Customer Signals
Turn feedback into evidence-backed decisions
Customer Signals
Turn feedback into evidence-backed decisions
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Description
There’s plenty of feedback—but what’s actually worth solving? Customer Signals organizes customer interviews, surveys, reviews, support tickets, and churn feedback. It extracts exact quotes, pain points, trigger events, and alternative approaches. It merges duplicate materials, preserves counterexamples, and separates research findings from opportunities that still need validation. It supports research synthesis, a VOC quote library, JTBD, provisional user personas, competitor review comparisons, and research gap plans. It can also design public community research, customer interviews, and PMF surveys. Online collection depends on the host’s available tools; without internet access, it provides search and sampling plans. For product managers, indie developers, operators, and small teams. Complaints do not equal willingness to pay, so the research marks evidence boundaries and identifies the next steps for validation. Published by: leowang. Adapted from customer-research v2.0.4 under the MIT License, with the required copyright notice retained.
Recommended by
Dancang
Why we love this skill
Turns customer quotes into traceable, testable judgments while distinguishing emotion, frequency, and actual behavior.
Best for
For product managers, indie developers, operators, and small teams turning customer feedback into product judgments they can validate.
How to use this Skill
Provide your input
Provide customer interviews, surveys, reviews, support tickets, win or churn feedback, or describe the target audience, product context, research question, and source of the materials.
Run the Skill
Organize customer quotes, merge duplicates, extract pain points, trigger events, alternatives, counterexamples, and evidence boundaries, and separate confirmed findings from opportunities to validate.
Review your result
Get a research synthesis with sources and distinct counts, a VOC quote library, JTBD, provisional personas, competitor review comparisons, or interview and survey research plans.
Related Skills
View all
ResearchInterview-to-Insights V3
Turn interview, user research, or focus-group transcripts into an evidence-based research synthesis where every conclusion is traceable to a supporting quote. From one transcript to twenty, get coded themes, a tension map, an opportunity backlog, and direct answers to your decision questions—no more hours of recordings with zero findings. For businesses and product teams: save days of analyst time spent organizing and coding. Every claim is anchored to participants’ exact words and timestamps, making it auditable and traceable while reducing the risk of AI-fabricated insights. Opportunities are presented as “when [situation], [who] wants [outcome] because [reason]”, ready to connect with product roadmaps and market decisions. For researchers and academics: follow rigorous qualitative research practice rather than simple summarization—quote-first open coding, Behaviour/Belief/Wish evidence categories, falsifiable themes, and deliberate searches for disconfirming evidence. The method is rigorous enough for a research paper. Sample discipline is strict: percentages are not reported with fewer than 12 participants, n=1 is identified as a hypothesis rather than a finding, and conclusion strength is based on participant counts. Ideal for product managers, UX and market researchers, journalists, consultants, founders, and anyone with hours of recordings but no research findings yet. English: Turn interview, user research, or focus-group transcripts into an evidence-led research synthesis where every conclusion is traceable to a verbatim quote. Feed it one transcript or twenty, and get coded themes, a tension map, an opportunity backlog, and a direct answer to your decision question—no more hours of recordings with zero findings. For businesses & product teams: skip days of manual coding; every claim is anchored to participant quotes with timestamps, auditable and traceable, eliminating the risk of AI-fabricated insights; opportunities are framed as "when [situation], [who] wants [outcome] because [reason]", ready to feed product roadmaps and market decisions. For researchers & academics: real qualitative practice, not summarisation—quote-first open coding, Behaviour/Belief/Wish evidence tagging, falsifiable themes, deliberate disconfirming-evidence hunting, rigorous enough for a methods section; strict sample discipline: no percentages under 12 participants, n=1 flagged as hypothesis rather than finding, theme strength counted in participants. For product managers, UX & market researchers, journalists, consultants, founders doing customer discovery—and anyone sitting on hours of recordings with no findings.
User Interview Question Builder
Design high-quality user interview questions, guides, and follow-ups for product research, customer discovery, UX research, and feature validation—and turn interview evidence into product decisions.
Can Your Business Idea Scale?
One-person company founders and side-hustle explorers: before you invest time and money, do a final calm check with customer data. Supports uploading business plans/BP (PDF/Word/PPT). Unlike the free version—which gives you a quick diagnosis and 3 interview questions to go talk to customers—the full version picks up after you've talked to customers: use customers' real answers (not your own feelings) to rescore the five switches, apply the one-vote veto system, automatically match validation experiments, GO gives a scaling roadmap, PIVOT gives a hypothesis adjustment path, and NO GO helps you extract reusable assets. If you haven't talked to customers yet, the full version can also give you interview questions directly—but it's recommended to first get the quick diagnosis and questions from the free version, talk to customers, and come back with their verbatim responses. The five switches: Is the need real? Can users represent the masses? Can it still run without you? What happens when you scale it 100x? Does selling more make more profit or more loss? If one switch breaks, the whole thing breaks—one-vote veto. Output includes: a customer-data-driven scoring report + 3 types of validation experiment plans (interview / landing page / Wizard of Oz, automatically matched) + GO → scaling roadmap + PIVOT → hypothesis adjustment path + NO GO → reusable asset extraction. Methodology: John List's The Voltage Effect + Rob Fitzpatrick's The Mom Test + Bland & Osterwalder's Testing Business Ideas.
Information
- Version
- v3
- Last updated
- Runtime credits
- Usage-based
- Models
- Auto
- Use cases
- Research & analysisBusiness & strategy
- What you get
- ReportDocumentDiagram
