daffy0208/ai-dev-standards/user-researcher

User Researcher

Understand user needs through systematic research before building products.

Core Principle

Users are not you. Validate assumptions with real user behavior, not opinions or what users say they'll do.

5-Phase User Research Process

Phase 1: Research Planning

Goal: Define what you need to learn and how

Activities:

• Define research objectives (2-4 key questions to answer)
• Identify target user segments and recruitment criteria
• Select research methods (interviews, surveys, observation)
• Prepare interview guides or survey questions
• Define sample size (5-12 per segment for qualitative)

Research Questions Examples:

• What are users' current workflows for [task]?
• What pain points do users experience with [current solution]?
• What motivates users to switch from current solution?
• How do users make decisions about [domain]?

Validation:

• [ ] Research objectives documented
• [ ] Target segments defined with criteria
• [ ] Methods selected with protocols ready
• [ ] Stakeholder buy-in obtained

Phase 2: User Recruitment

Goal: Find and schedule representative participants

Recruitment Sources:

• Existing customers (in-app recruiting, email)
• Prospect lists (sales leads, newsletter subscribers)
• User research platforms (UserTesting, Respondent.io)
• Social media and communities (LinkedIn, Reddit, Slack)
• Referrals from existing participants

Screening Criteria:

• Role or job title
• Experience level (novice, intermediate, expert)
• Use case relevance
• Tool stack (current solutions used)
• Willingness to participate (time commitment)

Compensation:

• B2B: $75-150 for 30-60 min interview
• B2C: $25-50 for 30-60 min interview
• Gift cards are easier than cash transfers

Sample Size:

• Qualitative: 5-12 participants per segment
• Quantitative: 50-100 minimum for statistical significance
• Stop when you reach saturation (no new insights)

Validation:

• [ ] 5-12 participants recruited per segment
• [ ] Diverse representation (include edge cases, power users)
• [ ] Sessions scheduled with consent forms sent
• [ ] Compensation method arranged

Phase 3: Data Collection

Goal: Gather rich user insights through chosen methods

User Interviews (Primary method):

Interview Structure (30-60 minutes):

1. Intro (5 min): Build rapport, explain purpose
2. Context (10 min): Role, current workflow, tools
3. Deep Dive (30 min): Pain points, needs, behaviors
4. Wrap-up (5 min): Questions, next steps

Good Interview Questions:

✅ Open-ended:
- "Tell me about the last time you [task]."
- "Walk me through your process for [activity]."
- "What's the most frustrating part of [workflow]?"
- "How do you currently solve [problem]?"
 
❌ Leading questions (avoid):
- "Would you use a feature that...?" (Everyone says yes)
- "Don't you think it would be better if...?" (Confirming bias)
- "How much would you pay for this?" (Hypothetical)

Ask "Why" Five Times:

User: "I use Excel for tracking leads."
You: "Why Excel specifically?"
User: "It's what I know."
You: "Why is familiarity important?"
User: "Learning new tools takes time."
You: "Why is time a concern?"
User: "I'm measured on closed deals, not tool expertise."
→ Root insight: Avoid tools with steep learning curves

Contextual Inquiry:

• Observe users in their natural environment
• Watch them complete actual tasks (not simulated)
• Note workarounds, frustrations, and hacks
• Take photos of physical workspace, sticky notes, checklists

Surveys (for quantitative validation):

• Use for validating qualitative findings at scale
• Mix closed (rating scales) and open-ended questions
• Keep under 10 questions (completion rate drops fast)
• Target 50-100+ responses for statistical significance

Validation:

• [ ] All sessions recorded (with permission)
• [ ] Notes taken during or immediately after
• [ ] Artifacts collected (screenshots, workflows)
• [ ] Early patterns emerging

Phase 4: Analysis & Synthesis

Goal: Identify patterns, themes, and insights from raw data

Affinity Diagramming:

1. Write each insight on a sticky note
2. Group similar notes together
3. Label groups with themes
4. Look for patterns across groups

Common Themes to Look For:

• Pain points (frequent frustrations)
• Workarounds (hacks users created)
• Unmet needs (things users wish existed)
• Behavioral patterns (how users actually work)
• Decision criteria (what influences choices)

Jobs-to-be-Done (JTBD) Framework:

When [situation],
I want to [motivation],
So I can [expected outcome].
 
Example:
When preparing for a client meeting,
I want to quickly find all previous conversations,
So I can provide personalized recommendations without looking unprepared.
 
