Discovery Interviews & Surveys

Table of Contents

• Purpose

• When to Use

• What Is It?

• Workflow

• Common Patterns

• Guardrails

• Quick Reference

Purpose

Discovery Interviews & Surveys help you learn from users systematically to:

• Validate assumptions before investing in building

• Discover real problems users experience (not just stated needs)

• Understand jobs-to-be-done (what users "hire" your product to do)

• Identify pain points and current workarounds

• Test concepts and positioning with target audience

• Uncover unmet needs that users may not articulate directly

This moves from guessing to evidence-based product decisions.

When to Use

Use this skill when:

• Pre-build validation: Testing product ideas before development

• Problem discovery: Understanding user pain points and workflows

• Jobs-to-be-done research: Identifying hiring/firing triggers and desired outcomes

• Market research: Understanding target audience, competitive landscape, willingness to pay

• Concept testing: Validating positioning, messaging, feature prioritization

• Post-launch learning: Understanding adoption barriers, churn reasons, expansion opportunities

• Customer satisfaction research: Identifying satisfaction/dissatisfaction drivers

• UX research: Mental models, task flows, usability issues

• Voice of customer: Gathering qualitative insights for roadmap prioritization

Trigger phrases: "user research", "customer interviews", "surveys", "discovery", "validation study", "voice of customer", "jobs-to-be-done", "JTBD", "user needs"

What Is It?

Discovery Interviews & Surveys provide structured approaches to learn from users while avoiding common biases (leading questions, confirmation bias, selection bias).

Key components:

• Interview guides: Open-ended questions that reveal problems and context

• Survey instruments: Scaled questions for quantitative validation at scale

• JTBD probes: Questions focused on hiring/firing triggers and desired outcomes

• Bias-avoidance techniques: Past behavior focus, "show me" requests, avoiding hypotheticals

• Analysis frameworks: Thematic coding, affinity mapping, statistical analysis

Quick example:

Bad interview question (leading, hypothetical): "Would you pay $49/month for a tool that automatically backs up your files?"

Good interview approach (behavior-focused, problem-discovery):

• "Tell me about the last time you lost important files. What happened?"

• "What have you tried to prevent data loss? How's that working?"

• "Walk me through your current backup process. Show me if possible."

• "What would need to change for you to invest time/money in better backup?"

Result: Learn about actual problems, current solutions, willingness to change—not hypothetical preferences.

Workflow

Copy this checklist and track your progress:

Discovery Research Progress:

• Step 1: Define research objectives and hypotheses
• Step 2: Identify target participants
• Step 3: Choose research method (interviews, surveys, or both)
• Step 4: Design research instruments
• Step 5: Conduct research and collect data
• Step 6: Analyze findings and extract insights

Step 1: Define research objectives

Specify what you're trying to learn, key hypotheses to test, success criteria for research, and decision to be informed. See Common Patterns for typical objectives.

Step 2: Identify target participants

Define participant criteria (demographics, behaviors, firmographics), sample size needed, recruitment strategy, and screening questions. For sampling strategies, see resources/methodology.md.

Step 3: Choose research method

Based on objective and constraints:

• For deep problem discovery (5-15 participants) → Use resources/template.md for in-depth interviews

• For concept testing at scale (50-200+ participants) → Use resources/template.md for quantitative validation

• For JTBD research → Use resources/methodology.md for switch interviews

• For mixed methods → Interviews for discovery, surveys for validation

Step 4: Design research instruments

Create interview guide or survey with bias-avoidance techniques. Use resources/template.md for structure. Avoid leading questions, focus on past behavior, use "show me" requests. For advanced question design, see resources/methodology.md.

Step 5: Conduct research

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 for critical requirements.

Step 6: Analyze findings

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. Minimum standard: Average score ≥ 3.5.

Common Patterns

Pattern 1: Problem Discovery Interviews

• Objective: Understand user pain points and current workflows

• Approach: 8-12 in-depth interviews, open-ended questions, focus on past behavior and actual solutions

• Key questions: "Tell me about the last time...", "Walk me through...", "What have you tried?", "How's that working?"

• Output: Problem themes, frequency estimates, current workarounds, willingness to change

• Example: B2B SaaS discovery—interview potential customers about current tools and pain points

Pattern 2: Jobs-to-be-Done Research

• Objective: Identify why users "hire" products and what triggers switching

• Approach: Switch interviews with recent adopters or switchers, focus on timeline and context

• Key questions: "What prompted you to look?", "What alternatives did you consider?", "What almost stopped you?", "What's different now?"

