Audit your current pricing model for structural gaps
Most pricing problems aren't about the number — they're about the metric. This prompt walks through your current model to expose misalignments between how you charge and how customers get value.
I want to audit the pricing model for {{PRODUCT_NAME}}. Here is how we currently charge: {{CURRENT_PRICING_MODEL}}. Our primary customer segment is {{PRIMARY_SEGMENT}}. Our average contract value is {{ACV}}.
1. Identify the pricing metric we use (per seat, per usage, per outcome, flat fee, etc.) and evaluate whether it correlates with the value the customer receives. State explicitly if it does not.
2. Check for 'value leakage': situations where customers get significant value but pay the same as customers who get little. Describe one concrete example based on the segment I provided.
3. Check for 'expansion friction': does our model make it easy or hard for a customer to spend more as they grow? Identify the specific point where expansion becomes awkward.
4. Suggest one alternative pricing metric that would better align cost with value delivery. Explain the tradeoff: what does the business gain, and what operational complexity does it add?
5. List the top two objections a sales rep would face if we switched to that alternative metric, and draft a one-sentence response to each.
Note: if {{CURRENT_PRICING_MODEL}} is a marketplace or two-sided platform model, flag that this prompt applies only partially — take-rate dynamics need separate analysis. {{PRODUCT_NAME}}{{CURRENT_PRICING_MODEL}}{{PRIMARY_SEGMENT}}{{ACV}}
How to use this prompt
- Copy the prompt above (Copy button on the top-right).
- Replace each
{{VAR}}with your own value. Variables:{{PRODUCT_NAME}}{{CURRENT_PRICING_MODEL}}{{PRIMARY_SEGMENT}}{{ACV}}. - Paste it into one of the recommended tools below.
- Iterate: tighten constraints in the prompt if the output is generic.
Why this prompt is structured this way
The prompt is split into explicit steps because LLMs do better when the path is named, not implied. Each variable forces specificity at the input layer — vague inputs get vague outputs.
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