211 lines
12 KiB
Markdown
211 lines
12 KiB
Markdown
---
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name: discovery-interviews-surveys
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description: Use when validating product assumptions before building, discovering unmet user needs, understanding customer problems and workflows, testing concepts or positioning, researching target markets, identifying jobs-to-be-done and hiring triggers, uncovering pain points and workarounds, or when users mention user research, customer interviews, surveys, discovery interviews, validation studies, or voice of customer.
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---
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# Discovery Interviews & Surveys
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## Table of Contents
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- [Purpose](#purpose)
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- [When to Use](#when-to-use)
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- [What Is It?](#what-is-it)
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- [Workflow](#workflow)
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- [Common Patterns](#common-patterns)
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- [Guardrails](#guardrails)
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- [Quick Reference](#quick-reference)
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## Purpose
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Discovery Interviews & Surveys help you learn from users systematically to:
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- **Validate assumptions** before investing in building
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- **Discover real problems** users experience (not just stated needs)
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- **Understand jobs-to-be-done** (what users "hire" your product to do)
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- **Identify pain points** and current workarounds
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- **Test concepts** and positioning with target audience
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- **Uncover unmet needs** that users may not articulate directly
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This moves from guessing to evidence-based product decisions.
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## When to Use
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Use this skill when:
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- **Pre-build validation**: Testing product ideas before development
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- **Problem discovery**: Understanding user pain points and workflows
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- **Jobs-to-be-done research**: Identifying hiring/firing triggers and desired outcomes
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- **Market research**: Understanding target audience, competitive landscape, willingness to pay
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- **Concept testing**: Validating positioning, messaging, feature prioritization
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- **Post-launch learning**: Understanding adoption barriers, churn reasons, expansion opportunities
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- **Customer satisfaction research**: Identifying satisfaction/dissatisfaction drivers
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- **UX research**: Mental models, task flows, usability issues
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- **Voice of customer**: Gathering qualitative insights for roadmap prioritization
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Trigger phrases: "user research", "customer interviews", "surveys", "discovery", "validation study", "voice of customer", "jobs-to-be-done", "JTBD", "user needs"
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## What Is It?
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Discovery Interviews & Surveys provide structured approaches to learn from users while avoiding common biases (leading questions, confirmation bias, selection bias).
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**Key components**:
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1. **Interview guides**: Open-ended questions that reveal problems and context
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2. **Survey instruments**: Scaled questions for quantitative validation at scale
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3. **JTBD probes**: Questions focused on hiring/firing triggers and desired outcomes
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4. **Bias-avoidance techniques**: Past behavior focus, "show me" requests, avoiding hypotheticals
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5. **Analysis frameworks**: Thematic coding, affinity mapping, statistical analysis
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**Quick example:**
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**Bad interview question** (leading, hypothetical):
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"Would you pay $49/month for a tool that automatically backs up your files?"
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**Good interview approach** (behavior-focused, problem-discovery):
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1. "Tell me about the last time you lost important files. What happened?"
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2. "What have you tried to prevent data loss? How's that working?"
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3. "Walk me through your current backup process. Show me if possible."
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4. "What would need to change for you to invest time/money in better backup?"
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**Result**: Learn about actual problems, current solutions, willingness to change—not hypothetical preferences.
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## Workflow
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Copy this checklist and track your progress:
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```
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Discovery Research Progress:
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- [ ] Step 1: Define research objectives and hypotheses
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- [ ] Step 2: Identify target participants
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- [ ] Step 3: Choose research method (interviews, surveys, or both)
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- [ ] Step 4: Design research instruments
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- [ ] Step 5: Conduct research and collect data
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- [ ] Step 6: Analyze findings and extract insights
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```
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**Step 1: Define research objectives**
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Specify what you're trying to learn, key hypotheses to test, success criteria for research, and decision to be informed. See [Common Patterns](#common-patterns) for typical objectives.
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**Step 2: Identify target participants**
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Define participant criteria (demographics, behaviors, firmographics), sample size needed, recruitment strategy, and screening questions. For sampling strategies, see [resources/methodology.md](resources/methodology.md#participant-recruitment).
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**Step 3: Choose research method**
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Based on objective and constraints:
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- **For deep problem discovery (5-15 participants)** → Use [resources/template.md](resources/template.md#interview-guide-template) for in-depth interviews
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- **For concept testing at scale (50-200+ participants)** → Use [resources/template.md](resources/template.md#survey-template) for quantitative validation
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- **For JTBD research** → Use [resources/methodology.md](resources/methodology.md#jobs-to-be-done-interviews) for switch interviews
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- **For mixed methods** → Interviews for discovery, surveys for validation
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**Step 4: Design research instruments**
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Create interview guide or survey with bias-avoidance techniques. Use [resources/template.md](resources/template.md) for structure. Avoid leading questions, focus on past behavior, use "show me" requests. For advanced question design, see [resources/methodology.md](resources/methodology.md#question-design-principles).
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**Step 5: Conduct research**
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Execute interviews (record with permission, take notes) or distribute surveys (pilot test first). Use proper techniques (active listening, follow-up probes, silence for thinking). See [Guardrails](#guardrails) for critical requirements.
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**Step 6: Analyze findings**
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For interviews: thematic coding, affinity mapping, quote extraction. For surveys: statistical analysis, cross-tabs, open-end coding. Create insights document with evidence. Self-assess using [resources/evaluators/rubric_discovery_interviews_surveys.json](resources/evaluators/rubric_discovery_interviews_surveys.json). **Minimum standard**: Average score ≥ 3.5.
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## Common Patterns
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**Pattern 1: Problem Discovery Interviews**
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- **Objective**: Understand user pain points and current workflows
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- **Approach**: 8-12 in-depth interviews, open-ended questions, focus on past behavior and actual solutions
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- **Key questions**: "Tell me about the last time...", "Walk me through...", "What have you tried?", "How's that working?"
