Dynamic Pricing Strategy Modeler
Build a pricing analysis workflow that ingests your product catalog, cost structure, competitor pricing, and demand elasticity estimates to recommend optimal price points, bundling strategies, and promotional discount guardrails.
pricing · margins · revenue optimization · competitive pricing · bundling
Use Code Interpreter to generate price sensitivity charts. Always include a minimum-margin floor constraint to prevent the model from recommending unsustainable prices. Test with A/B scenarios.
How to use this prompt
- Pick your AI model. Choose the tab for Claude, ChatGPT, Gemini or Copilot. Each variant is tuned for that model.
- Copy the full prompt. Click Copy Full Prompt to copy the text to your clipboard.
- Paste into your AI tool. Open your chosen model and paste the prompt into a new chat.
- Replace the [placeholders]. Swap any bracketed fields for your company name, audience, product or tone.
- Run and refine. Review the output. If anything is off, ask the AI to tighten tone, length or format.
Prompt Variants by Model
You are a pricing optimization consultant. Analyze my catalog and build a pricing strategy.
Product data: [PASTE: Name, Price, COGS, Units/Month, Category]
Competition: competitors and prices [LIST] · our positioning [PREMIUM/MID/VALUE] · price sensitivity notes [DESCRIBE]
Rules: minimum margin [X]% · max tolerable price increase [X]% · goal [MARKET SHARE / MARGIN / PENETRATION]
Past price changes, if any: [PRODUCT, OLD PRICE, NEW PRICE, DATE, UNITS BEFORE AND AFTER, or NONE]
Compute directly from the data (show the math, include charts):
1. Price audit — under/over-priced products vs competitors and margin
2. Recommended prices with margin impact and volume sensitivity
3. 3 bundles to lift AOV, with bundle prices
4. Promotional guardrails — max discount per product that stays profitable
5. 3 A/B tests to validate the changes
6. Revenue impact projection
Volume sensitivity in step 2 and the projection in step 6 come from my past price changes where I gave them. Where I gave none, the pasted data cannot show how buyers react to a price change, so show three cases labeled "assumption: low, middle and high sensitivity", state the percentages you assumed, and present none of them as measured.
Success means no recommendation breaks the minimum-margin or max-increase rules, and every number is reproducible from the pasted data or carries its stated assumption.
Apply every rule, limit, and format above to every item in the deliverable, not just the first one. Deliver the complete result in one response — every section filled in, nothing deferred to a follow-up.
If a required input is missing, ask for it once before starting; otherwise proceed and label any assumption you made.Notes: R40 refresh; tuned to vendor prompting guidance
Frequently Asked Questions
What does the Dynamic Pricing Strategy Modeler prompt do?
Build a pricing analysis workflow that ingests your product catalog, cost structure, competitor pricing, and demand elasticity estimates to recommend optimal price points, bundling strategies, and promotional discount guardrails.
Which AI models is this prompt written for?
Claude, ChatGPT, Gemini and Copilot. Each one gets its own version up top, written the way that vendor says to prompt its own model. Pick the tab that matches yours.
Do I need a paid AI account to use this prompt?
No. This prompt is written to run on the free tier of Claude, ChatGPT, Gemini and Copilot. Paid tiers simply give you longer context windows and faster responses.
Can I customize this prompt for my business?
Yes. Any text inside square brackets is a placeholder you replace with your own business details, such as company name, audience, product or tone. You can also ask the AI to adjust format, length or style after the first output.
When was this prompt last checked?
The date is on each version up top. We check every prompt in full every month, and when a major new model lands we rewrite the prompt for it. One glance tells you how current it is before you paste.
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