Free Voice of Customer Synthesis Prompt

Short answer: Cluster interviews, calls, reviews, and tickets into language the market actually uses.

Built by StackSwap · Updated August 28, 2026

OUTPUTQuote bank, themes, tensions, and message opportunities.
REPLACESFounder-memory messaging.
RUNS INChatGPT · Claude · Codex

BEFORE YOU COPY

Bring the context. Skip the blank page.

Collect the current offer, buyer language, alternatives including do-nothing, proof available, market frame, and the decision this message or strategy must change. The prompt separates facts from assumptions, compares viable paths, and produces the promised artifact instead of generic advice.

  1. Add contextProvide your company, buyer, motion, constraints, and decision.
  2. Run the workflowPaste the free prompt into ChatGPT, Claude, or Codex.
  3. Inspect the artifactReview assumptions, risks, actions, and the quality check.

FULL PROMPT · FREE FOREVER

Copy the full GTM workflow.

No account or email required. Copy it into ChatGPT, Claude, or Codex, add your context, and make the decision in front of you.

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## StackSwap execution contract

You are running a StackSwap operator workflow. Your job is to turn the user's real context into a decision-ready GTM artifact, not a generic explanation.

1. Start by extracting the objective, audience, motion, constraints, available evidence, decision, and definition of success.
2. If a missing fact would materially change the answer, ask up to 3 precise questions. Otherwise state reasonable assumptions and proceed.
3. Separate supplied facts, assumptions, unknowns, and recommendations. Never invent customer evidence, performance claims, market data, or proof.
4. Use the workflow below as the default operating method, adapting it to the user's context. Explain important trade-offs briefly.
5. Produce the promised artifact first. Make it copy-ready, specific enough to run, and structured for the user's actual team or buyer.
6. Include the evidence used, the verification or inspection loop, the main failure modes, and what would change the recommendation.
7. End with: Assumptions; Risks or failure modes; First 3 actions with owner and timing; and a short quality check showing what would make this artifact trustworthy.

### Output contract

Every workflow must make its output observable. Name the artifact, its required fields, the evidence or inputs behind each important claim, and the acceptance check that determines whether it is usable. If the workflow is a decision, show the viable alternatives, criteria, recommendation, runner-up, reversibility, and stop/continue rule. If the workflow is a copy-ready asset, include the final asset before commentary.

### Evidence and verification

Use the user's evidence first. Label sourced facts, assumptions, estimates, and recommendations. Prefer a small test, review, calculation, or comparison that can falsify the recommendation. Never treat an AI assertion as verification.

### Follow-on behavior

Name the next useful workflow only when it follows from the current artifact. Link the handoff to a concrete decision, missing evidence, or unresolved risk; do not recommend a generic tour of the library.

### Cross-platform behavior

This prompt is designed to work in ordinary chat, Claude, and Codex. Do not depend on hidden system instructions, a specific model, slash commands, or unavailable tools. If tools or files are available, use them only when they improve evidence quality; otherwise complete the workflow from the provided context.

---

---
name: voice-of-customer-synthesis
description: "Synthesize customer language into evidence-backed themes, message inputs, product signals, and unresolved questions."
allowed-tools: Read Write WebSearch WebFetch
metadata:
  author: Nick French / StackSwap
  version: '1.0'
  product: Operator Playbook
---

# Voice of Customer Synthesis

Voice-of-customer work preserves customer language while making the pattern, not the loudest quote, visible.

## When to use it

Use across interviews, calls, tickets, surveys, reviews, win/loss notes, and community evidence. Do not use it to cherry-pick quotes for a predetermined claim.

## Inputs to collect

Collect source set, dates, segments, sampling method, customer stage, research question, privacy constraints, and intended decisions.

## Method

1. Normalize and tag sources without stripping context or speaker role.
2. Separate direct quotes, observed behavior, repeated themes, interpretation, and outliers.
3. Cluster by problem, trigger, consequence, desired outcome, alternative, objection, and language.
4. Compare themes by segment, stage, win/loss, and frequency while preserving counterexamples.
5. Turn high-confidence themes into messages, product questions, and research gaps.

## Output

Produce a source register, theme map, quote bank with context, segment cuts, confidence notes, counterexamples, message inputs, product implications, and next research questions.

## Verification and failure modes

Audit claims back to source IDs and dates. Guard against quote laundering, sample bias, frequency-as-importance, stale evidence, and merging different jobs into one theme.

## Quality gate

Another operator must be able to trace every important theme to the source material and see what would disconfirm it.

Free forever. No email gate.

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QUESTIONS

About this free prompt

What does this voice of customer synthesis prompt help with?

Cluster interviews, calls, reviews, and tickets into language the market actually uses.

Who should use this voice of customer synthesis prompt?

This free GTM prompt is for B2B SaaS founders, GTM leaders, and RevOps operators who need a useful first draft without starting from a blank page.

What should I add before running this voice of customer synthesis prompt?

Add your company, buyer, GTM motion, constraints, and the decision you need to make. Better context produces a more specific artifact and makes weak assumptions easier to spot.

What output does this voice of customer synthesis prompt produce?

Quote bank, themes, tensions, and message opportunities. The workflow is designed to produce that artifact instead of generic GTM advice.

Can I use this voice of customer synthesis prompt in ChatGPT, Claude, or Codex?

Yes. The workflow is designed for ordinary chat, Claude, and Codex, with platform-specific formats available to copy for free.

How do I get a better result from this voice of customer synthesis prompt?

Include real customer language, current numbers, and hard constraints, then inspect the assumptions and risks in the result. Treat the first output as a decision artifact to improve, not an unquestionable answer.

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