---
name: customer-research-synthesizer
description: "Synthesizes raw customer interview notes, support tickets, and survey feedback into a structured insights report: clustered pain points, frequency and severity scoring, jobs-to-be-done framing, mapped product opportunity areas, and a prioritized opportunity backlog. Use when turning unstructured customer research into prioritized product opportunities, consolidating findings across multiple interviews, or preparing research readouts for product decisions."
argument-hint: "[research-input] [product-area]"
allowed-tools: ["Read"]
---

# Customer Research Synthesizer

## Purpose
Convert raw, unstructured customer research — interview transcripts, support tickets, survey verbatims, sales call notes — into a prioritized insights report that maps clustered pain points to specific product opportunity areas. Eliminates the manual coding-and-clustering slog and produces a decision-ready output that connects what customers said to what the product team should build.

## When to Use
Invoke this skill when the user asks to:
- Synthesize interview notes or research findings into themes
- Turn raw customer feedback into prioritized pain points
- Cluster qualitative research across multiple interviews or sources
- Map customer problems to product opportunity areas
- Prepare a research readout or insights report for product decisions
- Identify the highest-priority problems worth solving from a feedback dump

## Inputs Required
Before starting, collect:
- **Raw research input**: Interview notes, transcripts, support tickets, survey verbatims, or call notes (paste or summarize)
- **Number and type of sources**: How many interviews/tickets, and what segment they came from
- **Customer segment(s)**: Who was researched — role, company size, industry, persona
- **Product area / scope**: The product or feature space the research relates to
- **Research question or goal**: What the team was trying to learn or decide
- **Known product opportunity areas**: Existing initiatives, themes, or roadmap buckets to map against (optional)
- **Prioritization lens**: What matters most — reach, revenue impact, retention, activation, or strategic fit

If product opportunity areas are not provided, derive them from the clustered pain points. If a prioritization lens is missing, default to a reach × severity model and state the assumption.

## Instructions

### Step 1: Extract and Tag Raw Observations
Read every source and pull discrete observations — one customer statement, behavior, or signal per item.
- Separate **observations** (what was said/done) from **interpretations** (what it means). Tag interpretations as inferred.
- For each observation, capture: the verbatim or paraphrase, the source ID, and the segment.
- Distinguish **stated needs** (explicit asks), **latent needs** (problems implied but not requested), and **emotional signals** (frustration, workaround fatigue, delight).
- Flag direct quotes worth preserving — they become evidence in the final report.

### Step 2: Cluster Into Pain Point Themes
Group related observations into named pain point clusters using affinity logic.
- Name each cluster as a **problem statement**, not a feature request — e.g., "Users lose trust when data sync status is invisible," not "Add a sync indicator."
- Each cluster should contain 2+ supporting observations; single-source signals go to a separate "Weak Signals" list (don't discard them).
- Apply the **Jobs-to-Be-Done** lens to each cluster: "When [situation], I want to [motivation], so I can [expected outcome]."

### Step 3: Score Each Pain Point
Rate every cluster on three dimensions, with brief reasoning per score:
- **Frequency / Reach** (High / Medium / Low): How many sources or segments raised it
- **Severity** (High / Medium / Low): How much it blocks the customer's job or threatens retention
- **Current workaround cost** (High / Medium / Low): How painful the existing coping mechanism is

Combine into a **Priority Score** (High / Medium / Low). High = frequent AND severe; surface high-severity/low-frequency items separately as "high-risk niche."

### Step 4: Map Pain Points to Product Opportunity Areas
For each high- and medium-priority pain point:
- Assign it to a product opportunity area (use provided buckets, or create 3–6 named areas if none exist).
- State the **opportunity hypothesis**: "If we [solve X], then [customer segment] will [outcome], which improves [business metric]."
- Note solution direction without designing the solution — keep it problem-anchored.

### Step 5: Build the Prioritized Opportunity Backlog
Rank opportunity areas using the prioritization lens, producing a ranked table. For each entry include: opportunity area, the pain point(s) it addresses, priority score, affected segment, and supporting evidence count.

### Step 6: Surface Contradictions, Gaps, and Confidence
- Note where segments disagree or feedback conflicts.
- Flag where evidence is thin and confidence is low.
- List the top 3 follow-up research questions that would resolve the biggest open uncertainties.

## Output Format
- **Structure**: Executive summary → clustered pain points with scores → JTBD framing → opportunity area mapping → prioritized backlog table → contradictions & open questions
- **Length**: 1,000–1,400 words
- **Format**: Structured document — prose summary up top, scored pain points as labeled blocks, opportunity backlog as a ranked table
- **Sections**: Executive Summary (3–4 sentences) → Pain Point Clusters (each with JTBD, scores, evidence) → Weak Signals → Product Opportunity Map → Prioritized Opportunity Backlog (table) → Contradictions & Confidence Gaps → Recommended Follow-Up Research
- **Tone**: Senior product researcher — evidence-grounded, decisive, free of hedging; every claim traceable to a source

## Quality Checklist
Before delivering output, verify:
- [ ] Every pain point cluster is named as a problem statement, not a feature request
- [ ] Each cluster has Frequency, Severity, and Workaround Cost scores with one-line reasoning
- [ ] Each high/medium pain point is mapped to a named product opportunity area
- [ ] The prioritized backlog is ranked by the stated prioritization lens — not arbitrary order
- [ ] Single-source signals are preserved in Weak Signals, not silently dropped
- [ ] At least 3 specific follow-up research questions are listed
- [ ] Observations are distinguished from inferred interpretations throughout

## Notes
- The most common failure mode is jumping to solutions — keep clusters anchored to the customer problem until the opportunity mapping step.
- High-severity, low-frequency pain points are easy to deprioritize wrongly; flagging them as "high-risk niche" prevents that.
- When research spans multiple segments, surface where their needs diverge — enterprise B2B roadmaps often break when one persona's problem is generalized to all.
- Preserve a few verbatim quotes per major theme; they carry more weight in stakeholder readouts than paraphrased summaries.

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## How to use this skill

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**Using in Claude.ai, ChatGPT, Gemini, or other AI tools:**

1. Drag this file into a new chat, or copy and paste its contents
2. Send your task or request as your next message — the AI will apply the skill where it fits

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