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Design-to-strategy synthesis

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…

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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

How to use this skill

This file is a Claude Code skill in Anthropic's standard format — 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

For Claude Code skill details: https://docs.anthropic.com/en/docs/claude-code/skills