customer-research-synthesis

name	customer-research-synthesis
description	Use when raw qualitative research — interview notes, survey responses, session recordings — needs to become structured insights that drive decisions. Triggers on: "인터뷰 정리", "고객 인터뷰 분석", "리서치 결과 정리", "설문 분석", "VOC 정리", UX research synthesis",
license	MIT
metadata	{"author":"wondelai","version":"1.1.0"}
scenarios	["I have 10 interview transcripts — help me find the patterns.","고객 인터뷰 결과를 정리해서 PRD에 넣을 인사이트로 만들어줘.","Synthesize these survey verbatims into themes with evidence counts.","인터뷰 노트에서 Jobs-to-be-Done을 뽑아줘.","Which of these research findings are real patterns vs. one-off anecdotes?","VOC 데이터를 우선순위 결정에 쓸 수 있게 정리해줘."]
compatibility	{"recommended":["think-tool","sequential-thinking"],"optional":["mcp-reasoner"],"remote_mcp_note":"think-tool이 있으면 패턴/일화 판정과 JTBD 추출의 논리를 검증하는 데 도움이 됩니다. sequential-thinking은 6단계 체인 전체 흐름 유지에 유효합니다. Claude 설정 → MCP Servers에서 remote SSE 엔드포인트를 추가하세요."}

Customer Research Synthesis

Raw quotes are not insights. An insight requires an observation, supporting evidence, and an implication for what to build or change.

Synthesis chain: Raw data → affinity clusters → Jobs-to-be-Done → insight cards → hypotheses

For the full step-by-step process with examples, load references/synthesis-process.md.

When to Use / When Not to Use

Use	Do Not Use
Raw interview notes needing thematic structure	Quantitative survey with closed-ended stats
Verbatim quotes needing n= pattern analysis	Competitive research (use competitive-analysis)
Discovery research feeding a PRD or roadmap	Product analytics / behavioral data (use metrics-interpretation)
Post-research readout that needs insight cards	Research planning or question design

Mode 1: Synthesize Raw Data

When the user provides raw interview notes, survey verbatims, or usability observations, produce:

1. Affinity clusters — grouped themes with n= counts (e.g., "Pain: slow onboarding — 6/10 participants")
2. Pattern vs. anecdote verdict for each cluster (see thresholds in references/synthesis-process.md Step 3)
3. Top 3 Jobs-to-be-Done extracted from the data

   Run Step 3 (pattern judgment) and Step 4 (JTBD extraction) in parallel after affinity mapping — they are independent.

4. Insight summary cards — one per major finding (observation + evidence + implication)
5. Recommended hypotheses — feeds ../hypothesis-driven-dev/SKILL.md

Mode 2: Evaluate Synthesis Practice

When the user asks "how good is our research process?" or shares their existing synthesis output for review, rate 0–10:

Score	State
9–10	Patterns documented with n= counts; JTBD extracted; insight cards connect to decisions; hypotheses written before building
7–8	Themes exist but evidence counts missing; insights stop at observation without implication
5–6	Research summarized as a quote list; no pattern analysis
3–4	"We talked to users" treated as sufficient; no structured output
1–2	No synthesis practice; decisions made without reference to research

What Claude Does / What You Do

Claude	You
Groups observations into affinity clusters with verb phrases	Conducts interviews and recordings
Counts n= occurrences and flags pattern vs. anecdote	Judges whether participants are representative
Extracts Jobs-to-be-Done using the JTBD template	Validates JTBD accuracy against live customer context
Writes insight cards (observation + evidence + implication)	Makes final decisions on what to build
Chains insights to testable hypotheses	Decides experiment priority and runs experiments

Related Skills

• ../feature-prioritization/SKILL.md — insight cards as prioritization evidence
• ../prd-development/SKILL.md — problem statement, personas, and Jobs-to-be-Done
• ../hypothesis-driven-dev/SKILL.md — validated hypotheses ready for experiment design
• ../pricing-monetization-strategy/SKILL.md — WTP signals from research
• ../product-discovery/SKILL.md — triggered after customer research phase completes