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This user interview script generator is for product discovery and user research interviews, not hiring interviews. It helps you design decision-grade customer discovery interview guides for B2B and B2C contexts, with story-first, non-leading questions and a consistent structure you can reuse.
Instead of starting from a blank page or a generic question list, you get a complete user research discussion guide that adapts to your goal, segment, duration, and interview mode so every session produces higher-signal insights for product decisions.
No login. Runs in your browser. We do not store your inputs.
No login. Runs in your browser. We do not store your inputs.
Interview type
Duration
Interview mode
This output always includes: header, opening, warm-up, core sections, optional B2B module, probes library, leading-question anti-patterns, wrap-up, note-taking template, and interview snapshot.
Click Generate or load an example to create a decision-grade interview script. The full guide will appear here for copy or export.
Loading saved interview state...
Step 1: Set the context
Choose B2B or B2C, pick a goal like problem discovery, churn, win/loss, pricing, concept test, or usability test, then add a short product context, target audience, stage, duration, and interview mode. This keeps the guide grounded in a clear decision and avoids one-size-fits-all scripts.
Step 2: Generate a story-first guide
Click Generate (or load one of the three examples) to create a full user research discussion guide. The output always includes opening script, warm-up, goal-specific core sections, optional B2B module, probes, anti-leading-question patterns, wrap-up, note-taking template, and interview snapshot so every session is complete by default.
Step 3: Run, synthesize, and export
Use the script in live interviews, fill the notes table, and complete the snapshot one-pager after each call. Then export Markdown, JSON, or doc-friendly text and share a URL snapshot with your team so interview insights flow directly into JTBD, personas, problem statements, and prioritization frameworks.
In B2C user interviews, you are usually talking to the person who experiences the problem and makes the decision alone. You focus on life context, habits, routines, and trade-offs with time or money. In B2B customer interviews, you often separate user behavior from the buying process: users, champions, detractors, budget owners, and procurement all play different roles. That is why this generator adds a B2B module only when you select B2B, so you can explore decision-making, buying committees, security, and budget thresholds without mixing them into core workflow questions.
Leading questions bias answers by smuggling your idea into the wording. Asking “Would you use this feature?” or “Do you like this design?” pushes people to guess what you want to hear instead of describing what they actually do. Anchor your customer discovery interview script in behavior-first, open-ended questions instead. For example, replace “Would you use X?” with “Tell me about the last time you tried to do X. Walk me through what you did and where it broke.” This keeps the focus on real events rather than opinions about your solution.
A practical rule: at least 40% of your discovery interview questions should start with story-based prompts. Use this tool’s built-in “avoid these leading questions” section plus rewrites to systematically upgrade your script. You still get crisp discovery interview questions, but they come from the participant’s last time story instead of hypothetical future behavior.
When you ask people what they would do, you get guesses shaped by social pressure and optimism. When you ask what they actually did last time, you get concrete steps, tools, and trade-offs grounded in reality. “Tell me about the last time…” and “Walk me through…” force both interviewer and participant to replay a specific event instead of brainstorming. This is the core of story-based interviewing and why product discovery interviews should sound more like investigative reporting than surveys.
Story-first prompts naturally surface frequency, triggers, and impact because participants mention when it happened, how painful it was, what they tried, and what they did next. The generator leans into this pattern so a large share of your core questions are story-based by default. You can still add opinion questions later, but only after you have the underlying story in enough detail to interpret them correctly.
After each interview, resist the urge to jump straight into features. Start by filling the note-taking table and the interview snapshot one-pager. Capture participant context, one memorable quote, a 3–5 bullet story summary, opportunities, non-opportunity insights, and next actions or assumptions to test. When you repeat this for several conversations, you can line snapshots up side-by-side and quickly see patterns in pains, triggers, and constraints without rewatching every recording.
Once patterns are visible, cluster opportunities across interviews into themes and feed them into JTBD statements, personas, problem statements, and opportunity solution trees. This keeps your synthesis grounded in actual interview data, not just memory. The structure in this generator is built to make that handoff smooth: every section is copyable, and exports include the table and snapshot blocks that downstream tools expect.
Symptom: Participants give short, generic answers and the conversation never gets deep.
Cause: Questions are framed as opinions or hypotheticals instead of concrete stories from the past.
Fix: Rewrite at least 40% of core questions to start with “Tell me about the last time…” or “Walk me through…”.
Symptom: You hear a lot of enthusiasm for the idea but little useful detail about current behavior.
Cause: The interviewer is pitching the solution or describing features instead of asking about what people do today.
