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.
How to use it
- Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
- Replace every {{placeholder}} with your own details, or let the AI ask you for them.
- Use the follow-ups below to go deeper.
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
- Ask for any missing inputs before starting.
- Compare {{our_pricing}} against {{competitors}}, noting structure differences (tiers, discounts, bundling), not just headline price.
- Assess price sensitivity using {{customer_signal}}, flagging where evidence is thin.
- Recommend a pricing model or adjustment that fits {{cost_and_margin}} and the competitive position found.
- 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?