Free Win / Loss Analysis Prompt

## 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: win-loss-analysis
description: "Turn wins and losses into evidence-backed GTM learning about buyer choice, alternatives, messaging, product fit, and process."
allowed-tools: Read Write WebSearch WebFetch
metadata:
  author: Nick French / StackSwap
  version: '1.0'
  product: Operator Playbook
---

# Win / Loss Analysis

Win/loss work is not a story about who performed well. It is a structured attempt to learn why a buyer chose, delayed, or rejected a path.

## When to use it

Use after a meaningful win, loss, no-decision, or repeated pattern. Do not use it to generalize from one emotional interview or punish an individual rep.

## Inputs to collect

Collect deal context, segment, motion, stakeholders, alternatives, stage history, buyer language, pricing, proof used, implementation concerns, outcome, interview evidence, and sample size.

## Method

1. Separate observed facts, buyer-reported reasons, seller interpretation, and unknowns.
2. Compare wins, losses, and no-decisions by segment, trigger, alternative, message, product fit, process, and commercial terms.
3. Test whether a stated reason is causal, a proxy, or a polite explanation.
4. Identify repeatable patterns and counterexamples before changing strategy.
5. Convert learning into one message, product, qualification, enablement, or process experiment with an owner.

## Output

Produce a coded evidence set, pattern table, counterexamples, causal-confidence notes, segment cuts, implication map, prioritized experiments, and measurement plan.

## Verification and failure modes

Triangulate interviews with deal evidence and behavior. Guard against small-sample certainty, survivorship bias, seller-only narratives, post-hoc rationalization, and recommendations with no test.

## Quality gate

Every claimed lesson must name its evidence, confidence, counterexample, and the next experiment that could disprove it.

Free forever. No email gate.