---
name: design-to-strategy-synthesis
description: "Synthesizes raw user research, behavioral analytics, and qualitative feedback into a prioritized UX strategy with a clear problem framing, 3-5 strategic bets, ranked design recommendations, success metrics, and a sequenced roadmap. Use when translating research findings or product data into an actionable design strategy, building a synthesis deck from a research dump, or defining what the design team should do next based on evidence."
argument-hint: "[research-inputs] [product-goal]"
allowed-tools: ["Read"]
---

# Design-to-Strategy Synthesis

## Purpose
Convert a messy pile of user research, analytics, and feedback into a focused UX strategy — naming the underlying problem, the strategic bets worth making, the ranked design moves that follow, and the metrics that prove they worked. Solves the gap between "we have a lot of data" and "we know what to build and why."

## When to Use
Invoke this skill when the user asks to:
- Turn user research findings or interview notes into design strategy
- Synthesize behavioral analytics and product data into recommendations
- Build a research synthesis or strategy deck from a research dump
- Decide what the design team should prioritize based on evidence
- Reconcile conflicting signals from qualitative feedback and quantitative data
- Frame a UX problem and propose a sequenced plan to address it

## Inputs Required
Before starting, collect:
- **Research inputs**: What evidence exists — usability tests, interviews, surveys, support tickets, NPS verbatims, session recordings
- **Behavioral data**: Quantitative signals — funnel drop-off, retention curves, feature adoption, task completion rates, heatmap patterns
- **Product goal**: The business or product outcome the strategy must serve (activation, retention, expansion, etc.)
- **User segment**: Who this strategy is for — primary persona or behavioral cohort
- **Constraints**: Engineering capacity, timeline, platform limits, or non-negotiables
- **Current hypotheses**: What the team already believes is wrong (to test against the evidence)
- **Stakeholder audience**: Who receives this strategy — design team, PM, exec leadership

If behavioral data is missing, work from qualitative inputs and flag where quantitative validation is needed before committing resources.

## Instructions

### Step 1: Cluster the Raw Signals
Group every input into evidence themes before drawing conclusions.
- Tag each finding as **qualitative** (what users say/feel) or **quantitative** (what users do)
- Cluster findings that point at the same underlying behavior — aim for 4-7 themes
- For each theme, note **signal strength**: Strong (multiple sources agree), Moderate (one strong source), Weak (anecdotal or single data point)
- Flag **contradictions** explicitly — where what users say conflicts with what they do. These are often the most strategically valuable.

### Step 2: Frame the Core Problem
Distill the clusters into one sharp problem statement.
- Write the problem as a single sentence: "[User segment] cannot/struggles to [job] because [evidence-backed cause], which costs [product impact]."
- Distinguish **symptoms** (drop-off, complaints) from the **root cause** (the design or model failure underneath)
- State what the data does NOT tell you — the known gaps in the evidence
- Avoid solutioning here. The problem frame must be defensible from the clusters alone.

### Step 3: Define 3-5 Strategic Bets
Translate the problem into directional choices, not features.
For each bet, specify:
- **The bet**: A directional statement of where to invest (e.g., "Reduce time-to-first-value rather than expanding feature breadth")
- **Why now**: The specific evidence cluster that justifies it
- **The wager**: What you're betting will be true if you pursue this
- **Risk if wrong**: What it costs to bet here and miss
Bets should be mutually distinct and force a real tradeoff — if all bets could be pursued simultaneously, they aren't bets.

### Step 4: Rank Design Recommendations
Produce 6-10 concrete recommendations laddering up to the bets.
For each recommendation:
- **The move**: A specific design or experience change
- **Bet it serves**: Which strategic bet it advances
- **Evidence link**: The finding(s) it responds to
- **Effort**: Low / Medium / High
- **Confidence**: High / Medium / Low — based on signal strength behind it
Rank using an impact-to-effort lens: prioritize High-confidence + High-impact + Low-to-Medium effort first.

### Step 5: Attach Success Metrics
For each strategic bet, define how you'll know it worked.
- One **leading indicator** (early behavioral signal, measurable in days/weeks)
- One **lagging outcome** (the product goal metric it ultimately moves)
- A directional target or threshold where one is reasonable to estimate
- The instrumentation needed if it isn't currently tracked

### Step 6: Sequence the Roadmap
Order the work into phases.
- **Phase 1 — Validate** (highest-confidence, lowest-effort moves + any tests to close evidence gaps)
- **Phase 2 — Build** (core bets that depend on Phase 1 validation)
- **Phase 3 — Expand** (higher-effort or lower-confidence moves contingent on earlier results)
State dependencies between phases and the decision point that gates moving forward.

## Output Format
- **Structure**: Strategy document organized by the six steps, with bets and recommendations in tables for scannability
- **Length**: 1,100-1,500 words
- **Format**: Structured document — prose for problem framing and bets; tables for recommendations and metrics; phased list for roadmap
- **Sections**: Evidence Themes → Core Problem → Strategic Bets → Ranked Recommendations → Success Metrics → Roadmap
- **Tone**: Decisive UX strategist — evidence-anchored, opinionated about priorities, willing to name what the data doesn't support

## Quality Checklist
Before delivering output, verify:
- [ ] Every strategic bet traces back to a named evidence theme — no bet floats free of data
- [ ] The problem statement separates root cause from symptoms and states the evidence gaps
- [ ] 3-5 bets are defined and force a genuine tradeoff (not all simultaneously pursuable)
- [ ] 6-10 recommendations each carry an effort rating and confidence rating
- [ ] Every bet has both a leading indicator and a lagging outcome metric
- [ ] At least one contradiction between qualitative and quantitative signals is surfaced (or its absence noted)

## Notes
- The most common failure in synthesis is jumping to recommendations before framing the problem — hold the line on Step 2 even when the answer feels obvious
- When qualitative and quantitative signals conflict, default to what users *do* over what they *say*, but investigate the gap rather than dismissing the verbatims
- Low-confidence recommendations belong in Phase 1 as cheap tests, not Phase 2 as committed builds — sequence by evidence strength, not enthusiasm
- If the inputs are thin, name the single highest-value research activity that would most reduce strategic uncertainty

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## How to use this skill

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**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)
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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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