As of 13 August 2026, AI can create a subscription pricing model.

What to actually do

1. Hand it to a person

   The route this page recommends

   A person who owns the outcome does this end to end, worth it when the failure is dear.

2. Use a tool built for this

   Second choice

3. Do it yourself

   The distant third

   A chat interface, chat-fluent skill, and roughly 1 hour until you can act on the result.

   How to actually do it

   1. Open a new spreadsheet in Rows and create tabs for inputs, assumptions, model, scenarios and checks.
   2. Gather your current costs, customer numbers, churn or retention data, acquisition data, billing terms, discounts, refunds, payment fees and support costs, and record the source and period for each figure.
   3. Paste the prompt into an AI chat and replace each bracketed slot with your business information, marking unavailable data as unavailable rather than asking the model to estimate it.
   4. Paste the model output into Rows and ask it to turn the input table, formulas and scenarios into separate spreadsheet sections with GBP formatting and explicit units.
   5. Compare every imported input and formula against your accounts, current price list and customer records, then correct any unit, period, VAT or one-off cost error.
   6. Add evidence from customer interviews, trials or competitor research to the assumptions tab and rerun the scenarios to see whether the proposed range still holds.
   7. Send the checked model and its assumptions to the person responsible for commercial approval, then test the chosen price with customers before publishing it.

   Prompt

   Act as a commercial pricing analyst. Create a subscription pricing model for [product or service] in GBP, using only the information I provide and clearly labelling every assumption or estimate. Do not invent customer behaviour, competitor facts, costs or market data.
   
   Business context:
   - Target customers: [customer segments]
   - Product and included features: [description]
   - Billing options being considered: [monthly, annual or other terms]
   - Current or expected customer numbers: [figures and period]
   - Current or expected acquisition rate: [figures and period]
   - Churn or retention data: [figures and period, or say unavailable]
   - Variable cost per customer: [figure and basis]
   - Fixed operating costs: [figures and period]
   - Payment processing, platform and support costs: [figures and basis]
   - Discounts, refunds and free trials: [terms]
   - Usage limits or overage charges: [terms]
   - Tax and VAT treatment: [known treatment or say needs confirmation]
   - Commercial objective: [growth, margin, cash flow, retention or other]
   - Constraints: [minimum margin, price ceiling, implementation limits or other]
   - Evidence from customers or competitors: [paste sources and dates, or say unavailable]
   
   Produce:
   1. A clean input table with units and periods made explicit.
   2. A pricing model with clearly named inputs and spreadsheet-ready formulas.
   3. A comparison of the proposed pricing options, including revenue, variable costs, gross margin, customer count and cash timing where the supplied data supports it.
   4. A set of conservative, central and optimistic scenarios, with the assumptions that change in each one.
   5. A sensitivity analysis showing which assumptions have the greatest effect on the result.
   6. A list of missing inputs that materially limit the model.
   7. A recommended price range only if the supplied evidence supports one. Explain the trade-offs and do not present the recommendation as a fact.
   8. A short list of tests to run with real customers before publishing prices.
   
   Keep one-off implementation costs separate from recurring costs, keep VAT separate where relevant, and do not use unsupported competitor or willingness-to-pay claims. End with the exact checks I must perform before using the model in a pricing decision.

   Open it prefilled in ChatGPT or Claude, or copy it into Gemini, which takes no prefill link.