MWMS MCR

Research Brain User Interview And Survey Framework

vSystem: 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 User Interview And Survey Framework defines how MWMS plans, conducts, validates, and operationalizes interviews and surveys in order to gather reliable attitudinal insight from users, customers, buyers, leads, subscribers, and audiences.

This framework exists to ensure that MWMS:

  • gathers useful qualitative and quantitative insight
  • avoids biased questioning
  • avoids assumption confirmation
  • understands customer language and motivations
  • validates patterns at scale
  • separates beliefs from behaviour
  • produces operationally useful research intelligence

The framework standardizes:

  • interview design
  • survey design
  • participant selection
  • questioning structure
  • moderation behaviour
  • insight extraction
  • synthesis standards
  • operational routing

Scope

This framework applies to:

  • user interviews
  • customer interviews
  • buyer interviews
  • exploratory interviews
  • onboarding interviews
  • churn interviews
  • sales discovery interviews
  • survey research
  • validation surveys
  • satisfaction surveys
  • segmentation surveys
  • VOC collection systems
  • AI-assisted transcript analysis

This framework supports:

  • Affiliate Brain
  • Customer Brain
  • Conversion Brain
  • Product Brain
  • Content Brain
  • Offer Brain
  • Strategy Brain
  • HeadOffice Intelligence

Core Operating Principle

Interviews and surveys exist to understand:

  • beliefs
  • perceptions
  • motivations
  • language
  • frustrations
  • goals
  • emotional drivers
  • decision criteria

They do not reliably reveal actual behaviour on their own.

MWMS must distinguish between:

  • what users say
    and
  • what users actually do

Attitudinal insight is valuable, but incomplete without behavioural validation.

Interview Versus Survey Rule

MWMS separates interviews and surveys by purpose.

Interviews

Best for:

  • deep understanding
  • discovering unknowns
  • emotional insight
  • decision reasoning
  • behavioural stories
  • uncovering hidden friction
  • learning user language

Interviews are exploratory.

Surveys

Best for:

  • validating patterns at scale
  • quantifying known issues
  • segmentation
  • prioritization
  • measuring prevalence
  • comparing groups

Surveys are validation-oriented.

Interview Operating Model

Goal

Interviews are designed to uncover:

  • user motivations
  • emotional drivers
  • frustrations
  • workflows
  • expectations
  • beliefs
  • unmet needs
  • language patterns

Interview Structure

MWMS interviews generally follow:

1. Warm-Up

Establish comfort and context.

Examples:

  • Tell me about your role.
  • How do you currently handle this?
  • How often do you do this?

Goal:
reduce pressure and build context.

2. Behavioural Exploration

Focus on real events and actions.

Examples:

  • Tell me about the last time you…
  • Walk me through how you…
  • What happened when…?

Goal:
understand actual experiences.

3. Friction And Emotional Discovery

Identify pain points and emotional signals.

Examples:

  • What was frustrating?
  • What confused you?
  • What worried you?
  • What slowed you down?

Goal:
identify friction and emotional drivers.

4. Decision Understanding

Understand choice and prioritization.

Examples:

  • Why did you choose that option?
  • What alternatives did you consider?
  • What mattered most?

Goal:
understand decision criteria.

5. Reflection And Opportunity

Capture unmet needs and improvement ideas.

Examples:

  • What would make this easier?
  • What do you wish existed?
  • What would improve confidence?

Goal:
identify opportunities and unmet expectations.

Interview Question Rules

Rule 1 — Avoid Leading Questions

Bad:

“Did you find the onboarding confusing?”

Better:

“How did the onboarding experience feel?”

Rule 2 — Avoid Yes/No Questions

Bad:

“Did you like it?”

Better:

“What stood out to you about the experience?”

Rule 3 — Focus On Past Behaviour

Bad:

“Would you use this?”

Better:

“Tell me about the last time you solved this problem.”

Rule 4 — Use Neutral Language

Avoid emotionally loaded phrasing.

The interviewer must not push users toward a preferred answer.

Rule 5 — Allow Silence

Users often reveal deeper insight after pauses.

The interviewer should not rush to fill silence.

Rule 6 — Ask Follow-Up Questions

Good research often comes from follow-up exploration.

Examples:

  • Why was that important?
  • Can you explain further?
  • What happened next?
  • How did that affect your decision?

Survey Operating Model

Goal

Surveys validate patterns across larger groups.

Surveys help answer:

  • how many
  • how often
  • which group
  • which preference
  • which priority
  • which problem is most common

Survey Design Principles

Surveys Must Stay Focused

Surveys should only include questions tied to a real decision.

MWMS avoids bloated surveys.

Questions Must Be Clear

Avoid:

  • jargon
  • ambiguity
  • double meanings
  • technical phrasing
  • compound questions

One Question = One Concept

Bad:

“How satisfied were you with the speed and support quality?”