Fix: Add a visible note in the script: “Avoid pitching your solution during discovery. Focus on current behavior.” and stick to it.
Symptom: B2B interviews feel messy because stakeholders and buyers are mixed together without clarity.
Cause: The script does not separate user workflow questions from buying-process questions in B2B settings.
Fix: Use the B2B module only when type is B2B and keep it distinct from problem and usage sections.
Symptom: Team cannot remember which interviews were about churn, which were discovery, and which were pricing.
Cause: Scripts and notes do not clearly encode the interview goal, so sessions blur together later.
Fix: Always capture goal, audience, stage, and duration in the header and include them in exports and snapshots.
Symptom: Notes are hard to synthesize because quotes, behaviors, and follow-up questions are scattered.
Cause: Interviewers jot notes in free-form documents without a consistent structure or table.
Fix: Use the note-taking table with columns for timestamp, quote, observed behavior, pain/opportunity, evidence strength, and follow-up.
Symptom: After a round of interviews, the team jumps straight into features instead of opportunities.
Cause: There is no lightweight snapshot to summarize each conversation and extract opportunities first.
Fix: Fill the interview snapshot one-pager after each call and synthesize opportunities before discussing solutions.
Symptom: Different interviewers run the same “script” differently and bias creeps in.
Cause: The script leaves too much room for improvisation and does not include shared probes or anti-patterns.
Fix: Standardize on a shared probes library and explicitly list leading questions to avoid in the guide.
Symptom: Recordings pile up and the team rarely revisits them once the project moves on.
Cause: There is no clear link between interviews, notes, and next decisions or experiments.
Fix: Attach each snapshot to a specific decision or experiment and track which assumptions each interview helped test.
Yes. This user interview script generator is completely free and runs in your browser with no login or account creation step. You can generate as many product discovery and research guides as you need, experiment with different goals and segments, and export or share outputs without hitting a paywall or trial limit.
This tool is designed for product discovery and user research interviews, not hiring or job-candidate interviews. The language, goals, and sections focus on understanding customer behavior, workflows, and decision journeys. Use it when you want better customer discovery interview scripts, not when you are assessing candidates for a role.
Yes. The generator supports a B2B/B2C toggle so you can adapt the script for buying committees or individual consumers. When B2B is selected, you get an additional module on decision-making, procurement, security, and budget thresholds. When B2C is selected, the flow stays focused on personal context, habits, and day-to-day behavior instead.
The output includes a dedicated section on leading question anti-patterns plus behavior-first rewrites you can copy into your script. The core sections lean heavily on open-ended prompts like “Tell me about the last time…” and “Walk me through…”, which naturally reduce bias. You can review this list quickly before every interview block to stay sharp.
Generic lists rarely adapt to your specific goal, segment, and stage, so they feel either too broad or too shallow. This generator uses your goal, audience, and duration to structure a full flow from opening to wrap-up. It also includes probes, note-taking templates, and synthesis guides so you can go from conversation to decision-ready insight faster.
Yes. The Share action creates a compressed URL snapshot that reconstructs the inputs and generated script in a new browser session. You can paste the link into Slack, email, or docs so teammates land on the same guide. Because autosave is local and the snapshot only includes the structured script, you avoid sending raw notes or recordings by default.
Pick the churn interview goal, define your audience as recently cancelled users, and generate a script tailored to cancellation stories. The flow focuses on triggers, expectations, what customers tried before cancelling, and what they switched to next. You can then use the snapshot and note templates to compare patterns across conversations and surface top opportunities.
Yes. Select the pricing / willingness-to-pay goal and include any relevant market context or countries in the inputs. The generated questions explore budget mental models, value anchors, alternatives people would pay for instead, and approval thresholds. This helps you run more thoughtful pricing conversations without turning them into negotiation sessions.
Use the note-taking template live or immediately after each interview so quotes, behaviors, and pains stay organized. Then complete the one-page interview snapshot to summarize context, story, opportunities, and next actions. Once you have several snapshots, you can cluster opportunities across interviews and feed them into tools like JTBD, OST, or prioritization frameworks.
You can treat this generator as the front door for qualitative discovery. Use it to run better customer discovery and usability interviews, then feed stories into JTBD statements, personas, problem statements, and opportunity solution trees. Because the tools share similar structures and exports, it is easy to turn raw interviews into decision-grade artifacts for roadmap planning.
Use these courses, blog guides, and glossary entries to deepen your customer discovery interview skills and connect what you learn to product decisions.
Use CraftUp’s courses and workflows to connect interview insights with JTBD, personas, problem statements, and prioritization so discovery leads to action, not just more notes.
Last updated: 2026-03-05