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- **Output**: Problem themes, frequency estimates, current workarounds, willingness to change
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- **Example**: B2B SaaS discovery—interview potential customers about current tools and pain points
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**Pattern 2: Jobs-to-be-Done Research**
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- **Objective**: Identify why users "hire" products and what triggers switching
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- **Approach**: Switch interviews with recent adopters or switchers, focus on timeline and context
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- **Key questions**: "What prompted you to look?", "What alternatives did you consider?", "What almost stopped you?", "What's different now?"
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- **Output**: Hiring triggers, firing triggers, desired outcomes, anxieties, habits
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- **Example**: SaaS churn research—interview recent churners about switch to competitor
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**Pattern 3: Concept Testing (Qualitative)**
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- **Objective**: Test product concepts, positioning, or messaging before launch
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- **Approach**: 10-15 interviews showing concept (mockup, landing page, description), gather reactions
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- **Key questions**: "In your own words, what is this?", "Who is this for?", "What would you use it for?", "How much would you expect to pay?"
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- **Output**: Comprehension score, perceived value, target audience clarity, pricing anchors
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- **Example**: Pre-launch validation—test landing page messaging with target audience
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**Pattern 4: Survey for Quantitative Validation**
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- **Objective**: Validate findings from interviews at scale or prioritize features
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- **Approach**: 100-500 participants, mix of scaled questions (Likert, ranking) and open-ends
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- **Key questions**: Satisfaction scores (CSAT, NPS), feature importance/satisfaction (Kano), usage frequency, demographics
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- **Output**: Statistical significance, segmentation, prioritization (importance vs satisfaction matrix)
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- **Example**: Product roadmap prioritization—survey 500 users on feature importance
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**Pattern 5: Continuous Discovery**
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- **Objective**: Ongoing learning, not one-time project
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- **Approach**: Weekly customer conversations (15-30 min), rotating team members, shared notes
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- **Key questions**: Varies by current focus (new features, onboarding, expansion, retention)
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- **Output**: Continuous insight feed, early problem detection, relationship building
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- **Example**: Product team does 3-5 customer calls weekly, logs insights in shared doc
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## Guardrails
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**Critical requirements:**
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1. **Avoid leading questions**: Don't telegraph the "right" answer. Bad: "Don't you think our UI is confusing?" Good: "Walk me through using this feature. What happened?"
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2. **Focus on past behavior, not hypotheticals**: What people did reveals truth; what they say they'd do is often wrong. Bad: "Would you use this feature?" Good: "Tell me about the last time you needed to do X."
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3. **Use "show me" not "tell me"**: Actual behavior > described behavior. Ask to screen-share, demonstrate current workflow, show artifacts (spreadsheets, tools).
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4. **Recruit right participants**: Screen carefully. Wrong participants = wasted time. Define inclusion/exclusion criteria, use screening survey.
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5. **Sample size appropriate for method**: Interviews: 5-15 for themes to emerge. Surveys: 100+ for statistical significance, 30+ per segment if comparing.
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6. **Avoid confirmation bias**: Actively look for disconfirming evidence. If 9/10 interviews support hypothesis, focus heavily on the 1 that doesn't.
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7. **Record and transcribe (with permission)**: Memory is unreliable. Record interviews, transcribe for analysis. Take notes as backup.
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8. **Analyze systematically**: Don't cherry-pick quotes that support preferred conclusion. Use thematic coding, count themes, present contradictory evidence.
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**Common pitfalls:**
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- ❌ **Asking "would you" questions**: Hypotheticals are unreliable. Focus on "have you", "tell me about when", "show me"
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- ❌ **Small sample statistical claims**: "80% of users want feature X" from 5 interviews is not valid. Interviews = themes, surveys = statistics
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- ❌ **Selection bias**: Interviewing only enthusiasts or only detractors skews results. Recruit diverse sample
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- ❌ **Ignoring non-verbal cues**: Hesitation, confusion, workarounds during "show me" reveal truth beyond words
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- ❌ **Stopping at surface answers**: First answer is often rationalization. Follow up: "Tell me more", "Why did that matter?", "What else?"
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## Quick Reference
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**Key resources:**
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- **[resources/template.md](resources/template.md)**: Interview guide template, survey template, JTBD question bank, screening questions
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- **[resources/methodology.md](resources/methodology.md)**: Advanced techniques (JTBD switch interviews, Kano analysis, thematic coding, statistical analysis, continuous discovery)
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- **[resources/evaluators/rubric_discovery_interviews_surveys.json](resources/evaluators/rubric_discovery_interviews_surveys.json)**: Quality criteria for research design and execution
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**Typical workflow time:**
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- Interview guide design: 1-2 hours
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- Conducting 10 interviews: 10-15 hours (including scheduling)
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- Analysis and synthesis: 4-8 hours
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- Survey design: 2-4 hours
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- Survey distribution and collection: 1-2 weeks
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- Survey analysis: 2-4 hours
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**When to escalate:**
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- Large-scale quantitative studies (1000+ participants)
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- Statistical modeling or advanced segmentation
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- Longitudinal studies (tracking over time)
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- Ethnographic research (observing in natural setting)
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→ Use [resources/methodology.md](resources/methodology.md) or consider specialist researcher
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**Inputs required:**
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- **Research objective**: What you're trying to learn
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- **Hypotheses** (optional): Specific beliefs to test
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- **Target persona**: Who to interview/survey
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- **Job-to-be-done** (optional): Specific JTBD focus
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**Outputs produced:**
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- `discovery-interviews-surveys.md`: Complete research plan with interview guide or survey, recruitment criteria, analysis plan, and insights template
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