user-research-synthesizer

By Agentman

Synthesize user research interviews, surveys, and feedback into actionable insights. Provides affinity mapping frameworks, persona development templates, and insight prioritization methods. Use for research analysis, persona creation, or translating user feedback into product decisions.

Productv
researchsynthesispersonasinsightsqualitativeUXdiscoveryanalysis

Skill Instructions

# User Research Synthesizer

## Overview

Transform scattered user feedback into structured insights that drive product decisions. This skill encodes the frameworks for synthesizing qualitative research into actionable findings.

## Research Synthesis Process

```
RAW DATA → OBSERVATIONS → PATTERNS → INSIGHTS → RECOMMENDATIONS
```

### Step 1: Extract Observations

From each interview/feedback source, capture:
- Direct quotes (verbatim)
- Behaviors observed
- Pain points mentioned
- Goals expressed
- Context details

**Observation Format:**
```
[Source ID] [Quote/Observation]
"I spend 2 hours every Monday just compiling reports" - User 5, Sales Manager
```

### Step 2: Affinity Mapping

Group observations into themes:

| Theme | Observations | Frequency |
|-------|--------------|-----------|
| Reporting pain | U1, U3, U5, U7, U9 | 5/10 |
| Mobile needs | U2, U4, U6 | 3/10 |
| Integration gaps | U1, U2, U8, U10 | 4/10 |

### Step 3: Pattern Identification

**Pattern Statement Format:**
```
[WHO] experiences [WHAT problem/need]
when [CONTEXT/TRIGGER]
because [ROOT CAUSE]
```

**Example:**
> Sales managers experience time-consuming manual reporting when preparing for weekly meetings because data lives in multiple disconnected systems.

### Step 4: Insight Generation

**Insight Format:**
```
INSIGHT: [Actionable finding]
EVIDENCE: [Supporting data points]
CONFIDENCE: [High/Medium/Low]
IMPACT: [If we solve this, then...]
```

**Example:**
```
INSIGHT: Users need automated cross-system reporting
EVIDENCE: 5/10 users mentioned manual reporting pain, avg 2+ hrs/week
CONFIDENCE: High (consistent across segments)
IMPACT: Solving this would save ~100 hrs/month across user base
```

## Persona Development

### Persona Template

```
NAME: [Representative name]
ROLE: [Job title]
SEGMENT: [Customer segment]

GOALS
- Primary: [Main objective]
- Secondary: [Supporting goals]

PAIN POINTS
- [Pain 1]: [Impact]
- [Pain 2]: [Impact]

BEHAVIORS
- [How they work today]
- [Tools they use]
- [Decision-making style]

QUOTES
"[Verbatim quote that captures their perspective]"

SUCCESS METRICS
[How they measure success in their role]
```

### Persona Validation Checklist

- [ ] Based on actual research (not assumptions)
- [ ] Represents a significant segment
- [ ] Distinct from other personas
- [ ] Actionable for product decisions
- [ ] Includes both goals and frustrations

## Insight Prioritization

### Impact/Frequency Matrix

```
                HIGH FREQUENCY
                     │
   Address First     │    Top Priority
   (Common but       │    (Common and
    low impact)      │     high impact)
                     │
─────────────────────┼─────────────────────
                     │
   Monitor           │    Opportunistic
   (Rare and         │    (Rare but
    low impact)      │     high impact)
                     │
                LOW FREQUENCY

                LOW ←──────────────→ HIGH
                        IMPACT
```

### Prioritization Criteria

| Factor | Weight | Questions |
|--------|--------|-----------|
| Frequency | 30% | How many users mentioned this? |
| Severity | 25% | How painful is this problem? |
| Business Impact | 25% | Does this affect revenue/retention? |
| Solvability | 20% | Can we realistically address this? |

## Research Report Structure

```
EXECUTIVE SUMMARY
- Key findings (3-5 bullets)
- Recommended actions

METHODOLOGY
- Research type and sample
- Limitations

KEY INSIGHTS
- Insight 1 + evidence
- Insight 2 + evidence
- Insight 3 + evidence

PERSONAS (if applicable)
- Persona summaries

RECOMMENDATIONS
- Prioritized actions
- Quick wins vs. long-term

APPENDIX
- Raw data summary
- Full observation list
```

## Quality Checklist

Before presenting findings:
- [ ] Insights are based on patterns, not single data points
- [ ] Direct quotes support each finding
- [ ] Confidence level is stated
- [ ] Recommendations are actionable
- [ ] Limitations are acknowledged

## Resources

### references/
- **interview-guide.md** — Research interview question templates
- **analysis-methods.md** — Qualitative analysis techniques

### assets/
- **persona-template.pptx** — Persona presentation format
- **affinity-map-template.xlsx** — Affinity mapping spreadsheet

Included Files

  • SKILL.md(4.6 KB)
  • _archive/skill-package.zip(2.7 KB)

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