> ## Documentation Index
> Fetch the complete documentation index at: https://docs.userintuition.ai/llms.txt
> Use this file to discover all available pages before exploring further.
> Maximize the value of your customer research with these proven best practices for study design, participant recruitment, and insight activation.
# Best Practices
Maximize the value of your customer research with these proven best practices. These recommendations come from extensive experience conducting thousands of AI-moderated interviews.
***
## Study Design
### Start with Clear Objectives
Before creating a study, articulate:
* **The core question:** What must this research answer?
* **The decisions:** What will you do differently based on what you learn?
* **The stakeholders:** Who needs to act on these insights?
If you can't articulate what decision will change based on the research, consider whether you need the study at all.
### Focus on Depth, Not Breadth
**Don't try to cover everything.** Studies that go deep on 4-6 topics deliver more actionable insights than broad studies that skim 15 topics.
| ❌ Too Broad | ✅ Focused |
| -------------------------------------- | --------------------------------------- |
| "Tell us about your entire experience" | "Walk me through your checkout process" |
| 15 topics, 2 questions each | 5 topics, 6 questions each |
| Surface-level across everything | Deep understanding of key areas |
### Write for Your Participants
Use language your participants actually use, avoid internal jargon and acronyms, and frame questions from their perspective rather than your org chart's.
Good questions invite a story rather than a yes or no:
| Avoid | Better |
| --------------------------------- | ------------------------------------------------------------ |
| "Did you have a good experience?" | "Walk me through your most recent experience." |
| "Is our pricing fair?" | "How did you feel about the pricing when you were deciding?" |
| "Would you recommend us?" | "If a colleague asked you about us, what would you say?" |
The pattern: ask about what happened, not what someone thinks or would do. People are poor at predicting themselves and good at recounting what they did.
### Trust the AI to Probe
You don't need to script every follow-up question. The AI interviewer naturally:
* Asks "tell me more" when responses are brief
* Probes deeper when participants mention something interesting
* Requests specific examples to ground abstract statements
* Follows unexpected threads that may reveal insights
***
## Participant Recruitment
### Match the audience to the question
When the question is about **your** product — why they churned, what confused them in onboarding, whether the new pricing lands — interview your own participants. They bring real experience with the thing you're asking about, and their feedback is specific rather than hypothetical.
Use the [research panel](/recruiting/research-panel) when you need people who *aren't* your customers: category research, competitive work, or testing a concept with a market you haven't reached yet.
A study link shared by someone the participant already knows outperforms an invitation from a name they don't recognise. Where you can, send the ask yourself.
### Set Clear Expectations
In your invitation, communicate:
* How long the interview will take (be accurate)
* What topics you'll cover (high level)
* Why their feedback matters
* Any incentives offered
### Time Your Invitations
| Context | Best Timing |
| ------------------- | -------------------------- |
| Post-purchase | Within 24-48 hours |
| Churn research | Shortly after cancellation |
| Onboarding feedback | End of first week |
| General feedback | Avoid Mondays and Fridays |
| B2B research | Mid-week, business hours |
### Send Reminders
A single reminder 3-5 days after the initial invitation can significantly improve response rates. Keep it brief and friendly.
### Watch for self-selection
Anyone can follow an open study link, and the people who volunteer are rarely a neutral sample — they tend to be your most enthusiastic or most annoyed customers, with the quiet middle missing. That's fine when you want strong opinions; it's a problem when you want a representative read. Use screeners to keep the wrong people out, and reach for the panel when representativeness matters.
***
## During Data Collection
### Monitor Early Responses
Listen to your first 3-5 interviews to:
* Confirm questions are understood correctly
* Verify the conversation flows naturally
* Catch any issues before scaling recruitment
* Validate your research hypotheses (or adjust them)
### Don't Edit Mid-Study
Once participants have started completing interviews:
* **Avoid changing questions** (compromises comparability)
* **Don't adjust the flow** (earlier and later responses won't match)
* **Note issues for next time** instead of fixing mid-stream
If changes are truly necessary, document when they occurred and consider separating analysis into pre/post change periods.
### Check Quality Distribution
Monitor your Quality count relative to total responses. If too many interviews come back Fair or Poor:
* Review if questions are confusing
* Check if you're reaching the right participants
* Consider whether the topic engages participants
***
## Analysis and Reporting
### Look for Patterns, Not Just Quotes
Individual quotes are powerful, but patterns matter more:
* What do multiple participants mention?
