---
name: quant-qual-data-synthesis-tool
description: "Synthesizes mixed quantitative metrics and qualitative research observations into a triangulated findings report — identifying where the numbers and the stories agree, contradict, or explain each other, then converting each pattern into a prioritized, actionable design implication. Use when reconciling analytics with user interviews, triangulating survey data with usability sessions, or turning a pile of mixed research into design decisions."
argument-hint: "[research-question] [data-sources]"
allowed-tools: ["Read"]
---

# Quant + Qual Data Synthesis Tool

## Purpose
Merge quantitative signals (analytics, survey stats, funnel metrics) with qualitative evidence (interview quotes, usability observations, open-text feedback) into a single triangulated set of findings — surfacing where the data converges, diverges, or explains each other — and translate every validated pattern into a specific, prioritized design implication a team can act on.

## When to Use
Invoke this skill when the user asks to:
- Combine analytics data with user interview or usability findings
- Triangulate survey results against qualitative feedback
- Make sense of a mixed pile of quant and qual research
- Figure out why a metric is moving using qualitative evidence
- Turn research findings into concrete design or product decisions
- Resolve a contradiction between what users say and what the data shows

## Inputs Required
Before starting, collect:
- **Research question**: The primary question or decision this synthesis needs to inform
- **Quantitative data**: Metrics, funnel numbers, survey stats, feature usage, drop-off rates, satisfaction scores — with sample sizes where available
- **Qualitative data**: Interview quotes, usability observations, open-text responses, support tickets, session notes
- **Segments (if any)**: User segments, personas, plans, or cohorts the data can be split by
- **Study context**: How and when the data was collected (recency, method, any known bias)
- **Decision at stake**: What the team plans to do with these findings (roadmap, redesign, prioritization)
- **Confidence tolerance**: How much certainty the decision needs (exploratory vs. high-stakes)

If sample sizes or study context are missing, flag confidence limits explicitly in the findings rather than overstating certainty.

## Instructions

### Step 1: Normalize and Inventory the Evidence
Before finding patterns, organize the raw inputs.
- **Tag each data point** as QUANT or QUAL, and by the topic/theme it touches (e.g., onboarding, pricing, navigation)
- **Note the strength of each quant metric**: sample size, statistical reliability, and whether it's a leading or lagging indicator
- **Cluster qual observations** into themes — group quotes and observations that describe the same underlying behavior or friction
- Produce a short inventory: how many quant metrics, how many qual observations, and how many distinct themes emerged

### Step 2: Triangulate — Map Quant Against Qual per Theme
For each theme, classify the relationship between the numbers and the stories into one of four types:

- **Convergence** — Quant and qual point the same direction. High confidence. Example: 42% drop-off at step 3 (quant) + five users saying "I didn't know what to enter here" (qual).
- **Explanation** — Qual explains the *why* behind a quant signal. The metric tells you what; the observation tells you the cause.
- **Contradiction** — Quant and qual disagree. This is the most valuable and most dangerous zone. Resolve by checking: Is one sample biased? Is there a segment split? Are they measuring different moments in the journey?
- **Gap** — A signal exists in one dataset with no counterpart in the other. Flag as needing follow-up research; do not treat as validated.

For each theme, state the relationship type explicitly and cite the specific evidence on both sides.

### Step 3: Resolve Contradictions and Segment Splits
When quant and qual disagree:
- **Check for a hidden segment**: A metric averaged across all users often masks two opposing behaviors. Split by the available segments and re-examine.
- **Check for the say/do gap**: Stated preference (qual) often diverges from actual behavior (quant). Weight behavioral data higher for what users *do*; weight qual higher for *why*.
- **Check sample validity**: A vocal 5 interviewees can contradict a statistically significant 5,000-user metric — note which carries more weight for this specific question.
- Document the resolution and the residual uncertainty.

### Step 4: Rank Findings by Confidence and Impact
Score each validated finding on two axes:
- **Confidence** (High / Medium / Low): based on convergence, sample size, and method quality
- **Impact** (High / Medium / Low): based on how many users affected × severity × relevance to the decision at stake

Rank findings High-confidence + High-impact first. Findings that are Low-confidence but High-impact become research recommendations, not decisions.

### Step 5: Translate Each Finding into a Design Implication
For every ranked finding, produce an actionable implication with this structure:
- **Finding**: One-sentence statement of the validated pattern
- **Evidence**: The specific quant + qual data supporting it (with numbers and a representative quote)
- **Relationship type**: Convergence / Explanation / Contradiction resolved / Gap
- **Design implication**: The concrete change or direction this suggests — a specific hypothesis, not "improve onboarding"
- **Confidence**: High / Medium / Low, with the reason
- **Suggested next step**: Ship-and-measure, prototype-and-test, or investigate-further

### Step 6: Identify Open Questions and Research Gaps
List what remains unresolved — the gaps from Step 2 and the low-confidence findings — and recommend the specific follow-up method (analytics query, targeted interview, A/B test) that would close each gap.

## Output Format
- **Structure**: Evidence inventory → triangulation matrix by theme → ranked findings with design implications → open questions
- **Length**: 1,000–1,400 words
- **Format**: Structured document; triangulation summary as a table (Theme | Quant Signal | Qual Signal | Relationship | Confidence); findings as structured blocks
- **Sections**: Evidence Inventory → Triangulation Matrix → Ranked Findings & Design Implications → Open Questions & Recommended Research
- **Tone**: Rigorous and evidence-first — a senior researcher who cites data, resists overclaiming, and never turns a single quote into a mandate

## Quality Checklist
Before delivering output, verify:
- [ ] Every finding cites both a quantitative and qualitative data point, or explicitly notes when only one exists
- [ ] Each theme in the triangulation matrix is labeled with one of the four relationship types (Convergence / Explanation / Contradiction / Gap)
- [ ] Every contradiction includes an attempted resolution (segment split, say/do gap, or sample weighting) — not left unexplained
- [ ] Findings are ranked by confidence and impact — not listed in input order
- [ ] Each design implication is specific and testable — no "improve X" placeholders
- [ ] Confidence levels reflect actual sample sizes and method quality, not enthusiasm for the finding

## Notes
- The say/do gap is the most common source of quant-qual contradiction: users report what they wish were true; behavioral metrics report what actually happened. When in doubt about *behavior*, trust the numbers; when in doubt about *motivation*, trust the interviews.
- A statistically significant metric with no qualitative explanation is a signal to go interview — a strong number you can't explain is a risk, not a decision.
- Resist the pull to let one memorable quote override the data. A vivid anecdote and a representative pattern are not the same thing; label which is which.
- Contradictions are the highest-value output of this synthesis — they almost always reveal a hidden segment or a measurement mismatch worth surfacing to the team.

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## How to use this skill

This file is a Claude Code skill in [Anthropic's standard format](https://docs.anthropic.com/en/docs/claude-code/skills) — markdown with YAML frontmatter at the top.

**Installing in Claude Code:**
- `~/.claude/skills/<folder-name>/SKILL.md` — auto-loads in every session anywhere on your machine (recommended for skills you'll use across multiple projects)
- `<your-project>/.claude/skills/<folder-name>/SKILL.md` — auto-loads only when working in that project (recommended for project-specific skills)

Restart Claude Code or open a new session to register.

**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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