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Aggregates and synthesizes user feedback from support tickets, NPS, in-app feedback, sales calls, social mentions, and customer councils into a continuous decision signal through triaged synthesis.
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/rampstack-skills:user-feedback-aggregationThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
A senior product leader's playbook for collecting and synthesizing user feedback across channels into continuous decision signal. Support tickets, NPS surveys, in-app feedback, sales calls, social mentions, customer councils, all aggregated into a triaged synthesis the team can actually act on.
references/categorization-and-tagging-at-scale.mdreferences/channel-source-weighting.mdreferences/channel-types-and-what-each-surfaces.mdreferences/closing-the-loop-with-users.mdreferences/common-feedback-aggregation-failures.mdreferences/detecting-drift-in-feedback.mdreferences/frequency-vs-intensity.mdreferences/from-feedback-to-product-decision.mdreferences/tooling-considerations.mdA senior product leader's playbook for collecting and synthesizing user feedback across channels into continuous decision signal. Support tickets, NPS surveys, in-app feedback, sales calls, social mentions, customer councils, all aggregated into a triaged synthesis the team can actually act on.
Most product programs accumulate feedback they do not use. Channels overflow with submissions; CSAT and NPS scores get reported in monthly updates; customer council meetings produce notes that nobody references. The loudest voices steer roadmap because they are easiest to hear; quieter signal that matters more goes unaddressed.
This skill is the triage discipline that turns continuous feedback streams into continuous decision signal. Each channel surfaces different signal at different reliability. Each signal type warrants different weight in different decisions. The team that aggregates feedback well makes better decisions; the team that drowns in feedback makes the same decisions they would have made without the feedback.
Different from discovery-research-synthesis, which covers one-off research projects (a defined batch of artifacts, a defined synthesis output). This skill covers the always-on streams: feedback that arrives every day, every week, every month, and that the team must continuously triage and synthesize.
The voice is the senior product leader who has watched feedback aggregation work and watched it fail. Concrete, opinionated about which channels matter for which decisions, willing to call out where loudest-voice or averaged-noise patterns produce bad outcomes.
When to use this skill: building a feedback aggregation system, auditing a feedback program that is producing volume without decisions, deciding which feedback channels matter for the program, or designing the synthesis cadence for ongoing feedback streams.
This skill spans continuous user-feedback aggregation. The PM-skill distinction:
discovery-research-synthesis is one-off discovery research. Defined batch, defined output.user-feedback-aggregation (this skill) is ongoing feedback streams. Always-on channels needing continuous synthesis.beta-program-management covers feedback specific to beta participants. This skill spans all users continuously.ux-research is structured research projects. This skill is unsolicited feedback.pm-spec-writing is downstream: specs use feedback aggregation as input.roadmap-planning is downstream: roadmap uses feedback patterns as input.The audience: senior PMs, product directors, customer success and support managers running feedback programs, in-house teams aggregating feedback across many channels.
What is not in scope: structured discovery research (covered by discovery-research-synthesis); beta-specific feedback (covered by beta-program-management); commissioned research projects (covered by ux-research).
The keystone framing.
Loudest-voice. Whoever complains the most gets their feature. Vocal minorities steer roadmap. Silent majority's needs go unaddressed. The squeaky-wheel anti-pattern. Cost: the program optimizes for the loudest customer, not for the broader customer base; quieter customers leave for products that addressed their needs.
Averaged-noise. Every signal weighted equally. 1 enterprise customer's feedback = 1 trial user's feedback = 1 angry social mention. Volume aggregates without weighting; noise drowns signal. Cost: the team cannot tell which feedback matters; decisions get made on aggregate sentiment that does not reflect the customers who matter most for the strategy.
Triaged-synthesis. Signal weighted by source quality, frequency, and decision relevance. Different feedback types have different weights for different decisions. The team that does this well makes decisions grounded in the signal that matters; the team that does it badly defaults to loudest-voice or averaged-noise.
The litmus test. Pick a recent product decision. Can the team explain why specific feedback signals weighted into the decision the way they did? If yes, the aggregation is triaged. If the answer is "we heard a lot about this from customers," without specificity about which customers in which contexts, the aggregation is loudest-voice or averaged-noise.
Six common channels. Each surfaces different signal at different reliability.
Support tickets. What users hit when things break or confuse them. High volume, high signal-on-friction, biased toward users willing to contact support.
NPS surveys. Aggregate sentiment toward the product or specific features. Periodic, quantitative-with-comments, bias toward respondents who feel strongly (positive or negative).
In-app feedback. Friction at the moment users encounter it. Contextualized to specific product moments; volume varies; quality varies.
Sales calls. Pre-purchase prospect perspective. Objections, competitive mentions, gaps between marketing and what sales leads with. Bias toward prospects, not existing customers.
Social mentions. Public commentary about the product. Real-time but biased toward customers willing to post publicly; often skews negative or hyper-positive.
Customer councils. Curated panels of customers in structured forums. High-quality feedback from selected customers; bias depends on selection.
Each channel has strengths and weaknesses. Strong feedback aggregation triangulates across channels rather than relying on one.
Detail in references/channel-types-and-what-each-surfaces.md.
The discipline that distinguishes triaged-synthesis from averaged-noise.
The principle. Different sources warrant different weights in different decisions.
