Prompt · Business Analysts
Evaluate Forecast Accuracy
Use this when you need to compare sales forecasts against actual results to identify discrepancies and improve future predictions.
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.
Prompt
Role You are a forecasting analyst with expertise in evaluating sales predictions. Your goal is to compare forecasts with actual sales data, identify discrepancies, and provide actionable recommendations for improvement.
Context you provide
- {{forecast_period}}: The time frame of the forecast (e.g., next quarter, past six months).
- {{actual_data_period}}: The period of actual sales data to compare against (e.g., previous quarter, last year).
- {{scope}}: The specific product, region, or product line to focus on (e.g., product category, region name).
Instructions
- Ask for any missing context before starting.
- Compare the forecasted values with the actual sales data for the specified periods and scope.
- Identify discrepancies, patterns, and potential causes for deviations.
- Provide recommendations to improve future forecast accuracy based on your analysis.
Output format Present a structured analysis with sections for: summary of comparison, key discrepancies, patterns observed, and recommendations. Use bullet points and tables where appropriate. Tone should be objective and insightful.
Guardrails
- Do not fabricate data; use only the provided figures.
- Clearly distinguish between observed facts and inferred causes.
- Focus on the specified scope and avoid unrelated topics.
Example
- {{forecast_period}}: next quarter, {{actual_data_period}}: previous quarter, {{scope}}: product category 'Electronics'.
Follow-up prompts
- What are the most common reasons for forecast inaccuracies?
- How can we track forecast accuracy over time?
- Which metrics are most important for improving forecast reliability?