* Where do responses contradict each other?
* What's notably absent from conversations?
### Separate strong signals from weak ones
**Strong** — mentioned by several participants, consistent across segments, and backed by specific examples rather than generalities.
**Weak** — one participant, contradicted elsewhere, or asserted without an example behind it. Weak signals aren't worthless; they're hypotheses for the next study rather than findings for this one.
### Treat contradictions as information
When participants disagree, the disagreement is usually the finding. Look for a segment difference behind it — new versus long-term, one plan versus another, one market versus another. A contradiction that splits cleanly along a segment line is a discovery. One that doesn't usually means you need more interviews.
### Spot-check before you share
Follow a few quotes back to their transcripts before a report goes to stakeholders. It takes minutes, it catches the occasional quote that reads differently in context, and it means you can answer "where did this come from?" in the meeting.
### Consider Sample Size
How much your sample supports depends on how common the thing you're looking for is. Five interviews is plenty for something half your users hit and close to a coin flip for anything affecting 1 in 10.
Work out what your number actually buys — and how many you'd need for the confidence you want — with the [sample size calculator](/resources/sample-size).
Always state your sample size when sharing findings, and state it **per segment** if you split the sample.
### Connect Insights to Actions
Every insight should connect to potential action:
| Insight | Action |
| ------------------------------ | ---------------------------------------------- |
| "Checkout feels slow" | Investigate performance; A/B test improvements |
| "Confused about pricing tiers" | Revise pricing page; test clearer copy |
| "Love the mobile app" | Double down on mobile investment |
If an insight doesn't connect to a potential action, consider whether it was worth learning.
### Share Widely, But Appropriately
| Audience | What to Share |
| ------------- | --------------------------------------------- |
| Executives | Executive Summary + Top Insights |
| Product teams | Detailed findings + specific quotes |
| Marketing | Customer language + perception insights |
| Sales | Objection patterns + competitive intelligence |
***
## Building a Research Practice
### Create Recurring Studies
For ongoing intelligence, establish:
* **Quarterly brand tracking** — the same questions to a fresh sample each quarter
* **Monthly churn research** — talk to people shortly after they leave
* **Continuous onboarding feedback** — via the [embed widget](/recruiting/embed-widget)
* **Post-launch research** for each major release
Panel studies can do this for you: set a [recurring panel](/recruiting/research-panel#recurring-panels) to weekly or monthly and each wave lands in the same study, so the comparison stays like-for-like.
### Build Institutional Knowledge
User Intuition's Intelligence Hub becomes more valuable over time:
* Query across historical studies
* Identify long-term trends
* Preserve knowledge through team changes
* Accelerate onboarding for new team members
### Close the Loop
After each study:
1. Share findings with stakeholders
2. Identify 2-3 concrete actions
3. Track whether actions were taken
4. Measure impact where possible
5. Document learnings for future studies
***
## Common Mistakes to Avoid
**❌** "Don't you think our checkout is confusing?"\
**✅** "Walk me through your experience with checkout."
**❌** 20 topics in a 15-minute interview\
**✅** 5-6 topics explored deeply
Low-quality responses often reveal confusion or disengagement—which is itself a finding worth investigating.
One passionate participant doesn't make a pattern. Wait for consistent themes across multiple interviews.
Research that doesn't lead to action wastes resources and erodes organizational trust in research value.
Resist the urge to tweak questions once interviews have begun. Note improvements for next time instead.
***
## Quick Reference
### Study Setup Checklist
* [ ] Clear research objective defined
* [ ] 4-6 focused topics identified
* [ ] Questions written in participant language
* [ ] Decision/action identified for insights
* [ ] Study tested before launch
### Recruitment Checklist
* [ ] Target participants identified
* [ ] Invitation includes time estimate
* [ ] Incentive communicated (if applicable)
* [ ] Reminder plan in place
* [ ] Early responses monitored
### Analysis Checklist
* [ ] Listened to sample of interviews (not just read transcripts)
* [ ] Patterns identified across multiple participants
* [ ] Sample size noted with findings
* [ ] Insights connected to actions
* [ ] Appropriate sharing with stakeholders