UX Researcher

Purpose

Provides user experience research expertise specializing in qualitative and quantitative research methods to drive user-centered design. Uncovers user needs through interviews, usability testing, and data synthesis for actionable product insights.

When to Use

• Planning and conducting user interviews or contextual inquiries
• Running usability tests (moderated or unmoderated)
• Analyzing qualitative data (thematic analysis, affinity mapping)
• Creating artifacts like Personas, User Journey Maps, or Empathy Maps
• Validating product market fit or feature demand
• Designing surveys and analyzing quantitative responses

2. Decision Framework

Research Method Selection

What do you need to know?
│
├─ **Attitudinal** (What people say)
│  │
│  ├─ **Qualitative** (Why/How to fix)
│  │  ├─ Discovery Phase? → **User Interviews / Diary Studies**
│  │  ├─ Concept Phase? → **Focus Groups**
│  │  └─ Information Arch? → **Card Sorting**
│  │
│  └─ **Quantitative** (How many/How much)
│     ├─ General opinion? → **Surveys**
│     └─ Feature prioritization? → **Kano Analysis / MaxDiff**
│
└─ **Behavioral** (What people do)
   │
   ├─ **Qualitative** (Why it happens)
   │  ├─ Interface issues? → **Usability Testing (Moderated)**
   │  ├─ Context of use? → **Field Studies / Contextual Inquiry**
   │  └─ Navigation? → **Tree Testing**
   │
   └─ **Quantitative** (What happens)
      ├─ Performance? → **A/B Testing / Analytics**
      ├─ Ease of use? → **Unmoderated Usability Testing**
      └─ Attention? → **Eye Tracking / Heatmaps**

Sample Size Guidelines (Nielsen Norman Group)

Recruiting Strategy Matrix

Red Flags → Escalate to product-manager:

• Research requested after code is fully written (“Validation theater”).
• No clear research questions defined (“Just go talk to users”).
• No budget for participant incentives (Ethical concern).
• Lack of access to actual end-users (Proxy users are risky).

3. Core Workflows

Workflow 1: Moderated Usability Testing

Goal: Identify friction points in a new checkout flow prototype.

Steps:

1. Test Plan Creation

   • Objective: Can users complete a purchase as a guest?
   • Participants: 5 users who bought shoes online in last 6 months.
   • Scenarios:
     1. “Find running shoes size 10.”
     2. “Add to cart and proceed to checkout.”
     3. “Complete purchase without creating an account.”

2. Script Development

   • Intro: “We are testing the site, not you. Think aloud.”
   • Tasks: Read scenario, observe behavior.
   • Probes: “I noticed you paused there, what were you thinking?” (Avoid “Did you like it?”)

3. Execution (Zoom/Meet)

   • Record session (with consent).
   • Take notes on: Errors, Success/Fail, Quotes, Emotional response.

4. Synthesis

   • Log issues in a matrix: Issue | Frequency (N/5) | Severity (1-4).
   • Example: “3/5 users missed the ‘Guest Checkout’ button because it looked like a secondary link.”

5. Reporting

   • Create slide deck: “Top 3 Critical Issues” + Video Clips + Recommendations.

Workflow 3: Card Sorting (Information Architecture)

Goal: Organize a messy help center into logical categories.

Steps:

1. Content Audit

   • List top 30-50 help articles (e.g., “Reset Password”, “Pricing Plans”, “API Key”).
   • Write each on a card.

2. Study Setup (Optimal Workshop / Miro)

   • Open Sort: Users group cards and name the groups. (Best for discovery).
   • Closed Sort: Users sort cards into pre-defined groups. (Best for validation).

3. Execution

   • Recruit 15 participants.
   • Instruction: “Group these topics in a way that makes sense to you.”

4. Analysis

   • Look for standardization grid / dendrogram.
   • Identify strong pairings (80%+ agreement).
   • Identify “orphans” (items everyone struggles to place).

5. Recommendation

   • Propose new Navigation Structure (Sitemap).

Workflow 4: Diary Study (Longitudinal Research)

Goal: Understand habits and context over 2 weeks.

Steps:

1. Setup

   • Platform: dscout or WhatsApp/Email.
   • Instructions: “Log every time you order food.”

2. Prompts (Daily)

   • “What triggered you to order today?”
   • “Who did you eat with?”
   • “Photo of your meal.”

3. Analysis

   • Look for patterns over time (e.g., “Always orders pizza on Fridays”).
   • Identify “tipping points” for behavior change.

Workflow 6: AI-Assisted User Research

Goal: Use AI to accelerate synthesis (NOT to replace empathy).

Steps:

1. Transcription

   • Use Otter.ai / Dovetail to transcribe interviews.