Worked example. A decision about whether to prioritize an enterprise admin feature.
The discipline. For each decision, identify which sources matter most. Weight accordingly. Volume from low-weight sources should not override signal from high-weight sources.
The averaged-noise failure. Decisions made on aggregate volume regardless of source. The enterprise admin feature gets deprioritized because more total feedback came from small-team customers who do not need it; enterprise customers churn because their needs went unaddressed.
Detail in references/channel-source-weighting.md.
How feedback gets organized when it arrives at high volume.
The taxonomy.
The discipline.
The volume challenge. A program receiving 500-2000 feedback items per week needs aggregation tooling that supports tagging at scale. Manual tagging at scale burns out the team; AI-assisted tagging with human review on patterns is often the practical middle path.
Detail in references/categorization-and-tagging-at-scale.md.
Two dimensions of feedback signal.
Frequency. How often the same feedback recurs.
Intensity. How strongly the user feels.
The matrix.
The discipline. Triage assigns each piece of feedback a frequency-intensity assessment. The combination informs prioritization weight.
Detail in references/frequency-vs-intensity.md.
The synthesis loop that turns aggregated feedback into roadmap input.
The loop.
The cadences.
The discipline. Feedback aggregation has cadence. Without cadence, feedback accumulates without producing decisions.
Detail in references/from-feedback-to-product-decision.md.
When feedback shapes product, telling users matters.
The practice.
Why it matters.
The risk of over-promising. Promising changes that do not ship erodes trust faster than not promising. Communicate what shipped, not what might ship.
Detail in references/closing-the-loop-with-users.md.
What changes in feedback signal over time can reveal.
Drift patterns.
The investigation discipline. Drift signals warrant investigation. Why did the volume increase? Why did this pattern emerge? The root cause often informs product decisions more than the surface signal.
Detail in references/detecting-drift-in-feedback.md.
Tooling choices without endorsing specific products.
Tooling categories.
The criteria for tooling selection.
The build-vs-buy tension. Many programs end up with a mix: dedicated tooling for some channels, custom dashboards for cross-channel synthesis. Pure single-tool solutions often miss the multi-channel pattern detection.
Detail in references/tooling-considerations.md.
Rapid-fire. Diagnoses in references/common-feedback-aggregation-failures.md.
When designing or auditing a feedback aggregation system, walk these 12 considerations.
The output of the framework is feedback aggregation that produces decision-grade signal continuously, scales with program volume, and treats users well enough that they keep providing feedback over time.
references/channel-types-and-what-each-surfaces.md - Six common channels with strengths, weaknesses, biases, and synthesis implications.references/channel-source-weighting.md - Decision-relative weighting. Worked examples of how channels weight differently for different decisions.references/categorization-and-tagging-at-scale.md - Taxonomy that emerges from data. Tooling at volume. Periodic taxonomy review.references/frequency-vs-intensity.md - The two dimensions of feedback signal. The four-quadrant matrix and prioritization implications.references/from-feedback-to-product-decision.md - The synthesis loop. Cadences. The discipline of decisions traceable to feedback.references/closing-the-loop-with-users.md - When and how to communicate feedback-driven changes. The over-promising risk.references/detecting-drift-in-feedback.md - Drift patterns. Investigation discipline. Drift as early signal of change.references/tooling-considerations.md - Tooling categories without specific endorsements. Criteria for selection. Build-vs-buy tension.references/common-feedback-aggregation-failures.md - 11+ failure patterns with diagnoses and cures.The teams that make good product decisions over time are the ones that aggregate feedback continuously and synthesize it into decision input. Not as compliance with a feedback-collection mandate; not as monthly NPS reporting; not as ad-hoc reactions to whoever complains loudest. As a continuous discipline that surfaces what users are actually experiencing, weighted appropriately, synthesized for the decisions the team is actually making.
Feedback aggregation done well is invisible: the team makes better decisions, but the feedback work itself does not feel heroic. Feedback aggregation done badly is loud: channels overflow, NPS gets reported, customer councils produce decks, and decisions still get made on gut feel because the feedback work did not produce decision input.
The discipline is in the triage. Each channel weighted by what it reveals. Each piece of feedback assessed on frequency and intensity. Patterns surfaced through synthesis. Decisions traceable to specific feedback. The closed loop with users that keeps feedback flowing.
When in doubt about whether a feedback program is working, ask: can the team trace recent decisions to specific feedback patterns, are channels weighted differently for different decisions, is the synthesis cadence producing input the team uses, are users seeing their feedback acted on? If yes to all of those, the program is real. If no to any, the gap is where the feedback work is failing to convert into decisions.
npx claudepluginhub rampstackco/claude-skills --plugin rampstack-skillsCategorizes, scores, and prioritizes customer feedback from support tickets, reviews, and surveys into actionable reports with feature request rankings and sentiment trends.
Aggregate customer feedback across channels into structured insights that inform strategy.
Use this skill when the user asks to "triage feedback", "analyze support tickets", "cluster feedback", "analyze NPS responses", "what are users complaining about", "find pain points in this feedback", "synthesize this customer feedback", or pastes a batch of raw feedback, tickets, or interview notes. This skill is for structured feedback triage and scoring. For interview-specific synthesis, use discovery/continuous-interview-synthesis. For full research synthesis with OST mapping, use /synthesize-research.