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Insights Library

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Key insights and learnings from UX research activities

Insights Library

This library contains actionable insights discovered through user research. Each insight is backed by evidence and includes implications for product development.

What is an Insight?

An insight is a deep understanding of user behavior, needs, or motivations that goes beyond surface-level observations. It answers the "why" behind user actions and provides direction for product decisions.

Anatomy of a Good Insight

  • Observation - What we saw or measured
  • Inference - What it means for users
  • Implication - What we should do about it
  • Evidence - Data supporting the insight

Browse Insights

Navigation & IA
Insights about how users find and organize information
Onboarding
Insights about first-time user experiences
Accessibility
Insights about inclusive design and accessibility
Mobile Experience
Insights about mobile-specific behaviors and needs

Recent Insights

High-Confidence Insights

These insights are based on multiple research studies and strong evidence.


Insight Template

When documenting an insight, use this structure:

---
title: Insight Title (Clear, Specific Statement)
description: One-sentence summary of the insight
date: 2024-01-15
category: navigation | onboarding | accessibility | mobile | etc.
confidence: high | medium | low
impact: high | medium | low
status: validated | hypothesis | archived
---
 
# Insight Title
 
## TL;DR
 
One or two sentences summarizing the insight and its implications.
 
## The Insight
 
Clear, concise statement of what we learned about users.
 
**In one sentence:** Users do [behavior] because [motivation], which means we should [action].
 
## Evidence
 
Data, quotes, and observations that support this insight.
 
### Quantitative Evidence
 
- 78% of users (23/30) exhibited this behavior
- Average time to complete task: 45 seconds
- Task success rate: 65%
 
### Qualitative Evidence
 
> "Quote from user that illustrates the insight"
> — Participant 7, Study Name
 
> "Another supporting quote"
> — Participant 12, Study Name
 
### Supporting Studies
 
- [Study Name 1](/studies/study-1) - Key finding
- [Study Name 2](/studies/study-2) - Key finding
- [Survey Results](/studies/survey) - Relevant data
 
## Context & Background
 
When did we discover this? What prompted the research?
 
- **Discovery Date**: March 2024
- **Research Context**: Usability testing of new navigation
- **Related Problem**: Users were abandoning tasks
 
## Implications
 
What does this mean for our products and decisions?
 
### For Product Teams
 
How this insight should influence product decisions
 
### For Design Teams
 
How this insight should influence design decisions
 
### For Engineering Teams
 
Technical considerations or implications
 
## Recommendations
 
Specific, actionable steps based on this insight.
 
### Immediate Actions (Do Now)
 
1. **Recommendation 1** - Specific action to take
   - Owner: Team/Person
   - Timeline: 1 week
   - Effort: Low
 
### Short-term Actions (Next Quarter)
 
2. **Recommendation 2** - Another action
   - Owner: Team/Person
   - Timeline: 1 month
   - Effort: Medium
 
### Long-term Actions (Future)
 
3. **Recommendation 3** - Strategic action
   - Owner: Team/Person
   - Timeline: 3-6 months
   - Effort: High
 
## Actions Taken
 
What we've done based on this insight and the results.
 
### Implementation 1
 
- **What**: Description of change made
- **When**: March 2024
- **Result**: Measurable outcome
- **Impact**: How it affected users
 
### Implementation 2
 
- **What**: Another change
- **When**: April 2024
- **Result**: Outcome
- **Impact**: User impact
 
## Success Metrics
 
How we measure whether addressing this insight was successful.
 
- **Metric 1**: Baseline → Target (Actual)
- **Metric 2**: Baseline → Target (Actual)
- **Metric 3**: Baseline → Target (Actual)
 
## Related Content
 
Links to related insights, studies, and personas.
 
- [Related Insight 1](/insights/related-1)
- [Related Study](/studies/study)
- [Related Persona](/personas/persona)
 
## Confidence Assessment
 
**Confidence Level:** High
 
**Why this confidence level?**
 
- Multiple research methods (triangulation)
- Large sample size (30+ participants)
- Consistent across user segments
- Validated in multiple contexts
 
**Validation History:**
 
- Initial finding: Study A (10 participants)
- Confirmed: Study B (15 participants)
- Quantified: Survey (200 responses)
 
## Future Research
 
Questions this insight raises or areas needing more research.
 
- What about [edge case or different context]?
- How does this vary across [different user segment]?
- What are the long-term effects of [intervention]?
 