Analysis:
- Functional job: Find information quickly
- Emotional job: Appear competent
- Social job: Demonstrate attentiveness

User Segmentation (by behavior, not demographics):

• Power users vs. casual users
• Early adopters vs. late majority
• DIY vs. managed service preference
• Price-sensitive vs. value-focused

Validation:

• [ ] Data transcribed and coded
• [ ] Themes identified across participants
• [ ] Patterns validated (not one-off comments)
• [ ] Behavioral segments defined

Phase 5: Research Deliverables

Goal: Communicate findings in actionable formats

1. User Personas (3-5 evidence-based profiles):

persona_name: 'Sarah the Sales Manager'
role: 'Regional Sales Manager'
demographics:
  experience_level: 'Intermediate (5 years)'
  team_size: '12 sales reps'
goals:
  - Track team performance in real-time
  - Coach underperforming reps effectively
pain_points:
  - Data scattered across 3 systems
  - Can't see at-risk deals until too late
current_tools:
  - 'Salesforce: CRM tracking'
  - 'Excel: Custom reports (2 hrs/week)'
behaviors:
  - Checks dashboard first thing every morning
  - Spends 2 hours weekly compiling reports manually
quote: "I feel like I'm flying blind until the end of the quarter"
opportunity: 'Unified dashboard with predictive risk scoring'

2. Journey Maps (current-state experience):

Stages: Awareness → Research → Purchase → Onboarding → Usage → Support
 
For each stage:
- Actions: What users do
- Pain points: Frustrations and blockers
- Emotions: How users feel (frustrated, confident, confused)
- Opportunities: Where to improve

3. Research Report:

• Executive summary (1-page findings)
• Methodology (how research was conducted)
• Key insights (5-10 most important findings)
• Supporting quotes (evidence from users)
• Recommendations (what to build or change)
• Appendix (full data, transcripts)

4. Opportunity Areas (prioritized problems):

| Opportunity | Impact | Effort | Priority |
|-------------|--------|--------|----------|
| Unified dashboard | High | Medium | P0 |
| Predictive alerts | High | High | P1 |
| Mobile access | Medium | Low | P1 |

Validation:

• [ ] 3-5 personas created with evidence
• [ ] Journey maps show pain points
• [ ] Research report written and shared
• [ ] Opportunities prioritized with team
• [ ] Artifacts stored in shared repository

Key Research Principles

1. Observe Behavior, Not Just Words

What users do > what they say they do > what they say they'll do

2. Ask "Why" Five Times

Surface root causes and motivations, not symptoms

3. Recruit for Diversity

Include edge cases, power users, and struggling users—not just ideal customers

4. No Leading Questions

Ask "Tell me about..." not "Would you like..."

5. Research is Continuous

Not a one-time phase—continue throughout product lifecycle

6. Validate Assumptions Early

Test riskiest assumptions first with minimal investment

Research Methods by Stage

Exploratory (Early Discovery)

• User interviews: 1-on-1 conversations about context and pain points
• Contextual inquiry: Observe users in natural environment
• Diary studies: Users record experiences over days/weeks

Evaluative (Testing Ideas)

• Concept testing: Show mockups, gather reactions
• Usability testing: Watch users attempt tasks with prototypes
• A/B testing: Compare variants with real usage data

Quantitative (Validation at Scale)

• Surveys: Validate findings across larger populations
• Analytics: Track behavior patterns in existing products
• Card sorting: Understand how users categorize information

Common Research Mistakes

❌ Talking to friends and family → They'll tell you what you want to hear ❌ Asking hypothetical questions → "Would you use...?" is not predictive ❌ Leading questions → "Don't you think...?" confirms your bias ❌ Only talking to early adopters → They're not representative ❌ Skipping synthesis → Raw data isn't insights ❌ Ignoring negative feedback → Pay extra attention to criticism ❌ One-time research → User needs change, research continuously

Research Outputs Template

research_summary:
  objectives:
    - '<key question 1>'
    - '<key question 2>'
  participants:
    total: <number>
    segments:
      - name: '<segment>'
        count: <number>
  methods:
    - 'User interviews (12 participants)'
    - 'Survey (87 responses)'
  key_insights:
    - insight: '<finding>'
      evidence: '<quote or data>'
      impact: 'high/medium/low'
  personas:
    - name: '<persona name>'
      goals: ['<goal>']
      pain_points: ['<pain>']
  opportunities:
    - opportunity: '<problem to solve>'
      impact: 'high'
      effort: 'medium'
      priority: 'P0'
  recommendations:
    - '<action item 1>'
    - '<action item 2>'

Related Resources

Related Skills:

• product-strategist - For validating product-market fit
• ux-designer - For creating designs based on research
• mvp-builder - For prioritizing features from research

Related Patterns:

• META/DECISION-FRAMEWORK.md - Research method selection
• STANDARDS/best-practices/user-research-ethics.md - Research ethics (when created)

Related Playbooks:

• PLAYBOOKS/conduct-user-interviews.md - Interview procedure (when created)
• PLAYBOOKS/synthesize-research-findings.md - Analysis workflow (when created)