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Trade promo post-mortem that tells the truth

The event is over and the lift number looks good in the deck. Did the promotion actually pay, or did it just buy volume forward?

The prompt — copy and run it

You are a trade promotion analyst writing an honest post-event review. I will paste the event results. Produce:

A) A VERDICT — paid out, broke even, or bought volume forward — with the arithmetic shown step by step using only the numbers I provide (base vs incremental volume, lift, deal depth, feature/display support, event cost if I have it). If the payout math cannot be completed from my numbers, say exactly which input is missing rather than estimating it.
B) DIAGNOSTIC QUESTIONS for what the data cannot see: forward-buying, pantry loading, cannibalization of my own items, and halo — each phrased as a specific check I can run.
C) THREE GUARDRAILS for the next event: a depth cap, a support requirement (feature/display condition), and a timing rule — each tied to what this event's numbers showed.

Event data: [PASTE: base volume, incremental volume, lift %, deal depth, support type, retailer, dates, cost if known]

Rules: Do not invent, estimate, or extrapolate any figure — if a number is not in the data I give you, write "not provided" and flag it. Mark every claim I should verify against my syndicated data or internal reporting before using it externally. Never include retailer-confidential terms or personally identifiable shopper data.

How to use this prompt

  1. Copy the full prompt above with the Copy button.
  2. Add your context. This prompt runs as-is — paste it, then add the specific details, data, or files it should reason over.
  3. Paste into ChatGPT, Claude, or Gemini and run. Read the reality guardrail below before you act on the output.

Why this prompt works

Trade is most CPG companies' second-largest cost line and the post-mortem is usually a victory lap. Making the model show its payout arithmetic — and admit which inputs are missing — is the difference between a real read and a slide that launders a bad event.

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Reality guardrail: this prompt makes the model reason from data you paste — it does not source or verify facts for you. Check every claim, keep confidential data out of consumer AI tools, and follow your employer's AI-use policy.

Frequently asked

When should I use this prompt?

The event is over and the lift number looks good in the deck. Did the promotion actually pay, or did it just buy volume forward?

Why does this prompt work?

Trade is most CPG companies' second-largest cost line and the post-mortem is usually a victory lap. Making the model show its payout arithmetic — and admit which inputs are missing — is the difference between a real read and a slide that launders a bad event.

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PromptSharp prompts are drafted with AI assistance and human-reviewed. They structure how a model reasons over data you provide — they do not source or verify facts for you, and you own every output. Nothing here is financial, legal, tax, or investment advice. Never paste confidential, client, or material non-public information into consumer AI tools; follow your employer's AI-use policy. © 2026 PromptSharp.