• attribution limitations are accounted for
• invalid attribution does not influence decisions
Attribution without validation produces misleading optimisation direction.
Core Principle
Attribution must be validated before it is trusted.
If attribution is not validated:
→ it must not be used for decision-making
Position in MWMS System
This protocol operates between:
• Data Brain Attribution Reliability Framework
• Data Brain Measurement Validation Protocol
• HeadOffice Data Decision Gate Framework
It determines:
👉 whether attribution signals are usable
👉 whether attribution confidence is acceptable
Attribution Validation Execution Flow
All attribution must follow this process:
Step 1 — Validate Conversion Integrity
Before attribution is evaluated, confirm:
• conversion events are correct
• no duplicate conversions exist
• no missing conversion events
• values are accurate
If conversion integrity fails:
→ attribution validation cannot proceed
Step 2 — Identify Attribution Sources
Determine all sources reporting attribution:
• GA4
• Google Ads
• other ad platforms
• backend / CRM systems
Understanding sources is required before comparison.
Step 3 — Compare Cross-Platform Attribution
Compare:
• conversion counts
• channel contribution
• campaign performance
Expected outcome:
• directional alignment, not exact match
Step 4 — Identify Discrepancies
Check for:
• missing conversions in one system
• inflated conversions in another
• channel contribution differences
• inconsistent campaign results
Step 5 — Diagnose Discrepancy Causes
Common causes:
• attribution window differences
• attribution model differences
• platform bias
• tracking gaps
• consent restrictions
• cross-device fragmentation
Step 6 — Validate Attribution Inputs
Confirm:
• UTMs structured correctly
• source/medium assigned correctly
• no unwanted referrals
• campaign naming consistent
Step 7 — Evaluate Attribution Model
Confirm:
• attribution model used (last-click, DDA, etc.)
• model limitations understood
• comparison across models where relevant
Step 8 — Assess Attribution Stability
Check:
• consistency over time
• no sudden unexplained shifts
• repeatability of contribution patterns
Step 9 — Assign Attribution Confidence Level
High Confidence Attribution
Conditions:
• consistent across platforms
• validated conversion tracking
• stable patterns over time
→ Safe for decision-making
Medium Confidence Attribution
Conditions:
• minor discrepancies
• known limitations
• directional alignment present
→ Use with caution
Low Confidence Attribution
Conditions:
• major discrepancies
• unstable patterns
• incomplete validation
→ Do not use for decisions
Invalid Attribution
Conditions:
• broken tracking
• duplicate conversions
• missing events
• severe platform conflicts
→ Must not be used
Attribution Approval Rule
Attribution is approved only when:
• conversion integrity confirmed
• discrepancies understood
• attribution model limitations acknowledged
• confidence level acceptable
If any condition fails:
→ attribution is not approved
Validation Triggers
Attribution validation must be performed when:
• new campaigns launched
• tracking changes made
• discrepancies detected
• scaling decisions planned
• audit identifies issues
Common Attribution Failure Patterns
This protocol detects:
• GA4 vs Ads mismatch
• duplicate conversions inflating Ads data
• missing conversions in analytics
• attribution model distortion
• incorrect UTMs
• platform bias affecting results
🔴 Attribution Conflict Rule
When platforms disagree:
→ no platform is automatically correct
Required action:
• investigate
• identify cause
• assign confidence level
🔴 Attribution Usage Rule
Attribution may be used for:
• directional optimisation
• channel prioritisation
• budget allocation guidance
Attribution must NOT be used for:
• causal conclusions
• absolute performance truth
• decisions without validation
Relationship to Other Frameworks
This protocol supports:
• Data Brain Attribution Reliability Framework
• Data Brain Measurement Validation Protocol
• Data Brain Data Trust Framework
• HeadOffice Data Decision Gate Framework
• Experimentation Brain Statistical Confidence Framework
Key Outcomes
When applied correctly:
• attribution becomes reliable
• platform bias is controlled
• discrepancies are understood
• decision-making improves
• optimisation direction becomes safer
Change Log
Version: v1.0
Date: 2026-04-23
Author: Data Brain
Change:
Initial creation of Attribution Validation Protocol defining structured process for validating attribution signals across platforms.
Change Impact Declaration
Pages Created:
Data Brain Attribution Validation Protocol
Pages Updated:
None
Pages Deprecated:
None
Registries Requiring Update:
MWMS Architecture Registry
Canon Version Update Required:
No
Change Log Entry Required:
Yes