Document Type: Framework
Status: Canon
Authority: HeadOffice → Data Brain
Applies To: Finance Brain, Product Brain, Strategy Brain, Affiliate Brain, AIBS Brain
Parent: Data Brain Canon
Version: v1.0
Last Reviewed: 2026-05-03
Purpose
The Data Brain Pricing Research Framework defines how MWMS collects, analyzes, and interprets data to understand customer value perception and willingness to pay.
This framework ensures pricing decisions are based on:
- real customer behaviour
- validated research
- segmented insights
- measurable signals
rather than assumptions, internal opinions, or competitor copying.
Core Principle
Pricing must be informed by evidence, not intuition.
Data reduces uncertainty.
It does not eliminate it.
Position In MWMS System
This framework supports:
- Finance Brain → pricing decisions
- Product Brain → feature prioritization
- Strategy Brain → market selection
- Affiliate Brain → offer selection
- AIBS Brain → monetization systems
Research Objective
The goal of pricing research is to answer:
- What do customers value most?
- What are they willing to pay for it?
- How does value differ across segments?
- What pricing structure aligns with behaviour?
Research Dimensions
Pricing research must cover four core dimensions:
1. Feature Value
Understanding which features matter most.
2. Willingness To Pay
Understanding acceptable price ranges.
3. Segmentation Differences
Understanding how value varies across groups.
4. Perceived Value Drivers
Understanding why customers assign value.
Research Methods
1. Relative Preference Testing
Measures importance of features.
Methods
- MaxDiff (maximum difference scaling)
- forced ranking
- trade-off analysis
Output
- feature priority ranking
- differentiation identification
2. Willingness To Pay Testing
Measures price sensitivity.
Methods
- Van Westendorp price sensitivity meter
- price ladder questions
- direct willingness-to-pay surveys
Output
- acceptable price range
- optimal pricing zone
- price perception thresholds
3. Conjoint Analysis
Measures how customers value combinations.
Purpose
Understand trade-offs between:
- price
- features
- packaging
Output
- feature pricing impact
- optimal package structure
4. Behavioural Data Analysis
Uses real behaviour instead of stated preference.
Sources
- purchase data
- upgrade behaviour
- churn patterns
- feature usage
- conversion rates
Output
- real willingness to pay
- retention-linked value
- behaviour-based segmentation
5. VOC Pricing Signals
Extract pricing insights from:
- reviews
- testimonials
- support tickets
- complaints
- objections
Output
- perceived value language
- pricing objections
- price sensitivity signals
Segmentation Requirement
All pricing data must be segmented.
Segmentation Variables
- customer type
- income or company size
- experience level
- use case
- geography
Rule
Aggregated data hides truth.
Segmentation reveals value.
Pricing Signal Hierarchy
Not all data is equal.
Highest Reliability
- actual purchase behaviour
- upgrade patterns
- churn behaviour
Medium Reliability
- structured research (surveys, conjoint)
Lowest Reliability
- stated opinions without behaviour
Rule
Behaviour > Stated Preference
Output Structure
Pricing research must produce:
1. Value Map
What customers care about.
2. Price Range
Acceptable pricing boundaries.
3. Feature Value Ranking
What drives decisions.
4. Segment Pricing Differences
Who pays more and why.
5. Risk Indicators
Where pricing may fail.
Pricing Insight Application
Finance Brain
- sets pricing levels
- defines monetization strategy
Product Brain
- prioritizes features
- removes low-value features
Strategy Brain
- selects markets
- defines positioning
Affiliate Brain
- selects offers
- evaluates commission potential
AIBS Brain
- builds monetization systems
- defines client pricing models
Testing Integration
Pricing insights must be validated through:
- real campaigns
- conversion data
- retention analysis
Rule
Research informs decisions.
Testing confirms them.
Common Failure Modes
This framework prevents:
1. Guess-Based Pricing
No data → poor decisions
2. Over-Averaging
Ignoring segment differences
3. Feature Mispricing
Charging for low-value features
4. Ignoring Behaviour
Relying only on surveys
5. Static Pricing
Not adapting to new data
Drift Protection
The system must prevent:
- pricing without research
- ignoring segmentation
- over-reliance on opinions
- failure to update data
- using outdated insights
Operational Rules
Rule 1: Start Small
Begin with focused questions.
Rule 2: Iterate
Research → test → refine
Rule 3: Validate With Behaviour
Always confirm with real data
Rule 4: Avoid Perfection
Good data > perfect data
Architectural Intent
This framework ensures MWMS:
- understands value before pricing
- reduces pricing risk
- improves monetization
- aligns product with demand
Final Rule
If pricing is not supported by data:
→ it is a guess
Change Log
Version: v1.0
Date: 2026-05-03
Author: HeadOffice
Change:
Created Pricing Research Framework defining structured data collection, analysis, and application for pricing decisions across MWMS.
Change Impact Declaration
Pages Created:
Data Brain Pricing Research Framework
Pages Updated:
None
Pages Deprecated:
None
Registries Requiring Update:
MWMS Architecture Registry
Data Brain Page Registry
Canon Version Update Required:
No
Change Log Entry Required:
Yes