System: MWMS
Brain: Research Brain
Document Type: Framework
Authority Level: MCR Source Of Truth
Status: Active
Primary Location: MCR
Parent Page: Research Brain
Owner: Martyn
Developer Boundary: Operational Research Governance Only
Source Of Truth: MCR
Purpose
The Research Question And Method Selection Framework defines how MWMS turns business goals, operational problems, user uncertainty, and strategic assumptions into clear research questions and appropriate research methods.
This framework exists to prevent MWMS from:
- choosing research methods randomly
- asking vague research questions
- collecting unusable data
- confirming internal assumptions
- using surveys when interviews are needed
- watching behaviour when belief research is needed
- treating all research questions as the same
The framework ensures that every research activity begins with:
- a clear business goal
- a clear research question
- a clear assumption set
- a correct method choice
- a useful decision outcome
Scope
This framework applies to all MWMS research involving:
- users
- buyers
- customers
- leads
- visitors
- subscribers
- offer audiences
- affiliate traffic
- product users
- onboarding users
- content consumers
- sales prospects
It applies across:
- Research Brain
- Customer Brain
- Conversion Brain
- Product Brain
- Affiliate Brain
- Offer Brain
- Ads Brain
- Content Brain
- Experimentation Brain
- HeadOffice Intelligence Layer
Core Operating Principle
The research question determines the research method.
MWMS must not begin with:
“Let’s do a survey.”
or:
“Let’s interview users.”
MWMS must begin with:
“What decision are we trying to improve?”
Only after the goal and question are defined should the method be selected.
Step 1 — Start With The Business Goal
Every research project must begin with a business or operational goal.
Examples:
- reduce support requests
- increase subscription revenue
- improve onboarding completion
- increase VSL click-through
- reduce landing page confusion
- improve offer selection
- improve customer retention
- improve paid traffic efficiency
- identify why users abandon checkout
The goal defines why the research matters.
Step 2 — Convert The Goal Into Research Questions
MWMS uses the newspaper method:
- who
- what
- when
- where
- why
- how
Example goal:
Reduce support requests for onboarding setup.
Possible research questions:
- Who is submitting these requests?
- What are the requests about?
- When do users get stuck?
- Where in the journey does confusion happen?
- Why are users struggling?
- How are users trying to solve the problem?
Step 3 — Separate Open Questions From Closed Questions
MWMS classifies questions as:
Open Exploratory Questions
Used when the system does not yet understand the problem.
Examples:
- Why are users abandoning onboarding?
- What makes users trust this offer?
- How do buyers compare this solution with alternatives?
- What fears block purchase?
Best for:
- interviews
- contextual inquiry
- exploratory observation
- qualitative synthesis
Closed Validation Questions
Used when the system has a specific hypothesis to test.
Examples:
- Are users confused by the pricing table?
- Do users trust the guarantee statement?
- Does video A produce higher engagement than video B?
- Do returning customers respond better to bundle offers?
Best for:
- surveys
- usability tasks
- analytics
- A/B testing
- controlled experiments
Step 4 — Document Assumptions Before Research
Before selecting a method, MWMS must document what it believes may be true.
Examples:
- users do not understand the offer
- buyers are price-sensitive
- visitors distrust the claims
- new users need clearer onboarding
- technical users prefer more detail
- mobile users abandon because of friction
Assumptions must be treated as testable, not factual.
Step 5 — Determine Required Insight Type
MWMS selects methods based on the type of insight required.
Attitudinal Insight
Used to understand what people:
- think
- feel
- believe
- fear
- want
- say
- remember
- perceive
Methods:
- interviews
- surveys
- open-ended feedback
- customer conversations
Behavioural Insight
Used to understand what people:
- do
- click
- ignore
- abandon
- complete
- repeat
- struggle with
Methods:
- usability testing
- analytics
- session recordings
- task observation
- heatmaps
- A/B testing
Step 6 — Match Method To Development Stage
Different stages require different methods.
Discover Stage
Problem is unclear.
Best methods:
- interviews
- ethnography
- contextual inquiry
- exploratory surveys
- customer conversations
Goal:
understand needs, beliefs, problems, and context.
Explore Stage
Problem is known but solution is unclear.
Best methods:
- concept testing
- interviews
- journey mapping
- workflow analysis
- prototype feedback
Goal:
identify possible solution directions.
Test Stage
Solution exists and needs validation.
Best methods:
- usability testing
- task-based testing
- A/B testing
- preference testing
- conversion testing
Goal:
test whether solution works.
Measure Stage
Solution is live.
Best methods:
- analytics
- surveys
- cohort analysis
- behavioural tracking
- support-ticket review
Goal:
measure real-world performance.