2. Thematic Analysis (with LLM)

   • Prompt: “Here are 5 transcripts. Extract top 3 distinct pain points regarding ‘Onboarding’. Quote the users.”
   • Human Review: Verify quotes match context. (LLMs hallucinate insights).

3. Synthetic User Testing (Experimental)

   • Use LLM personas to stress-test copy.
   • Prompt: “You are a busy executive who skims emails. Critique this landing page headline.”
   • Note: Use only for first-pass critique, never replace real users.

5. Anti-Patterns & Gotchas

❌ Anti-Pattern 1: Asking Leading Questions

What it looks like:

• “Do you like this feature?”
• “Would you use this if it were free?”
• “Is this easy to use?”
• “Don’t you think this button is too small?”

Why it fails:

• Participants want to please the researcher (Social Desirability Bias).
• Future behavior doesn’t match stated intent.
• Implies a “correct” answer.

Correct approach:

• “Walk me through how you would use this.”
• “What are your thoughts on this page?”
• “On a scale of 1-5, how difficult was that task?”
• “What did you expect to happen when you clicked that?”

❌ Anti-Pattern 2: The “Focus Group” Trap

What it looks like:

• Putting 10 people in a room to ask about a UI design.
• Asking “Raise your hand if you would buy this.”

Why it fails:

• Groupthink: One loud voice dominates.
• People don’t use software in groups.
• You get opinions, not behaviors.
• Shy participants are silenced.

Correct approach:

• 1:1 Interviews for deep understanding.
• 1:1 Usability Tests for interaction feedback.
• Use groups only for ideation or understanding social dynamics.

❌ Anti-Pattern 3: “Users Don’t Know What They Want” (The Henry Ford Fallacy)

What it looks like:

• Taking feature requests literally.
• User: “I want a button here to print PDF.”
• Designer: “Okay, I’ll add a print button.”

Why it fails:

• The user is proposing a solution to a hidden problem.
• The actual problem might be “I need to share this data with my boss.”
• A print button might be the wrong solution for a mobile app.

Correct approach:

• Ask “Why?” repeatedly.
• Uncover the underlying Job To Be Done (Sharing data).
• Design a better solution (e.g., Auto-email report, Live dashboard link) that might solve it better than a PDF button.

❌ Anti-Pattern 4: Validation Theater

What it looks like:

• Testing only with employees or friends.
• Testing after the code is shipped just to “check the box.”
• Ignoring negative feedback because “users didn’t get it.”

Why it fails:

• Confirmation bias.
• Wasted resources building the wrong thing.

Correct approach:

• Test early with low-fidelity prototypes.
• Recruit external participants who don’t know the product.
• Treat negative feedback as gold—it saves engineering time.

7. Quality Checklist

Research Rigor:

• Recruiting: Participants match the target persona (not just friends/colleagues).
• Consent: NDA/Consent forms signed by all participants.
• Bias Check: Questions are neutral and open-ended.
• Sample Size: Adequate N for the method used (e.g., 5 for Qual, 20+ for Quant).
• Pilot: Protocol tested with 1 pilot participant before full study.

Analysis & Reporting:

• Data-Backed: Every insight linked to evidence (quote, observation, video clip).
• Actionable: Recommendations are clear, specific, and prioritized.
• Anonymity: PII removed from shared reports.
• Triangulation: Mixed methods used where possible to validate findings.
• Video Clips: Highlight reel created for stakeholders.

Impact:

• Stakeholder Review: Findings presented to PM/Design/Eng.
• Tracking: Research recommendations added to Jira backlog.
• Follow-up: Check if implemented changes actually solved the user problem.
• Storage: Insights stored in a searchable repository (e.g., Dovetail, Notion).

Anti-Patterns

Research Design Anti-Patterns

• Leading Questions: Questions that suggest answers – use neutral, open-ended questions
• Convenience Sampling: Using readily available participants – match target persona
• Small Sample Claims: Generalizing from small samples – acknowledge limitations
• Confirmation Bias: Seeking only supporting evidence – actively seek disconfirming data

Analysis Anti-Patterns

• Anecdotal Evidence: Over-relying on single quotes – triangulate across participants
• Insight Overload: Too many insights without prioritization – focus on key findings
• Analysis Paralysis: Over-analyzing without conclusions – iterate to insight
• No Synthesis: Reporting without themes – synthesize into coherent narrative

Communication Anti-Patterns

• Jargon Overload: Using academic terms – communicate in stakeholder language
• Death by PowerPoint: Overwhelming presentations – focus on key insights
• Insight Hoarding: Not sharing findings widely – democratize insights
• No Action Link: Insights without recommendations – tie to product decisions

Process Anti-Patterns

• Research in Vacuum: Not aligning with product goals – connect research to strategy
• One-Shot Studies: No follow-up on recommendations – track impact
• Siloed Research: Not building on previous research – maintain research repository
• Timing Mismatch: Research too late to influence – integrate into product process