Document Type: Framework
Status: Draft
Authority: Data Brain
Applies To: Data Brain, Ads Brain, Research Brain, Experimentation Brain, Conversion Brain, Affiliate Brain
Parent: Data Brain
Version: v1.0
Last Reviewed: 2026-04-22
Purpose
The Data Brain Event Value Classification Framework defines how MWMS evaluates the relative importance of tracked events.
Not all recorded user actions represent equal business value.
Some events indicate:
• attention
• engagement
• intent
• evaluation
• commitment
• conversion
• monetization
Without structured classification, analytics systems may:
• overestimate weak signals
• misinterpret behaviour
• optimize toward the wrong outcomes
• inflate perceived performance
• generate false positives in testing
• distort decision-making
This framework ensures events are interpreted according to their true decision significance.
The framework strengthens signal reliability across:
• Ads Brain testing
• Experimentation Brain learning cycles
• Affiliate Brain offer validation
• Conversion Brain optimisation
• Research Brain behavioural analysis
• Data Brain measurement integrity
Core Principle
Every event recorded inside MWMS must be interpretable within a structured value hierarchy.
Events do not represent value equally.
Events must be classified according to behavioural significance, not technical availability.
Tracking capability alone does not determine importance.
Event meaning must align with decision-stage progression.
Definition
An event is any recorded user interaction captured through analytics instrumentation.
Examples:
• page view
• scroll depth
• button click
• video play
• form interaction
• outbound click
• add to cart
• purchase
• subscription
• lead submission
• navigation interaction
• engagement signal
Each event represents a behavioural signal.
Signals vary in strength.
Signal strength reflects likelihood of decision progression.
Event Value Hierarchy
MWMS classifies events into structured tiers.
Tier 1 — Presence Signals
Definition:
Indicates exposure to environment but not engagement.
Examples:
• page view
• session start
• first visit
• landing page view
• ad impression
Interpretation:
User has entered environment.
No evidence of interest.
Weak predictive value.
Usage:
traffic diagnostics
reach measurement
exposure analysis
Do not optimize toward these signals alone.
Tier 2 — Attention Signals
Definition:
Indicates user attention beyond passive exposure.
Examples:
• scroll depth
• video start
• time-on-page threshold
• carousel interaction
• tab visibility duration
• content expansion click
Interpretation:
User is consuming content.
Indicates potential interest.
Still weak purchase correlation.
Usage:
creative diagnostics
message resonance detection
content engagement evaluation
Useful supporting signal only.
Tier 3 — Engagement Signals
Definition:
Indicates active interaction with environment.
Examples:
• navigation interaction
• multi-page visit
• content category exploration
• repeat session behaviour
• internal link click
• tool interaction
• filter usage
Interpretation:
User is exploring environment.
Indicates moderate relevance perception.
Signals curiosity or investigation.
Usage:
behaviour pattern analysis
interest segmentation
UX friction detection
Indicates increased probability of intent formation.
Tier 4 — Intent Signals
Definition:
Indicates meaningful progression toward decision.
Examples:
• CTA click
• outbound click
• video completion
• product detail view
• pricing page view
• feature comparison interaction
• lead magnet initiation
• quiz start
• calculator usage
Interpretation:
User is evaluating decision relevance.
Indicates problem awareness or solution interest.
Strong directional signal.
Usage:
offer validation
funnel diagnostics
persuasion effectiveness evaluation
High value signal tier.
Tier 5 — Pre-Conversion Signals
Definition:
Indicates commitment movement toward conversion outcome.
Examples:
• form start
• add to cart
• checkout start
• email submission
• trial start
• booking initiation
• application start
• payment info entry
Interpretation:
User has crossed psychological threshold toward commitment.
Strong predictive value.
Critical for funnel diagnostics.
Usage:
conversion friction identification
funnel optimisation
drop-off analysis
High diagnostic importance.
Tier 6 — Conversion Signals
Definition:
Represents defined success outcome.
Examples:
• purchase
• qualified lead submission
• booked consultation
• completed signup
• confirmed application
• subscription activation
• completed order
Interpretation:
Primary outcome achieved.
Represents measurable business value.
Core optimisation target.
Usage:
campaign evaluation
offer validation
ROI calculation
traffic quality evaluation
Primary optimization objective.
Tier 7 — Monetization Signals
Definition:
Represents realized financial value.
Examples:
• revenue generated
• subscription payment captured
• order value recorded
• lifetime value increase
• upsell acceptance
Interpretation:
Final realized economic outcome.
Highest certainty signal.
Usage:
financial modelling
ROI analysis
scaling decisions
Ultimate validation layer.
Event Classification Rules
Rule 1
Events must not be treated as equal.
Presence does not equal intent.
Engagement does not equal conversion.
Conversion does not equal profitability.
Each tier must be interpreted within its behavioural meaning.
Rule 2
Optimization targets must match decision-stage objective.
Example:
traffic testing should not optimize toward Tier 1 metrics alone.
Conversion optimization should prioritize Tier 5–6 signals.
Scaling decisions require Tier 6–7 confirmation.
Rule 3
Multiple tiers should be evaluated together.
Strong systems track:
signal progression patterns.
Example:
Tier 2 increase without Tier 4 increase may indicate weak persuasion.
Tier 4 increase without Tier 6 increase may indicate friction.
Tier 5 increase without Tier 7 increase may indicate pricing misalignment.
Signal interpretation requires cross-tier analysis.
Rule 4
Event classification must remain stable across experiments.
Changing event definitions mid-test corrupts learning continuity.
Signal definitions must remain consistent across:
campaign tests
funnel tests
creative tests
offer tests
Consistency protects experiment comparability.
Rule 5
Events must map to behavioural meaning, not platform defaults.
Analytics platforms provide default events.
Default events do not guarantee meaningful interpretation.
MWMS classification overrides platform assumptions when necessary.
Relationship to Other MWMS Frameworks
Supports:
MWMS Standard Conversion Signal Ladder
Data Brain Signal Classification Framework
Data Brain Measurement Integrity Framework
Data Brain Signal Flow Framework
Research Brain Behavioural Analysis structures
Experimentation Brain Test Interpretation Discipline
Ads Brain Pre Conversion Signal Framework
Provides value-layer interpretation across system measurement logic.
Example Application
Example:
Video Ad Funnel
Tier 1
impression
Tier 2
video watch 25 percent
Tier 3
video watch 75 percent
Tier 4
CTA click
Tier 5
email submit
Tier 6
purchase
Tier 7
repeat purchase
System analysis evaluates:
signal progression strength
drop-off concentration
persuasion effectiveness
friction presence
rather than single metric performance.
Governance Notes
Incorrect event weighting can lead to:
false positive experiment results
premature scaling
incorrect creative conclusions
incorrect offer conclusions
incorrect traffic source evaluation
Event classification discipline improves decision reliability.
Change Log
Version: v1.0
Date: 2026-04-22
Author: Data Brain
Change:
Initial creation of Event Value Classification Framework establishing hierarchical event interpretation model for MWMS signal analysis.