Step 7 — Avoid Method Bias
MWMS must not choose a research method because:
- it is easy
- it is familiar
- it is trendy
- a stakeholder requested it
- AI can process it quickly
- it confirms what the team already believes
Method selection must be governed by the question.
Step 8 — Choose The Smallest Useful Method
Research should be practical and efficient.
MWMS should choose the smallest method that can answer the question with enough confidence.
Examples:
- five usability tests may reveal major interface problems
- five interviews may reveal early qualitative patterns
- a survey may validate pattern prevalence
- analytics may identify behavioural scale
- session recordings may reveal friction location
Research should not become bloated.
Step 9 — Combine Methods When Risk Is High
High-risk decisions need stronger evidence.
Examples of high-risk decisions:
- major offer changes
- pricing changes
- funnel rebuilds
- onboarding redesigns
- new product direction
- paid traffic scale decisions
For high-risk decisions, MWMS should triangulate insight using more than one method.
Example:
Interview insight
- usability observation
- analytics evidence
= stronger decision confidence
Research Method Selection Guide
| Question Type | Best Method |
|---|---|
| Why do users feel confused? | Interviews |
| How many users share this problem? | Survey |
| Where do users abandon? | Analytics |
| What do users actually do? | Behavioural observation |
| Can users complete this task? | Usability testing |
| Which version performs better? | A/B testing |
| What language do users use? | Interviews / VOC mining |
| What patterns exist across many responses? | Survey / AI-assisted synthesis |
| What journey creates friction? | Journey mapping |
| What should we build first? | Mixed research + prioritization |
Method Selection Rules
Rule 1 — Do Not Use Surveys For Unknown Problems
Surveys are strongest when MWMS already knows what it wants to validate.
Surveys are weaker for discovering unknown problems.
Rule 2 — Do Not Ask Users To Predict Future Behaviour
Users are poor predictors of what they will do later.
MWMS should avoid questions like:
“Would you buy this?”
Better:
“Tell me about the last time you bought something like this.”
Rule 3 — Observe Behaviour When Behaviour Matters
If the question involves:
- clicking
- completing
- abandoning
- using
- searching
- comparing
- navigating
MWMS should observe behaviour, not only ask about it.
Rule 4 — Use Interviews For Deep Understanding
Interviews are best for:
- motivation
- context
- fears
- decision criteria
- language
- objections
- emotional drivers
Rule 5 — Use Analytics For Scale
Analytics are best for:
- volume
- drop-off
- frequency
- conversion paths
- behavioural trends
Rule 6 — Use Testing For Task Success
Testing is best when MWMS needs to know whether users can successfully complete an action.
AI Assisted Method Selection
AI may assist by:
- turning goals into research questions
- classifying question type
- suggesting suitable methods
- identifying assumptions
- recommending triangulation options
- summarizing method trade-offs
AI must not:
- choose methods without business context
- replace researcher judgment
- ignore sample quality
- treat AI-generated assumptions as evidence
- skip participant validation
Research Output Requirements
Every research project should clearly state:
- business goal
- research question
- assumption being tested
- method selected
- why that method was selected
- participant type required
- expected decision outcome
- destination Brain for findings
Governance Role
Research Brain governs:
- research question quality
- method selection
- assumption documentation
- research integrity
- synthesis and routing
HeadOffice governs:
- strategic importance
- cross-Brain relevance
- final decision priority
- escalation when findings affect major architecture
Relationship To Other MWMS Standards
This framework supports:
- Research Brain User Research Operating Framework
- Research Brain User Interview And Survey Framework
- Research Brain Behavioural Testing And Observation Framework
- Research Brain Research Synthesis And Deliverables Framework
- Customer Brain Persona Intelligence
- Conversion Brain Funnel Analysis
- Experimentation Brain Test Design
- Affiliate Brain Offer Intelligence
- HeadOffice Intelligence Layer
Drift Protection
MWMS must prevent:
- research without goals
- vague research questions
- method-first research
- stakeholder-led method bias
- assumption confirmation
- survey overuse
- behavioural questions answered only by opinion
- closed questions used too early
- open questions used when validation is needed
- AI-assisted research without human interpretation
Architectural Intent
This framework establishes Research Brain as the method-selection authority for user research.
It ensures MWMS research becomes:
- goal-driven
- question-led
- method-appropriate
- assumption-aware
- evidence-sensitive
- operationally useful
The intent is to make research a decision-quality system, not a data-collection exercise.
Change Log
v1.0
- Created Research Question And Method Selection Framework
- Added business-goal-first research logic
- Added newspaper method for question generation
- Added open vs closed question classification
- Added attitudinal vs behavioural insight distinction
- Added method selection by development stage
- Added AI-assisted method selection governance
- Added drift protection against method-first research