user-research

name	user-research
description	Research methodologies, user interviews, surveys, usability testing, persona creation, and insight extraction

User Research

Research Methodologies

User Interviews

• One-on-One Interviews: Deep, qualitative conversations with individual users
• Semi-Structured: Use a guide but allow flexibility to explore unexpected topics
• Open-Ended Questions: Ask questions that encourage detailed responses
• Active Listening: Listen more than you speak, probe for deeper understanding
• Recording: Record interviews (with permission) for later analysis
• Interview Length: 30-60 minutes is optimal for maintaining engagement

Surveys

• Survey Design: Keep surveys short and focused (5-10 minutes max)
• Question Types: Use a mix of multiple choice, rating scales, and open-ended questions
• Avoid Bias: Use neutral language and avoid leading questions
• Pilot Testing: Test surveys with a small group before full distribution
• Distribution Channels: Email, in-app, social media, or dedicated survey platforms
• Response Rates: Expect 10-20% response rate for email surveys

Usability Testing

• Moderated Testing: Researcher guides participants through tasks
• Unmoderated Testing: Participants complete tasks independently
• Think-Aloud Protocol: Ask participants to verbalize their thoughts
• Task Design: Create realistic tasks that represent actual user goals
• Metrics: Track task completion rate, time on task, error rate, and satisfaction
• Sample Size: 5 users reveal 80% of usability issues

Card Sorting

• Open Card Sort: Users create their own categories
• Closed Card Sort: Users sort into predefined categories
• Hybrid Approach: Combine both methods for comprehensive insights
• Tools: Use online tools for remote card sorting sessions
• Analysis: Look for patterns and consensus in how users organize information
• Application: Inform information architecture and navigation design

Persona Creation

Persona Development

• Research-Based: Personas should be based on real research data
• Demographics: Age, gender, location, education, occupation
• Psychographics: Goals, motivations, frustrations, attitudes
• Behaviors: How they interact with products, technology preferences
• Quotes: Include real quotes from interviews to bring personas to life
• Scenarios: Describe typical use cases and contexts

User Journey Mapping

• Touchpoints: List all interactions across channels and devices
• Emotions: Map user emotions at each touchpoint
• Pain Points: Identify areas of frustration or difficulty
• Opportunities: Find moments to delight users or improve experience
• Timeline: Show the sequence of interactions over time
• Channels: Include all channels (web, mobile, email, in-person)

User Stories and Use Cases

User Story Format

• Template: "As a [type of user], I want [goal] so that [benefit]"
• Acceptance Criteria: Define specific conditions for story completion
• Priority: Rank stories by business value and user need
• Estimation: Provide effort estimates for planning
• Dependencies: Identify relationships between stories

Use Case Development

• Actors: Identify primary and secondary actors
• Preconditions: Define conditions before use case begins
• Main Flow: Describe the primary success scenario
• Alternative Flows: Document alternative paths and edge cases
• Postconditions: Define the state after use case completion
• Exceptions: Handle error conditions and failures

Research Analysis and Insight Extraction

Data Synthesis

• Affinity Diagramming: Group related findings into themes
• Pattern Recognition: Identify recurring themes and insights
• Triangulation: Validate findings across multiple research methods
• Quantitative Analysis: Use statistical methods for survey data
• Qualitative Analysis: Use thematic analysis for interview data

Insight Extraction

• So What?: Ask why findings matter and what they imply
• Now What?: Determine actionable next steps
• Prioritization: Rank insights by impact and feasibility
• Validation: Plan how to validate insights with additional research
• Communication: Present insights in a clear, compelling way

A/B Testing and Experiment Design

Experiment Design

• Hypothesis: Clearly state what you're testing and why
• Variables: Define independent (what you change) and dependent (what you measure) variables
• Control Group: Include a group that doesn't see the change
• Sample Size: Calculate required sample size for statistical significance
• Duration: Run tests long enough to reach statistical significance
• Metrics: Choose appropriate metrics (conversion, engagement, satisfaction)

A/B Testing Best Practices

• Test One Variable: Change only one element at a time
• Statistical Significance: Use proper statistical methods to analyze results
• Segmentation: Analyze results by user segments
• Iterative Testing: Build on learnings from previous tests
• Ethical Considerations: Consider impact on user experience and privacy