Product

---
name: user-research-synthesis
description: Synthesize raw user-research data into personas, a journey map, and prioritized insight statements.
triggers: [synthesize user research, affinity mapping interviews, jobs to be done analysis, build user personas, empathy and journey mapping, write insight statements, prioritize research findings]
tags: [product, research, ux]
core: false
context_cost: medium
---
# User Research Synthesis

## Goal
Convert raw, messy research data — interview notes, survey responses, support transcripts, observation
logs — into structured, decision-ready artifacts: synthesized personas, a journey map, and a prioritized
list of insight statements. Synthesis is the act of finding the signal in noise: turning individual
anecdotes into patterns, and patterns into insights that change what the team builds. The discipline is
staying grounded in evidence so insights are *discovered*, not invented.

## Steps
1. **Tag and affinity-map the raw data.**
   - Break notes into atomic observations (one quote or fact per unit), then cluster by similarity into
     affinity groups. Name each group by the pattern it represents, not the topic it covers.
   - Track how many distinct participants support each cluster — a pattern seen once is an anecdote, a
     pattern seen across many participants is a finding.
2. **Extract Jobs-To-Be-Done (JTBD).**
   - For recurring goals, write job statements in the canonical form: "When [situation], I want to [motivation],
     so I can [expected outcome]." JTBD captures the progress a user is trying to make, independent of any
     specific solution, which keeps the team from over-fitting to current features.
3. **Synthesize personas.**
   - Build personas as composites grounded in the clusters — goals, jobs, pains, and context — not
     demographic stereotypes. Each persona attribute should trace to observed evidence.
   - Keep the set small (2-4); too many personas dilute focus.
4. **Map the journey.**
   - For the primary persona, lay out the end-to-end journey in stages. For each stage capture actions,
     thoughts, emotional highs/lows, and pain points. Mark the moments of greatest friction — these are
     where the journey map earns its keep.
5. **Write insight statements.**
   - Convert findings into insight statements that pair an observation with its implication:
     "We observed [pattern]; this matters because [consequence]; therefore [opportunity]." An insight is
     not a raw fact — it carries a "so what."
6. **Prioritize the insights.**
   - Rank insights by evidence strength (how many participants) and potential impact, separating
     high-confidence, high-impact insights from interesting-but-thin ones.

## Constraints
- Every persona attribute, journey pain point, and insight MUST trace back to observed data; the agent
  MUST NOT invent quotes, metrics, or behaviors to fill gaps, and labels any inferred claim as inference.
- Distinguish what users *say* from what they *do*; weight observed behavior over stated preference when
  they conflict.
- Note the evidence base for each insight (number of participants, method) so consumers can judge how far
  to trust it — a finding from 2 interviews is not the same as one from 30.
- Personas are synthesized composites, never real individuals; do not reproduce a single participant as a
  persona.

## Output Format
Produce a synthesis package containing:
- 2-4 personas, each with goals, primary JTBD statements, key pains, and context, every attribute grounded
  in evidence.
- A journey map for the primary persona: stages with actions, emotions, and flagged friction points.
- A prioritized list of insight statements (observation → implication → opportunity), each tagged with its
  evidence strength (participant count / method) and a confidence level.

## Security & Guardrails

### 1. Skill Security
- **Risk**: Confirmation-biased synthesis — the agent surfaces only patterns matching a stakeholder's prior
  hypothesis and buries disconfirming data; mitigation: the agent MUST report contradicting observations
  alongside supporting ones and never drop a cluster solely because it conflicts with a desired conclusion.
- **Risk**: Evidence inflation — a single anecdote is presented as a widespread pattern; mitigation: every
  finding carries its participant count and the agent refuses to label an n=1 observation as a "pattern."

### 2. System Integration Security
- **Risk**: PII exposure — raw research contains real names, contact details, recordings, and sensitive
  disclosures; mitigation: the agent strips and pseudonymizes all participant identifiers before any
  persona or insight is emitted, and keeps raw transcripts in a restricted working artifact only.
- **Risk**: Re-identification — a "synthetic" persona reproduces one participant closely enough to identify
  them; mitigation: the agent composites across multiple participants and removes uniquely identifying
  detail so no persona maps to a single real person.

### 3. LLM & Agent Guardrails
- **Risk**: Hallucinated evidence — the model fabricates a verbatim quote or statistic to support a tidy
  insight; mitigation: quotes are used only verbatim from source and any number must trace to data, with
  unsupported claims marked as inference rather than fact.
- **Risk**: Sampling and demographic bias — synthesis over-represents the loudest or most available segment
  and erases under-served users; mitigation: the agent reports who is and is not represented in the sample
  and flags segments with no coverage as gaps rather than silently generalizing.