Sales Forecast Calculator
Calculate accurate sales forecasts using pipeline stages, win rates, and historical data. Get commit, weighted, and best-case scenarios.
Pipeline by Stage
Enter the total dollar value in each pipeline stage
Historical Data (Optional)
Add historical data to improve forecast accuracy
How to Build Accurate Sales Forecasts
Sales forecasting predicts future revenue by analyzing your pipeline, historical data, and deal progression. Accurate forecasts enable better resource allocation, hiring decisions, and strategic planning.
The Weighted Pipeline Method
Each pipeline stage has an associated probability of closing. Multiply the value in each stage by its probability and sum the results for your weighted forecast.
Standard Stage Probabilities
Forecast Types
- Commit: Deals with 80%+ probability. What you're highly confident will close.
- Best Case: Total pipeline value if everything closed. Useful for upside planning.
- Weighted: Probability-adjusted forecast. Most accurate prediction.
- Worst Case: Only the highest-probability deals. Conservative estimate.
Pipeline Coverage Rules
Pipeline coverage = Total Pipeline / Target. General guidelines:
- 3x coverage: Standard for most B2B sales. Gives comfortable buffer.
- 4x coverage: Recommended for complex enterprise deals with long cycles.
- 2x coverage: May work for high-velocity sales with strong win rates.
Improving Forecast Accuracy
- Track historical accuracy: Compare forecasts to actuals and adjust probabilities.
- Use consistent definitions: Clear criteria for each stage reduces subjectivity.
- Update regularly: Stale forecasts become inaccurate quickly.
- Layer in qualitative data: Consider deal health, buyer engagement, and competitive dynamics.
- Account for seasonality: Some quarters naturally close more business.
Common Forecasting Mistakes
- Happy ears: Taking buyer interest as commitment
- Stale pipeline: Including deals that haven't moved in months
- Sandbagging: Under-reporting to beat targets easily
- Hockey stick: Assuming deals will close at quarter end
- Ignoring history: Not learning from past forecast accuracy
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