Document Type: Framework
Status: Canon
Authority: HeadOffice
Parent: Research Brain Canon
Version: v1.2
Last Reviewed: 2026-04-22
Purpose
The Research Brain Behavioural Signal Framework defines how MWMS interprets observable customer actions as structured intelligence inputs used to improve decision accuracy across acquisition, conversion, lifecycle, forecasting, and optimisation systems.
Observed behaviour provides probabilistic evidence of preference, motivation, friction, and decision readiness.
Behaviour reveals decision context under real-world constraints.
Structured behavioural interpretation improves:
decision relevance
targeting precision
offer alignment
lifecycle timing accuracy
CRO prioritisation
forecast reliability
product usability insight
Behaviour signals provide stronger evidence than stated opinion.
Interpretable behaviour improves system learning quality.
Improved learning quality improves optimisation stability.
Stable optimisation improves system performance.
Scope
This framework governs interpretation of behavioural signals indicating:
attention patterns
engagement depth
interaction sequences
navigation tendencies
comparison behaviour
repeat interaction behaviour
drop-off patterns
response timing behaviour
effort tolerance behaviour
decision hesitation behaviour
Behaviour signals may originate from:
page interaction patterns
content engagement behaviour
navigation sequences
search interaction behaviour
conversion pathway observation
tool usage patterns
workflow friction behaviour
email engagement behaviour
SMS engagement behaviour
repeat purchase behaviour
Behaviour signals influence:
Creative Brain persuasion structure
Customer Brain lifecycle interpretation
Product Brain usability optimisation
Strategy Brain capability prioritisation
Offer Brain value clarity
Conversion Brain decision environment design
Experimentation Brain hypothesis formation
Data Brain signal measurement logic
This framework does not govern:
statistical validation logic
tracking implementation
data storage architecture
campaign configuration logic
capital allocation decisions
These remain governed by:
Experimentation Brain
Data Brain
Ads Brain
Finance Brain
Research Brain governs behavioural interpretation logic.
Core Principle
Behaviour indicates preference under constraint.
Preference under constraint reveals true priorities.
True priorities improve decision relevance.
Decision relevance improves optimisation efficiency.
Behaviour signals improve clarity of real-world interaction patterns.
Interaction clarity improves system learning quality.
Behaviour clarity improves system learning quality.
Improved learning quality improves optimisation stability.
Behaviour Signal Dimensions
Behaviour signals may be evaluated across six structural dimensions:
attention behaviour
engagement behaviour
sequence behaviour
repetition behaviour
friction behaviour
effort tolerance behaviour
Each dimension improves interpretation clarity.
Behavioural Signal Layer Model
Behavioural interpretation can be evaluated across layered psychological progression stages.
These layers improve understanding of how users transition from awareness to action.
Layer 1 — Attention
User notices the opportunity.
Measured by:
visual hierarchy
headline clarity
dominant page element recall
Flash testing method:
What was the most prominent element on the page?
Attention indicates perceived relevance.
Perceived relevance influences engagement probability.
Layer 2 — Comprehension
User understands:
what the offer is
what problem it solves
Flash test question:
What do you think this page was about?
Understanding reduces uncertainty.
Reduced uncertainty improves behavioural progression probability.
Layer 3 — Desire / Motivation
User perceives benefit value.
Signal question:
Can you name one benefit?
Motivation increases behavioural readiness.
Layer 4 — Emotional Activation
Emotion determines behavioural energy level.
Primary driver categories:
gain motivation
threat avoidance
relief seeking
identity reinforcement
Emotion drives action more strongly than logic.
Motivation equals emotion in action.
Layer 5 — Trust
Trust is evaluated rapidly and subconsciously.
Trust judgement occurs within fractions of a second.
Trust clarity reduces perceived risk.
Reduced perceived risk improves decision continuation probability.
Layer 6 — Action Clarity
User must understand next step.
Signal question:
What is the next step the site owner wants you to take?
Clarity reduces hesitation.
Reduced hesitation improves behavioural completion probability.
Layer 7 — Retention Signal
Long-term behaviour requires repeated interaction.
Habit formation typically requires repeated behavioural reinforcement cycles.
Repeated interaction strengthens relationship continuity.
Relationship continuity improves lifecycle stability.
Behaviour Signal Dimensions Explained
Attention Behaviour
Indicates what captures focus.
Examples:
click patterns
scroll behaviour
time allocation patterns
information prioritisation behaviour
Attention behaviour reveals perceived relevance.
Perceived relevance influences engagement probability.
Engagement Behaviour
Indicates depth of interaction.
