Document Type: Framework
Status: Canon
Authority: HeadOffice → Finance Brain
Applies To: Affiliate Brain, Product Brain, Strategy Brain, Data Brain, AIBS Brain
Parent: SYSTEM Finance Brain Canon
Version: v1.0
Last Reviewed: 2026-05-03
Purpose
The Finance Brain Value Based Pricing Framework defines how MWMS determines, validates, and optimizes pricing based on customer-perceived value, not internal assumptions, cost structures, or competitor copying.
This framework exists to:
- maximize revenue per customer
- ensure pricing reflects real market value
- reduce pricing risk and guesswork
- align product, offer, and monetization strategy
- support scalable, data-driven pricing decisions across all MWMS systems
Core Principle
Pricing is the exchange rate on value.
Customers do not pay for:
- features
- effort
- complexity
They pay for:
- perceived outcomes
- solved problems
- delivered value
Position In MWMS System
Value Based Pricing sits at the intersection of:
- Product Brain → what is built
- Strategy Brain → who it is for
- Data Brain → what is measured
- Affiliate Brain → what is sold
- Finance Brain → what is monetized
Flow:
- Strategy defines target market
- Product defines features and capabilities
- Data captures value perception and behaviour
- Finance converts value into pricing
- Affiliate and Ads execute monetization
Pricing Model Hierarchy
MWMS prioritizes pricing approaches in the following order:
1. Value Based Pricing (Primary)
Price is determined by:
- perceived benefit
- willingness to pay
- importance of outcome
2. Value Metric Pricing (Preferred Structure)
Price scales with customer growth or usage:
- usage-based
- outcome-based
- growth-aligned
3. Packaging Optimization
Features structured into:
- core
- differentiators
- add-ons
4. Tactical Adjustments
- discounts
- localization
- billing structure
Two Axes Of Value
All pricing decisions must be evaluated on:
Axis 1: Relative Feature Value
How important is a feature compared to others?
Axis 2: Willingness To Pay
How much more will customers pay for it?
Value Matrix
All features must be classified into one of four categories:
1. Differentiators
High value + high willingness to pay
→ core positioning
→ pricing leverage
2. Core Features
High value + low willingness to pay
→ expected
→ must exist
→ not priced independently
3. Add-Ons
Low value (to majority) + high willingness to pay (to minority)
→ monetization expansion
→ optional revenue drivers
4. Commoditized / Trash
Low value + low willingness to pay
→ deprioritize
→ remove or simplify
Critical Rule
MWMS does not assume feature value.
Feature value must be validated through research or data signals.
Pricing Research Requirements
All value-based pricing decisions must be supported by:
1. Segmentation Data
Who is the customer?
- company size
- income / revenue
- use case
- experience level
2. Relative Preference Data
What matters most?
- feature importance ranking
- trade-off decisions
- max-diff style prioritization
3. Willingness To Pay Data
What will they pay?
- price sensitivity ranges
- perceived “too expensive” thresholds
- perceived “too cheap = low quality” thresholds
Decision Philosophy
Pricing Does NOT Provide Certainty
Pricing data:
- reduces risk
- narrows range
- informs decisions
It does not:
- guarantee accuracy
- eliminate uncertainty
Decision Rule
Higher impact decisions → require more data
Lower impact decisions → require speed over precision
Common Failure Modes
MWMS explicitly avoids:
1. Gut Feel Pricing
Assuming value without validation
2. Feature-Based Pricing Without Value Alignment
Charging for features customers do not care about
3. Averaging Customers
Trying to satisfy all segments equally
→ leads to weak positioning and poor monetization
4. Static Pricing
Failing to evolve pricing as value and market change
5. Over-Reliance On Competitors
Copying pricing instead of understanding value
Pricing Evolution Rule
Value perception changes over time.
Feature lifecycle:
- Differentiator → Core → Commoditized
This movement is accelerating.
MWMS must:
- continuously reassess value
- update pricing accordingly
- avoid outdated assumptions
Operational Rules
Rule 1: Start Small
Begin with:
- simple pricing question
- narrow scope
- focused segment
Rule 2: Iterate Fast
Run cycles of:
- hypothesis
- test
- data collection
- decision
Rule 3: Segment Everything
Aggregate data is weak.
All pricing insights must be:
- segmented
- compared
- evaluated per group
Rule 4: Identify Bad Customers Early
Not all customers are worth serving.
Pricing must expose:
- low willingness to pay segments
- unprofitable personas
- misaligned markets
Rule 5: Pricing Must Support Business Model
Pricing is not independent.
It must align with:
- CAC (customer acquisition cost)
- LTV (lifetime value)
- retention
- scalability
MWMS Integration
This framework feeds into:
Affiliate Brain
- offer selection
- commission evaluation
- pricing angle creation
Product Brain
- feature prioritization
- roadmap decisions
- packaging structure
Data Brain
- pricing research
- signal collection
- segmentation analysis
Strategy Brain
- market selection
- positioning
- target customer definition
AIBS Brain (Future)
- client pricing systems
- business monetization models
- consulting frameworks
Output Of This Framework
When applied correctly, this framework produces:
- validated pricing ranges
- clear feature prioritization
- defined pricing personas
- scalable monetization models
- reduced pricing risk
- increased revenue per customer
Final Principle
You cannot guess value at scale.
You must measure it, interpret it, and act on it.