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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.
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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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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