voice-of-customer
Builds the loop from what customers say to what gets changed — collecting feedback, distinguishing signal from noise, routing it to owners, and closing the loop back to the customer. Use this to set up a feedback program, design or interpret CSAT/NPS, decide what customer feedback deserves action, get product to act on recurring issues, or diagnose why feedback is collected but nothing changes.
git clone --depth 1 https://github.com/cbrock84/headcount /tmp/voice-of-customer && cp -r /tmp/voice-of-customer/plugins/customer-experience/skills/voice-of-customer ~/.claude/skills/voice-of-customerSKILL.md
# Voice of customer Most feedback programs collect diligently and change nothing. The collection is the easy half; the loop is the whole value. ## Sources, weighted honestly - **Support contacts** — the highest-volume and least *prompted* source, and the most under-used. People contacting you have a real problem nobody asked them about. But the sample is strongly self-selected: it excludes everyone who silently churned, worked around the problem, or would never contact you. Treat it as operational evidence to be normalized per active account and triangulated against churn and behavioral data — never as representative of the customer base. - **Churn and loss reasons** — the most valuable and most under-sampled. People leaving have no reason to be polite. - **Interviews** — depth, small n, best for understanding *why* something in the data is happening. - **Surveys** — breadth, and only meaningful once you know what to ask. - **Public reviews and forums** — biased toward extremes, useful for what people say when you are not in the room. Anything a customer built a workaround for outranks anything they merely said in a survey. ## On CSAT and NPS Both are useful as trends and misleading as targets. The moment a team is measured on a score, the score improves faster than the experience does — asking at the favorable moment, coaching for the rating, excluding difficult segments. Treat the score as a prompt for the free-text answer, which is where the information is. Segment before concluding: an overall score is an average of experiences that have nothing in common. Never target a number without also watching the behavior it is supposed to predict. ## Turning feedback into change The failure is not collection, it is triage. Feedback needs: - **Categorization against a stable taxonomy**, so volume per cause is countable across periods. - **Quantification.** "Several customers mentioned" loses every argument. "Eighty-one contacts this quarter, four percent of active accounts, twelve of them on enterprise plans" wins. - **A named owner per theme**, outside the feedback function. A theme owned by the team collecting it goes nowhere. - **A standing review** where product, support, and success look at the same list together. Distinguish requests from problems. Customers describe solutions; your job is to recover the problem underneath, because the request is often not the best fix for it. ## Closing the loop Tell the customer what changed and that they prompted it. Almost nobody does this, which is exactly why it works — it converts a complainer into someone who reports the next issue instead of leaving. Also close it internally: show the support team what shipped because of what they escalated, or they stop escalating. ## Never - Report themes without volume. - Let one loud enterprise account set the roadmap without checking how widely the problem is shared. - Run a program with no mechanism for anything to change as a result. That is a survey habit, not a feedback loop.
Concentrates marketing and sales effort on a named set of accounts rather than on volume — qualifying whether the model fits your economics at all, building the account list and the buying group inside each, tiering effort against account value, coordinating so the account experiences one campaign rather than several, and measuring account progression instead of leads. Use this to decide whether to run an account-based program, build one, or work out why an existing one produces activity and no pipeline.
Designs orchestrator-and-subagent hierarchies for a repository — splitting agents by exclusive write surface, pairing every producer with an independent auditor, and enforcing the split with a script that runs in CI. Use this whenever the user wants to set up, expand, audit, or fix a multi-agent or subagent structure for a codebase; asks how to divide work between agents; wants agent charters, roles, or a surface map written; or is hitting agents that collide on the same files, review their own work, or drift from their remit. Also use when sizing a roster or deciding whether a new agent is justified.
Governs models and AI systems in production — intended use, evaluation, monitoring, human oversight, documentation, and the decision to deploy or retire. Use this before deploying a model or AI feature, when defining evaluation criteria, when a model's behavior has drifted, when assessing AI risk or regulatory exposure, or when deciding whether an AI system is fit for a consequential decision.
Produces executive-level research — market sizing, competitor mapping, trend analysis, and strategic intelligence — grounded in cited sources with the confidence in each claim made explicit. Use this to analyze a market or industry, map competitors, evaluate a market-entry or build-versus-buy decision, produce a research brief, or assemble evidence for a decision. Also use when comparing options that need a structured, evidence-based verdict rather than an opinion.
Optimizes for AI assistants and AI-generated answers — being retrievable, being cited, and being represented accurately when a model answers on your behalf. Use this when traffic is shifting from links to AI answers, when a brand is misrepresented or absent in AI responses, when planning content for retrieval rather than ranking, or when deciding how AI search changes an existing SEO program.
Improves visibility and conversion in the App Store and Google Play — metadata, keywords, screenshots, ratings, and the listing experience that turns an impression into an install. Use this to audit or optimize an app listing, plan a launch listing, diagnose poor install conversion, or improve store search visibility.
Protects and restores data — backup coverage and scope, retention, immutability against ransomware, and proving restores actually work. Use this to design a backup regime, verify restores, plan retention, protect backups from ransomware, or recover from data loss.
Runs the planning cycle — annual budget, rolling forecast, consolidation of business unit inputs, and the variance analysis that explains actuals against plan. Use this to build or challenge a budget, run a forecast cycle, consolidate departmental submissions, explain why results diverged from plan, or improve forecast accuracy.