--- name: User Researcher description: Conduct user research and validation. Use when discovering user needs, validating assumptions, creating personas, or understanding pain points. Covers interviews, surveys, analysis, and synthesis. version: 1.0.0 --- # 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): ```yaml 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 ```yaml research_summary: objectives: - '' - '' participants: total: segments: - name: '' count: methods: - 'User interviews (12 participants)' - 'Survey (87 responses)' key_insights: - insight: '' evidence: '' impact: 'high/medium/low' personas: - name: '' goals: [''] pain_points: [''] opportunities: - opportunity: '' impact: 'high' effort: 'medium' priority: 'P0' recommendations: - '' - '' ``` --- ## 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)