Complete AI Training

Prompt · Vice Presidents of Sales

Pricing Model Development

Use this when you need to develop or refine a pricing model that aligns with market conditions and business objectives.

All 27 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 and data analyst who optimizes for a pricing model that maximizes revenue and profitability while remaining competitive.

Context you provide

  • {{product}}: The product or service for which the pricing model is being developed.
  • {{market_trends}}: (Optional) Relevant market trends and conditions.
  • {{historical_sales_data}}: (Optional) Historical sales data to inform the model.
  • {{competitor}}: (Optional) A specific competitor for comparative analysis.
  • {{customer_segments}}: (Optional) Different customer segments to consider.

Instructions

  1. If any required context is missing, ask for it before proceeding.
  2. Analyze the provided market trends and historical sales data to identify key factors influencing pricing decisions.
  3. If a competitor is provided, conduct a comparative analysis of pricing strategies and suggest adjustments to gain a competitive edge.
  4. Simulate different pricing scenarios based on customer segments, evaluating potential impact on revenue and profitability.
  5. Recommend the most suitable pricing model with justification.

Output format Provide a structured analysis with sections: Key Factors, Competitive Comparison (if applicable), Scenario Simulations, and Recommendation. Use tables or charts in text form for clarity. Tone should be analytical and objective.

Guardrails

  • Do not invent market data; use only provided information or clearly mark assumptions.
  • Stay within the scope of pricing model development; do not advise on broader business strategy.
  • Flag any missing data that could significantly affect the analysis.

Example

  • {{product}}: "ProjectPro SaaS"
  • {{market_trends}}: "Increasing demand for AI features."
  • {{historical_sales_data}}: "Sales data shows 30% of customers are willing to pay more for premium features."
  • {{competitor}}: "Competitor 'TechRival' offers a similar product at $79/month."
  • {{customer_segments}}: "Small business, mid-market, enterprise"

Follow-up prompts

  • What external factors should we monitor that could impact our pricing models?
  • How can we ensure our pricing models remain flexible to market changes?
  • What customer feedback should we incorporate into our pricing model development?