Better:

Separate questions.

Surveys Should Avoid Prediction Questions

Bad:

“Would you buy this in the future?”

Better:

“What factors matter most when choosing this type of product?”

Use Open Questions Sparingly

Open questions are useful but harder to analyze at scale.

Best use:

  • final comments
  • pain-point capture
  • language gathering
  • unmet need discovery

Survey Question Types

Multiple Choice

Best for:

  • segmentation
  • prioritization
  • classification

Rating Scales

Best for:

  • satisfaction
  • confidence
  • clarity
  • difficulty
  • agreement

Ranking Questions

Best for:

  • priority understanding
  • trade-off analysis

Open-Ended Questions

Best for:

  • emotional insight
  • language patterns
  • hidden problems
  • unmet needs

Participant Selection Rules

Research quality depends heavily on participant quality.

MWMS participant selection must match:

  • the business goal
  • the research question
  • the user segment
  • the decision context

Participant Types

Examples:

  • first-time users
  • repeat customers
  • churned users
  • high-value buyers
  • technical users
  • non-technical users
  • mobile users
  • affiliate traffic users
  • paid traffic users

Recruitment Rules

MWMS must avoid:

  • recruiting only easy-to-reach users
  • interviewing internal staff as substitutes
  • overusing loyal users
  • relying on friends/family
  • selecting users who only confirm existing beliefs

Moderation Rules

Interviewers must:

  • remain neutral
  • avoid defending the product
  • avoid teaching users
  • avoid correcting users
  • avoid selling during interviews

The interviewer’s role is understanding, not persuasion.

Behavioural Reality Rule

Attitudinal research has limits.

Users may:

  • forget actions
  • rationalize choices
  • simplify experiences
  • describe ideal behaviour instead of real behaviour

Therefore:

interviews and surveys should often be combined with:

  • usability testing
  • analytics
  • behavioural observation
  • session recordings
  • experimentation

Insight Extraction Standards

MWMS interview and survey analysis should extract:

  • pain points
  • emotional signals
  • recurring language
  • decision drivers
  • trust factors
  • confusion points
  • objections
  • unmet needs
  • behavioural indicators
  • segmentation patterns

Synthesis Rules

Raw answers are not intelligence.

MWMS converts responses into:

  • themes
  • patterns
  • friction clusters
  • opportunity signals
  • behavioural insights
  • positioning insights
  • prioritization recommendations

AI Assisted Interview And Survey Analysis

AI may assist with:

  • transcript summaries
  • open-ended response clustering
  • pain-point extraction
  • recurring-language analysis
  • sentiment grouping
  • theme categorization

AI must not:

  • replace interpretation
  • invent themes
  • ignore sample quality
  • remove contradictory evidence
  • decide strategic direction automatically

Human review remains mandatory.

Research Outputs

Interview and survey outputs may generate:

  • persona updates
  • onboarding improvements
  • offer insights
  • content angles
  • positioning improvements
  • messaging refinements
  • conversion opportunities
  • behavioural hypotheses
  • test ideas
  • strategic recommendations

Governance Role

Research Brain governs:

  • interview standards
  • survey standards
  • participant quality
  • moderation integrity
  • synthesis quality
  • operational routing

HeadOffice governs:

  • strategic prioritization
  • cross-Brain operationalization
  • escalation of major findings

Relationship To Other MWMS Standards

This framework supports:

  • Research Brain User Research Operating Framework
  • Research Brain Research Question And Method Selection Framework
  • Research Brain Behavioural Testing And Observation Framework
  • Research Brain Research Synthesis And Deliverables Framework
  • Customer Brain Persona Intelligence
  • Conversion Brain Funnel Analysis
  • Offer Brain Validation Systems
  • HeadOffice Intelligence Layer

Drift Protection

MWMS must prevent:

  • leading interviews
  • survey bias
  • asking predictive behaviour questions
  • overreliance on opinion-based research
  • interviewing the wrong participant groups
  • bloated surveys
  • confirmation-bias interviewing
  • AI-generated fake synthesis
  • research disconnected from business goals

Architectural Intent

This framework establishes interviews and surveys as structured operational intelligence systems inside MWMS.

The intent is to ensure that:

  • customer understanding improves continuously
  • user language becomes reusable intelligence
  • assumptions are challenged
  • strategic decisions improve
  • research produces operational value
  • attitudinal insight strengthens behavioural systems

The framework ensures that user understanding becomes a reusable asset across the MWMS ecosystem.

Change Log

v1.0

  • Created User Interview And Survey Framework
  • Added interview structure model
  • Added survey governance standards
  • Added participant selection rules
  • Added moderation standards
  • Added behavioural reality rule
  • Added AI-assisted analysis governance
  • Added synthesis and operational routing standards