Purpose
Convert scattered, high-volume customer feedback into a ranked list of product priorities — each backed by frequency, severity, segment, and root cause — so product managers can defend roadmap decisions with evidence instead of anecdotes and close the loop with the customers who raised the issue.
When to Use
Invoke this skill when the user asks to:
- Synthesize support tickets, CSAT/NPS comments, and usage data into themes
- Turn a pile of feature requests into a prioritized roadmap input
- Build a quarterly customer insights or voice-of-customer report
- Identify the top drivers of churn or dissatisfaction from raw feedback
- Decide what to build next based on what customers are actually saying
- Find which complaints are high-frequency vs. high-severity (and which are both)
Inputs Required
Before starting, collect:
- Feedback data: The raw input — pasted tickets, survey verbatims, request lists, review excerpts, or a summary of each
- Feedback sources included: Which channels are represented (support, NPS, sales calls, in-app, reviews, churn surveys)
- Product area / scope: Whole product or a specific feature/surface area
- Customer segments: How to slice (plan tier, company size, industry, new vs. tenured) — and any segment to weight more heavily
- Strategic context: Current company priorities or themes the roadmap is organized around
- Volume context: Approximate total customer base or ticket volume, so frequency can be read as a percentage
- Output use: Roadmap input, exec report, or sprint planning — to calibrate depth and framing
If segments or volume context are missing, analyze across all feedback and flag where segment data would change the ranking.
Instructions
Step 1: Theme Clustering
Group every piece of feedback into themes rather than treating each item individually.
- Cluster by underlying need, not surface wording ("can't find the export button" and "exports take too long" may both be one Export friction theme, or two — decide based on root cause, not phrasing)
- Aim for 6–12 themes; more than 15 means clusters are too granular, fewer than 5 means they're too broad
- For each theme, capture: a clear name, a one-line definition, and 2–3 representative verbatim quotes
- Tag each theme as Bug / Usability / Missing capability / Performance / Pricing-or-packaging / Onboarding / Other
Step 2: Frequency & Severity Scoring
Score each theme on two independent axes — this is what separates loud-but-minor from quiet-but-critical.
Frequency (1–5): How many distinct customers or tickets raised it, ideally as a % of volume.
- 5 = raised by >20% of feedback; 1 = isolated (1–2 mentions)
Severity (1–5): How much it hurts the customer when it occurs.
- 5 = blocks core workflow / cited as a churn reason; 1 = minor annoyance or cosmetic
Plot every theme on a Frequency × Severity grid and label its quadrant:
- Critical (high freq, high sev): Fix now
- Targeted (low freq, high sev): High-value but niche — often retention drivers
- Polish (high freq, low sev): Cheap wins that lift satisfaction broadly
- Monitor (low freq, low sev): Watch, don't act
Step 3: Segment & Trend Analysis
- For each high-priority theme, note which segments raise it most (e.g., "85% of these came from Enterprise accounts")
- Flag any theme concentrated in a high-value segment — these punch above their raw frequency
- If timestamps or recency are available, mark themes as rising / steady / declining
- Cross-reference complaints with usage signals: is a complained-about feature heavily used (high-stakes) or barely touched (possibly an onboarding/discovery problem, not a feature problem)?
Step 4: Root-Cause Analysis
For each Critical and Targeted theme, go past the symptom.
- State the surface complaint vs. the likely root cause (a request for a feature is often a workaround for an unmet job-to-be-done)
- Classify the cause: product gap / discoverability gap / performance / expectation mismatch / documentation
- Note when multiple themes share one root cause — fixing it resolves several complaints at once (highest-leverage work)
Step 5: Ranked Priority List
Produce the top 8–10 priorities, ranked by a blend of severity, frequency, segment value, and root-cause leverage. For each:
- Priority name and quadrant
- The evidence: frequency %, severity, segment skew
- Root cause and recommended response (fix / build / clarify / monitor)
- Estimated effort signal: Quick win / Medium / Large initiative
- Expected outcome: what metric should move (churn, CSAT, activation, ticket volume)
Step 6: Closing the Loop
Define how to communicate back — feedback loops only work when customers see action.
- Which themes warrant a direct reply to the customers who raised them (high-severity, named accounts)
- Which warrant a public update (changelog, roadmap note, release announcement)
- A short message template for each tier acknowledging the input and stating what's happening
- A cadence recommendation for re-running this analysis (monthly for support trends, quarterly for roadmap)
Output Format
- Structure: Themes table → Frequency × Severity grid (described as a labeled quadrant breakdown) → segment/trend notes → root-cause analysis → ranked top 8–10 priority list → closing-the-loop plan
- Length: 1,000–1,400 words
- Format: Structured document with one table for themes and one for the ranked priorities; bulleted analysis between them
- Sections: Theme Clusters → Frequency & Severity Map → Segment & Trends → Root Causes → Ranked Priorities → Closing the Loop
- Tone: Product insights lead — evidence-first, decisive, willing to recommend not building something
Quality Checklist
Before delivering output, verify:
- [ ] Every piece of input feedback maps to a theme — nothing is left uncategorized
- [ ] Each theme has a frequency score, a severity score, and a quadrant label
- [ ] At least one theme is recommended as "monitor / don't build" — not everything is a priority
- [ ] The ranked priority list is 8–10 items ordered by a stated rationale, not by raw frequency alone
- [ ] Root-cause analysis distinguishes surface complaint from underlying cause for every Critical theme
- [ ] The closing-the-loop section includes at least one usable message template
Notes
- The most common failure mode is ranking by volume — a feature requested 50 times by free users may matter less than a churn driver mentioned 5 times by enterprise accounts. Always weight by segment value.
- When feedback is a feature request, treat the request as a hypothesis about a job-to-be-done, not a spec. The right build is often different from what was asked for.
- Themes that share a single root cause are the highest-leverage work in the report — surface them explicitly.
- This analysis is most valuable run on a fixed cadence; recommend the user save the theme structure so trends can be compared period over period.
How to use this skill
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- Drag this file into a new chat, or copy and paste its contents
- Send your task or request as your next message — the AI will apply the skill where it fits
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