Examples:
content consumption depth
interaction frequency
return visit patterns
time spent engaging
Engagement depth often reflects perceived value.
Value perception influences progression likelihood.
Sequence Behaviour
Indicates how individuals move through environments.
Examples:
navigation order patterns
decision pathway sequences
step progression behaviour
Sequence patterns reveal decision logic structure.
Decision structure clarity improves optimisation relevance.
Repetition Behaviour
Indicates repeated interaction behaviour.
Examples:
repeat visits
repeated product interaction
repeat content engagement
Repetition often indicates perceived usefulness or unresolved need.
Unresolved need signals opportunity.
Friction Behaviour
Indicates resistance patterns.
Examples:
drop-off behaviour
abandonment patterns
hesitation signals
backtracking behaviour
Friction signals reveal decision obstacles.
Obstacle clarity improves optimisation direction.
Effort Tolerance Behaviour
Indicates acceptable effort levels before disengagement occurs.
Examples:
multi-step completion patterns
complex navigation tolerance
information processing persistence
Effort tolerance reveals usability boundaries.
Understanding boundaries improves environment design.
Behavioural Signal Categories
Navigation Signals
indicate exploratory behaviour patterns.
Product Interaction Signals
indicate product-level interest intensity.
Cart Behaviour Signals
indicate evaluation stage behaviour.
Checkout Behaviour Signals
indicate high purchase intent signals.
Purchase Behaviour Signals
indicate economic relationship formation.
Engagement Signals
indicate communication responsiveness patterns.
Lifecycle Progression Signals
indicate movement between relationship stages.
Friction Signals
indicate decision resistance presence.
Signal Strength Model
Signals vary in strength depending on proximity to economic action.
Example weighting logic:
purchase event = strong signal
checkout initiation = high signal
add to cart = moderate signal
product view = weaker signal
Signal weighting improves prioritisation clarity.
Signal Recency Principle
Recent behaviour carries stronger predictive value than historical behaviour.
Recency improves predictive accuracy.
Signal decay must be considered in interpretation logic.
Recent behaviour improves relevance alignment.
Behaviour Pattern Interpretation Principle
Behaviour should be interpreted in context.
Isolated signals may mislead interpretation.
Pattern repetition improves reliability.
Multiple signal sources increase confidence.
Confidence improves decision stability.
Relationship to Other Brains
Creative Brain
uses behavioural interpretation to improve persuasion relevance.
Customer Brain
uses behavioural signals to interpret lifecycle stage.
Product Brain
uses behavioural signals to improve usability.
Strategy Brain
uses behavioural patterns to guide capability prioritisation.
Conversion Brain
uses behaviour interpretation to improve decision environment clarity.
Experimentation Brain
tests behavioural sensitivity to environmental changes.
Data Brain
ensures behavioural signals remain measurable.
HeadOffice
retains governance authority.
Research Brain ensures behavioural understanding remains structured across MWMS.
Failure Modes Prevented
misinterpreting stated preference as true preference
ignoring friction patterns
misreading engagement depth signals
overvaluing weak behavioural indicators
misinterpreting isolated events as patterns
designing environments misaligned with actual behaviour
Behaviour clarity improves optimisation direction.
Drift Protection
The system must prevent:
behaviour assumptions without signal support
weak patterns treated as strong evidence
isolated behaviour events treated as stable trends
behaviour interpretation without contextual evaluation
misclassification of signal strength
interpretation drift across time
persuasion replacing behavioural interpretation logic
Behaviour interpretation must remain structured.
Architectural Intent
Research Brain Behavioural Signal Framework ensures MWMS decisions reflect observable interaction patterns rather than assumption-driven reasoning.
Behaviour clarity improves:
message relevance
environment design quality
opportunity selection accuracy
product usability alignment
conversion optimisation logic
Structured behaviour interpretation strengthens long-term system intelligence.
Final Rule
If behavioural signals are ignored, decision accuracy decreases.
Reduced decision accuracy increases optimisation instability.
Behavioural interpretation must remain structured and evidence-informed.
Change Log
Version: v1.2
Date: 2026-04-22
Author: HeadOffice
Change:
Merged behavioural signal layer model from legacy Affiliate Brain Behavioural Signal Framework.
Added behavioural layer sequence:
Attention
Comprehension
Desire
Emotional Activation
Trust
Action Clarity
Retention Signal
Expanded interpretation structure for psychological drivers of behaviour.
Improved alignment between:
behaviour interpretation
conversion environment optimisation
offer evaluation inputs
hook relevance logic
Strengthened Research Brain authority over behavioural interpretation logic.