Work · GPT Task
Plan and Test a Pricing Strategy
Turn supplied offer, customer, current-price, cost, market, and willingness-to-pay evidence into pricing-model hypotheses, packaging options, tests, measurement criteria, uncertainty notes, and user-reviewable next decisions.
Context → structured prompt → explicit AI handoff
Runs in this browser. No upload by this page. External AI handoff is explicit. Review before sharing.
Configure task
Additional options
What you’ll need
- Offer and pricing objective plus any real cost, current-price, customer, competitor, or willingness-to-pay evidence you have
- Offer, customer context, and pricing objective
- Known costs, current prices, and customer evidence
What you’ll get
- Factual pricing baseline and evidence gaps
- Pricing objective and decision criteria
- Pricing-model hypotheses
- Packaging/tier hypotheses
- Perceived-value factors and proof needs
Prompt preview
Review or edit the generated prompt before you copy it or explicitly open an AI chat.
ChatGPT opens with the current prompt prepared. Gemini copies the current prompt, then opens Gemini so you can paste it. Keep secrets and sensitive information out of URL-carried prompts.
Examples
1. SaaS tier review
Context: A SaaS company supplies current monthly prices, plan entitlements, cost notes, customer interview findings, and a goal to review its tier structure.
Goal: Develop tiering hypotheses and a qualitative test plan.
Useful output: Evidence baseline, tier hypotheses, tradeoffs, customer questions, proof gaps, measurement criteria, and next decisions.
2. Service pricing research
Context: A service business supplies current ranges, delivery capacity, actual cost inputs, client feedback, and packaging constraints.
Goal: Plan pricing research without pretending willingness-to-pay is known.
Useful output: Baseline, fixed/tiered hypotheses, qualitative research plan, assumptions, legal/tax questions, and decision criteria.
3. Usage-based exploration
Context: A product team supplies usage patterns, current flat pricing, known costs, customer objections, and no validated value metric yet.
Goal: Explore usage/value-based hypotheses and scenario tests.
Useful output: Candidate value metrics, evidence gaps, scenario structure, test plan, stop/continue criteria, and risk notes.
Expected output
- Factual pricing baseline and evidence gaps
- Pricing objective and decision criteria
- Pricing-model hypotheses
- Packaging/tier hypotheses
- Perceived-value factors and proof needs
- Test or research plan
- Measurement and stop/continue criteria
- Legal/tax/current-rule questions to verify
- Assumptions, risks, and unsupported claims
- Priority next decisions/actions
Truth boundary
General business planning only. Do not invent willingness-to-pay evidence, costs, legal/tax requirements or demand. Do not guarantee revenue or profit. Keep prices, assumptions and tests user-reviewable.
Overlap guard: Owns contextual pricing-model, packaging, and testing hypotheses. Use Client Proposal/SOW for client-specific commercial terms, High-Ticket Offer for premium package design, Upsell/Cross-Sell for offer-pairing strategy, and deterministic Tools for margin/markup/price arithmetic.
Privacy and handoff
Runs in this browser. No upload by this page. Copy/download locally or explicitly open ChatGPT or Gemini. Source files are attached in the chosen AI service, not uploaded by Mycelgrid. Review the generated prompt and AI output before using or sharing it.