Document Type: Framework
Status: Active
Authority: Data Brain
Parent: Data Brain Canon
Applies To: All user-level data interpretation, attribution systems, traffic evaluation, and future scoring engines across MWMS
Version: v1.0
Last Reviewed: 2026-05-02
Purpose
The Data Brain Visitor Value Scoring Framework defines how MWMS assigns value to individual visitors based on their likelihood to convert and generate future revenue.
The purpose is to:
- differentiate traffic quality
- identify high-value users
- support smarter marketing decisions
- enable segmented treatment of users
- improve allocation of budget and effort
This framework shifts MWMS from:
→ treating all traffic equally
to:
→ valuing users based on predicted impact
Scope
This framework applies to:
- website visitors
- ad traffic
- affiliate traffic
- remarketing audiences
- lead scoring systems
- conversion optimization processes
- future personalization systems
It governs how user value is interpreted and acted upon, not how it is technically calculated.
Core Principle
Not all visitors are equal.
Every visitor has a different:
- likelihood to convert
- expected value
- behavioural profile
MWMS must identify these differences and act accordingly.
Definition — Visitor Value Score
A Visitor Value Score is:
a dynamic representation of how valuable a user is at a given moment in time based on their behaviour and likelihood to convert.
The score is:
- predictive, not historical
- relative, not absolute
- dynamic, not fixed
Scoring Intent
Visitor value scoring must support:
- decision making
- segmentation
- prioritisation
It must NOT be used for:
- static reporting
- vanity metrics
- unnecessary complexity
Scoring Dimensions
Visitor value is influenced by a combination of signals.
1. Behavioural Signals
- pages viewed
- time on site
- repeat visits
- depth of interaction
- engagement with key assets
2. Journey Position
- early exploration
- mid consideration
- high intent stage
- near conversion
3. Attribution Signals
- source quality
- campaign origin
- historical conversion patterns
- contribution of prior touchpoints
4. Historical Patterns
- similarity to past converters
- similarity to high-value users
- frequency of engagement
- returning vs new user behaviour
5. Contextual Signals
- device type
- timing of visit
- recency
- external triggers
Value Segmentation
Visitors must be grouped into actionable segments.
Example Structure
- High Value
- Medium Value
- Low Value
- Unknown / Unclassified
Segment Purpose
Each segment must drive:
- different treatment
- different messaging
- different budget allocation
Decision Application
Visitor value must influence real decisions.
1. Advertising
- higher bids for high-value users
- lower bids for low-value users
- suppression of low-quality segments
2. Remarketing
- prioritise high-value audiences
- adjust frequency and timing
- customise messaging
3. User Experience
- change content or offers
- adjust recommendations
- modify funnel pathways
4. Affiliate Traffic Evaluation
- evaluate traffic quality beyond clicks
- identify valuable affiliates
- optimise source selection
5. Conversion Optimization
- prioritise high-value journeys
- identify friction for valuable users
- adjust landing pages accordingly
Dynamic Nature Rule
Visitor value must be treated as dynamic.
A user can:
- increase in value
- decrease in value
- shift segments
MWMS must continuously reassess visitor value.
Aggregation Rule
Although scoring occurs at the visitor level:
→ decisions are made at the segment level
MWMS must avoid:
- overfitting to individuals
- relying on isolated behaviour
Pattern Learning Integration
Visitor value scoring must feed into:
- Research Brain
- Experimentation Brain
- Affiliate Brain
Purpose:
→ improve understanding of what drives value
Cognitive Simplicity Rule
Scoring must remain interpretable.
Avoid:
- overly complex scoring logic
- opaque models
- scores that cannot be explained
Users of MWMS must understand:
- what high value means
- what low value means
- how to act on it
System Evolution
Initial scoring may be:
- rule-based
- simple segmentation
Over time, it may evolve into:
- predictive models
- machine learning systems
- automated scoring engines
Drift Protection
The system must prevent:
- treating all users equally
- static segmentation
- over-complex scoring systems
- scoring without actionable outcomes
- using scores without influencing decisions
Architectural Role
This framework operates within:
- Data Brain (primary ownership)
- Ads Brain (bid strategy)
- Affiliate Brain (traffic valuation)
- Research Brain (pattern analysis)
It acts as the bridge between:
→ raw user behaviour
and
→ strategic action
Relationship To Other MWMS Standards
This framework works alongside:
- Data Brain Decision Surface Framework
- Tracking Governance Protocol
- MWMS Architecture Registry
- MWMS Brain Routing Rule
Architectural Intent
The Data Brain Visitor Value Scoring Framework ensures:
- MWMS prioritises valuable users
- decisions are informed by predicted value
- marketing becomes more efficient
- scaling becomes more controlled
- system intelligence increases over time
It moves MWMS from:
→ volume-based thinking
to:
→ value-based thinking
Change Log
Version: v1.0
Date: 2026-05-02
Author: Data Brain
Change:
Created Data Brain Visitor Value Scoring Framework to define how MWMS assigns and uses visitor value for decision-making and traffic prioritisation.
Change Impact Declaration
Pages Created:
Data Brain Visitor Value Scoring Framework
Pages Updated:
None
Pages Deprecated:
None
Registries Requiring Update:
MWMS Architecture Registry
Canon Version Update Required:
No
Change Log Entry Required:
Yes
END OF DOCUMENT