Document Type: Framework
Status: Canon Candidate
Authority: HeadOffice
Applies To: Experimentation Brain, Affiliate Brain, Ads Brain, Content Brain
Parent: Experimentation Brain Testing Intelligence Framework
Version: v1.1
Last Reviewed: 2026-04-25
Purpose
Defines the experiment structures used to test how trust signals influence behavioural continuation.
Ensures Experimentation Brain can evaluate whether changes to:
• credibility signals
• reassurance elements
• transparency structures
improve or reduce progression probability.
Supports structured testing of trust variables across:
• ads
• landing pages
• bridge pages
• offer environments
🔴 Extension (NEW)
All trust signal tests must operate within:
👉 Measurement Planning
👉 Interpretation Discipline
👉 Decision Workflow
Trust signals must not be tested in isolation from system structure.
Core Principle
Users are more likely to act when perceived credibility outweighs perceived risk.
Trust formation variables can be tested structurally through changes to environment signals.
Trust is influenced by:
• competence perception
• integrity perception
• risk transparency
🔴 System Principle (NEW)
Trust signals do not act independently.
They operate within:
• behavioural progression
• environment context
• expectation alignment
Interpretation must reflect system-level influence.
Test Domains
Competence Signal Tests
Test whether perceived capability influences continuation probability.
Variables may include:
• mechanism explanation clarity
• structured presentation of information
• logical process explanation
• clarity of how outcome is produced
Primary question:
Does increasing perceived competence increase continuation probability?
Integrity Signal Tests
Test whether perceived honesty influences progression.
Variables may include:
• realistic claim language
• balanced expectation framing
• transparent explanation of limitations
• moderated promise intensity
Primary question:
Does perceived honesty improve commitment confidence?
Risk Transparency Tests
Test whether visibility of conditions influences behaviour.
Variables may include:
• explanation of what happens next
• visibility of requirements
• clarity of expected commitment
• clarity of process steps
Primary question:
Does increased clarity about risk reduce hesitation?
Reassurance Signal Tests
Test whether reassurance elements improve behavioural progression.
Variables may include:
• guarantee presence or absence
• refund visibility
• expectation clarity statements
• process clarity statements
• support visibility
Primary question:
Do reassurance signals increase continuation probability?
Consistency Signal Tests
Test whether alignment between ad and landing environment influences trust stability.
Variables may include:
• message continuity
• promise continuity
• tone consistency
• value proposition consistency
Primary question:
Does alignment improve trust stability across steps?
🔴 Measurement Planning Requirement (NEW)
Before any trust test:
Data Brain must confirm:
• measurement plan exists
• segmentation is defined
• behavioural progression points are mapped
• metrics align with hypothesis
If not:
→ test must not proceed
Recommended Metrics
Primary
• progression rate to next step
• conversion completion rate
• click-through to commitment event
• continuation behaviour rate
Secondary
• hesitation time before action
• abandonment rate
• scroll depth before exit
• repeated review behaviour
Diagnostic
• bounce behaviour after exposure
• delayed decision behaviour
• return visits before commitment
🔴 Metric Alignment Rule (NEW)
Metrics must:
• map to hypothesis
• map to behavioural stage
• align with decision thresholds
Testing Rules
• isolate trust variables where possible
• avoid simultaneous changes to unrelated persuasion variables
• document expected behavioural mechanism prior to test
• maintain consistent measurement conditions
• interpret results using behavioural explanation, not uplift alone
🔴 Data Validation Rule (NEW)
Before interpreting results:
Data must pass:
• Signal Integrity
• Measurement Integrity
• Data Trust
If not:
→ test must be downgraded or invalidated
🔴 Forecast Requirement (NEW)
Each test must define:
• expected direction of change
• expected magnitude range
Interpretation must compare:
👉 expected vs actual vs variance
Structural Insight
Trust is not created through claims alone.
Trust emerges when the environment:
• appears competent
• appears honest
• clarifies risk
• aligns expectations
🔴 Behavioural Layer Extension (NEW)
Trust signals must be evaluated within:
• funnel stage
• behavioural progression
• user intent level
A signal may succeed in one stage and fail in another.
Interpretation Discipline (NEW SECTION)
Trust tests must be interpreted using:
• behavioural explanation
• signal stability
• repeatability
• context awareness
• variance vs expectation
Interpretation must answer:
• Why did behaviour change?
• Which trust mechanism influenced it?
• Was the effect stable?
• Was the effect consistent across segments?
Interpretation must NOT rely on:
• uplift alone
• isolated metric movement
• short-term spikes
Decision Discipline (NEW SECTION)
Trust signal tests must follow:
Allowed Decisions
• refine trust element
• extend test
• validate across segments
• integrate into system cautiously
Restricted Decisions
• immediate scaling based on single result
• system-wide rollout without repeatability
• capital escalation without validation
Decision Gate
Decision may only occur if:
• signal is stable
• interpretation is complete
• variance is understood
• data integrity is confirmed
Interfaces
Inputs
• Affiliate Brain Trust Formation Model
• Ads Brain Trust Formation Signals
• Behavioural Signal Framework
• Funnel Environment Audit
• creative review
Outputs
• Trust Signal Impact Result
• Credibility Influence Finding
• Risk Transparency Impact Finding
• Reassurance Impact Finding
🔴 Extended Outputs (NEW)
• Trust Mechanism Validation
• Behavioural Stage Impact
• Variance Analysis Result
Drift Triggers
Reject or flag if:
• multiple credibility variables are changed without isolation
• reassurance elements contradict structural reality
• test interpretation lacks behavioural explanation
• uplift is claimed without trust mechanism evidence
• trust signals are tested without considering risk context
• forecast comparison is missing (NEW)
• data integrity is not validated (NEW)
Controlled Loss Alignment (NEW)
This framework supports Controlled Loss by:
• preventing false-positive scaling
• enforcing behavioural validation
• reducing misinterpretation risk
• ensuring trust signals are proven before rollout
Architectural Intent
This framework ensures MWMS can:
👉 systematically test trust formation
rather than:
👉 guessing persuasion effects
🔴 Architectural Extension (NEW)
It ensures:
• trust is measured structurally
• trust is interpreted behaviourally
• trust is validated before scaling
Final Rule
If trust signals improve metrics but behavioural explanation is unclear:
→ result must not be trusted
Change Log
Version: v1.1
Date: 2026-04-25
Author: HeadOffice
Change
Upgraded framework to include:
• measurement planning dependency
• forecast vs actual comparison
• interpretation discipline integration
• decision gating logic
• behavioural context enforcement