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Planogram critique: does the shelf follow the shopper's decision tree?

A retailer sent a draft planogram and you have one meeting to argue for a different flow.

The prompt — copy and run it

You are a category advisor critiquing a draft planogram against the category's shopper decision tree. I will paste the proposed flow and any decision-tree evidence. Produce:

A) A FLOW ASSESSMENT — for each block/segment in the proposal, state what shopping decision it assumes shoppers make first, and whether my evidence supports that order.
B) A FRICTION LIST — the three specific places a shopper is most likely to miss an item or substitute out of the category, each with the shelf reason (adjacency, vertical vs horizontal block, eye-level allocation, sightline break).
C) THREE PROPOSED CHANGES, each written as a retailer-facing sentence that leads with the CATEGORY benefit (trip conversion, basket, shopper ease) and only then names any of my items — plus the measure you would agree in advance to judge each change.

Inputs: [PASTE PLANOGRAM DESCRIPTION OR SEGMENT ORDER, SHELF DIMENSIONS/FACINGS, DECISION-TREE OR SHOPPER RESEARCH, CATEGORY SALES BY SEGMENT]

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. Every recommendation must be defensible as category-first; flag any suggestion that only benefits my brand.

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

Planogram arguments fail when they read as a facings grab. Anchoring every change to the decision tree, naming the friction it removes, and pre-agreeing the measure turns a self-interested ask into the kind of category-captain reasoning merchants actually reward.

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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?

A retailer sent a draft planogram and you have one meeting to argue for a different flow.

Why does this prompt work?

Planogram arguments fail when they read as a facings grab. Anchoring every change to the decision tree, naming the friction it removes, and pre-agreeing the measure turns a self-interested ask into the kind of category-captain reasoning merchants actually reward.

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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.