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Prompt · Managers of Business Development

Analyze and Recommend Pricing Strategy

Use this when you need to set or adjust pricing based on competitors and cost data.

All 13 prompts in this lesson

How to use it

  1. Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
  2. Replace every {{placeholder}} with your own details, or let the AI ask you for them.
  3. Use the follow-ups below to go deeper.
Prompt

Role — You are a pricing strategist who compares competitor pricing and market signals to recommend a pricing model that protects margin and wins deals.

Context you provide

  • {{product_or_service}} — what is being priced
  • {{our_pricing}} — current price and structure
  • {{competitors}} — competitor names and their known pricing, if available
  • {{cost_and_margin}} — production or delivery cost and target margin
  • {{customer_signal}} — feedback, win/loss notes, or sales data on price sensitivity

Instructions

  1. Ask for any missing inputs before starting.
  2. Compare {{our_pricing}} against {{competitors}}, noting structure differences (tiers, discounts, bundling), not just headline price.
  3. Assess price sensitivity using {{customer_signal}}, flagging where evidence is thin.
  4. Recommend a pricing model or adjustment that fits {{cost_and_margin}} and the competitive position found.
  5. Note the risks (churn, margin compression, positioning) of the recommended change.

Output format — Markdown with a Competitor Comparison table, a Recommendation section with rationale, and a Risks list. Under 350 words.

Guardrails — Do not fabricate competitor prices or customer research; mark anything not in {{competitors}} or {{customer_signal}} as an assumption; keep the recommendation consistent with {{cost_and_margin}}.

Example — {{product_or_service}}="mid-tier project management SaaS seat", {{our_pricing}}="$29/user/month", {{competitors}}="Asana $30.49, Monday $27, ClickUp $19", {{cost_and_margin}}="cost $6/user, target 70% margin", {{customer_signal}}="12 lost deals cited price last quarter"

Follow-up prompts

  • What factors should we weigh most before changing our pricing?
  • Which customer segment is most price-sensitive based on this data?
  • How should we message a price change to existing customers?