• Output: Hiring triggers, firing triggers, desired outcomes, anxieties, habits

• Example: SaaS churn research—interview recent churners about switch to competitor

Pattern 3: Concept Testing (Qualitative)

• Objective: Test product concepts, positioning, or messaging before launch

• Approach: 10-15 interviews showing concept (mockup, landing page, description), gather reactions

• 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?"

• Output: Comprehension score, perceived value, target audience clarity, pricing anchors

• Example: Pre-launch validation—test landing page messaging with target audience

Pattern 4: Survey for Quantitative Validation

• Objective: Validate findings from interviews at scale or prioritize features

• Approach: 100-500 participants, mix of scaled questions (Likert, ranking) and open-ends

• Key questions: Satisfaction scores (CSAT, NPS), feature importance/satisfaction (Kano), usage frequency, demographics

• Output: Statistical significance, segmentation, prioritization (importance vs satisfaction matrix)

• Example: Product roadmap prioritization—survey 500 users on feature importance

Pattern 5: Continuous Discovery

• Objective: Ongoing learning, not one-time project

• Approach: Weekly customer conversations (15-30 min), rotating team members, shared notes

• Key questions: Varies by current focus (new features, onboarding, expansion, retention)

• Output: Continuous insight feed, early problem detection, relationship building

• Example: Product team does 3-5 customer calls weekly, logs insights in shared doc

Guardrails

Critical requirements:

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?"

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."

Use "show me" not "tell me": Actual behavior > described behavior. Ask to screen-share, demonstrate current workflow, show artifacts (spreadsheets, tools).

Recruit right participants: Screen carefully. Wrong participants = wasted time. Define inclusion/exclusion criteria, use screening survey.

Sample size appropriate for method: Interviews: 5-15 for themes to emerge. Surveys: 100+ for statistical significance, 30+ per segment if comparing.

Avoid confirmation bias: Actively look for disconfirming evidence. If 9/10 interviews support hypothesis, focus heavily on the 1 that doesn't.

Record and transcribe (with permission): Memory is unreliable. Record interviews, transcribe for analysis. Take notes as backup.

Analyze systematically: Don't cherry-pick quotes that support preferred conclusion. Use thematic coding, count themes, present contradictory evidence.

Common pitfalls:

• ❌ Asking "would you" questions: Hypotheticals are unreliable. Focus on "have you", "tell me about when", "show me"

• ❌ Small sample statistical claims: "80% of users want feature X" from 5 interviews is not valid. Interviews = themes, surveys = statistics

• ❌ Selection bias: Interviewing only enthusiasts or only detractors skews results. Recruit diverse sample

• ❌ Ignoring non-verbal cues: Hesitation, confusion, workarounds during "show me" reveal truth beyond words

• ❌ Stopping at surface answers: First answer is often rationalization. Follow up: "Tell me more", "Why did that matter?", "What else?"

Quick Reference

Key resources:

• resources/template.md: Interview guide template, survey template, JTBD question bank, screening questions

• resources/methodology.md: Advanced techniques (JTBD switch interviews, Kano analysis, thematic coding, statistical analysis, continuous discovery)

• resources/evaluators/rubric_discovery_interviews_surveys.json: Quality criteria for research design and execution

Typical workflow time:

• Interview guide design: 1-2 hours

• Conducting 10 interviews: 10-15 hours (including scheduling)

• Analysis and synthesis: 4-8 hours

• Survey design: 2-4 hours

• Survey distribution and collection: 1-2 weeks

• Survey analysis: 2-4 hours

When to escalate:

• Large-scale quantitative studies (1000+ participants)

• Statistical modeling or advanced segmentation

• Longitudinal studies (tracking over time)

• Ethnographic research (observing in natural setting) → Use resources/methodology.md or consider specialist researcher

Inputs required:

• Research objective: What you're trying to learn

• Hypotheses (optional): Specific beliefs to test

• Target persona: Who to interview/survey

• Job-to-be-done (optional): Specific JTBD focus

Outputs produced:

• discovery-interviews-surveys.md : Complete research plan with interview guide or survey, recruitment criteria, analysis plan, and insights template