---
 
**Insight Owner:** Researcher Name
**Last Updated:** March 15, 2024
**Next Review:** June 15, 2024
**Status:** Validated

Creating Great Insights

Start with Observations

Observation: Users clicked the back button 5 times before finding settings

Insight: Users expect settings in the navigation menu because that's where other applications place it, not in their profile dropdown

Add Evidence

Support your insight with multiple sources:

  • User quotes
  • Behavioral data
  • Comparative analysis
  • Industry patterns

Make It Actionable

Bad: "Users struggle with navigation" Good: "Users expect settings in the main menu, not the profile dropdown, because that matches mental models from other applications"

Insight Categories

Navigation & Information Architecture

  • Findability issues
  • Mental model mismatches
  • Search behavior
  • Menu organization

Onboarding & Learning

  • First-time user experience
  • Feature discovery
  • Learning curves
  • Help and support needs

Task Completion

  • Workflow bottlenecks
  • Error prevention
  • Task efficiency
  • Success patterns

Accessibility

  • Assistive technology usage
  • Inclusive design needs
  • Barrier identification
  • Accommodation requirements

Mobile & Cross-Platform

  • Device-specific behaviors
  • Context of use
  • Platform expectations
  • Responsive needs

Content & Communication

  • Language preferences
  • Reading patterns
  • Information needs
  • Tone and voice

Insight Confidence Levels

High Confidence āœ…

  • Multiple research methods
  • Large sample size (20+)
  • Consistent across segments
  • Validated with quantitative data
  • Clear, measurable evidence

Medium Confidence āš ļø

  • Single research method
  • Moderate sample (10-20)
  • Some variation across segments
  • Needs additional validation
  • Strong qualitative evidence

Low Confidence šŸ¤”

  • Exploratory finding
  • Small sample (fewer than 10)
  • Significant variation
  • Requires further research
  • Hypothesis to test

Using Insights

In Product Planning

"Based on [Insight Name], we should prioritize..."

In Design Reviews

"Remember [Insight Name] showed that users expect..."

In Prioritization

"This addresses [High Confidence Insight], affecting 75% of users"

In Validation

"Let's test whether [Insight Name] still holds true"

Best Practices

Do āœ…

  • Base insights on patterns, not individual cases
  • Support with multiple data sources
  • Make insights specific and actionable
  • Update insights as you learn more
  • Track what actions resulted from insights

Don't āŒ

  • Present observations as insights
  • Generalize from one or two users
  • Write vague or abstract insights
  • Create insights based on assumptions
  • Let insights sit unused

Common Pitfalls

Confusing Observations with Insights

āŒ Observation: "Users clicked the wrong button" āœ… Insight: "Users clicked the CTA button instead of the link because the link looked like body text, not an actionable element"

Being Too Vague

āŒ Vague: "Users want better navigation" āœ… Specific: "Users expect to find account settings in the main navigation menu, not buried in their profile dropdown, because that matches their mental model from banking and email applications"

Missing the "So What?"

āŒ Incomplete: "Users prefer visual layouts" āœ… Complete: "Users prefer visual card layouts to text lists because they can scan faster and recognize items by thumbnail, reducing time to find items by 40%"

Insight Lifecycle

  1. Discovery - Find pattern in research
  2. Documentation - Write up the insight
  3. Validation - Confirm with additional research
  4. Activation - Share with stakeholders
  5. Implementation - Take action based on insight
  6. Measurement - Track impact of changes
  7. Refinement - Update based on results
  8. Archive - Mark as outdated when no longer relevant

Measuring Insight Impact

Track the value of your insights:

  • Usage: How often is this insight referenced?
  • Influence: What decisions did it inform?
  • Implementation: What changes resulted?
  • Outcome: What measurable impact occurred?

Need Help?

  • Writing insights - Use our insight template
  • Validation - Consult with the UXR team
  • Prioritization - Join insight review sessions
  • Questions - Reach out to the UXR team

Remember: Great insights change how teams think about users and lead to better product decisions. Focus on quality over quantity.

@strongtie/stylesResearch Methods

On This Page

Insights LibraryWhat is an Insight?Anatomy of a Good InsightBrowse InsightsRecent InsightsHigh-Confidence InsightsInsight TemplateCreating Great InsightsStart with ObservationsAdd EvidenceMake It ActionableInsight CategoriesNavigation & Information ArchitectureOnboarding & LearningTask CompletionAccessibilityMobile & Cross-PlatformContent & CommunicationInsight Confidence LevelsHigh Confidence āœ…Medium Confidence āš ļøLow Confidence šŸ¤”Using InsightsIn Product PlanningIn Design ReviewsIn PrioritizationIn ValidationBest PracticesDo āœ…Don't āŒCommon PitfallsConfusing Observations with InsightsBeing Too VagueMissing the "So What?"Insight LifecycleMeasuring Insight ImpactNeed Help?

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