Sales Forecast Calculator
Use this Sales Forecast Calculator to convert your current revenue and monthly growth assumptions into clear, numeric projections. It computes end-period revenue, aggregate revenue across the forecast horizon, average monthly revenue, and an optional churn-adjusted projection.
This tool is intended for short- to medium-term planning (1–60 months). It uses standard compound growth formulas so you can compare scenarios or run sensitivity checks by changing growth and churn inputs.
Inputs
Results
Forecasted revenue after N months
$12,682.42
Total projected revenue over N months (includes month 0)
$134,120.90
Average monthly revenue over N months
—
Churn-adjusted revenue after N months
$11,956.18
Implied annual growth rate (from monthly growth)
2682.42%
| Output | Value | Unit |
|---|---|---|
| Forecasted revenue after N months | $12,682.42 | currency |
| Total projected revenue over N months (includes month 0) | $134,120.90 | currency |
| Average monthly revenue over N months | — | currency |
| Churn-adjusted revenue after N months | $11,956.18 | currency |
| Implied annual growth rate (from monthly growth) | 2682.42% | percent |
Visualization
Methodology
Primary method: discrete monthly compound growth. Each month is modeled as prior_month_revenue × (1 + monthly_growth_rate). This yields an end-period value of current_revenue × (1 + g)^N where g is the monthly growth rate and N is the number of months.
Total revenue for the forecast horizon is calculated using the geometric series sum for a sequence of monthly values. A separate churn-adjusted projection uses net monthly growth = monthly_growth_rate - monthly_churn_rate to estimate retained revenue under steady churn assumptions.
This calculator is deterministic and rule-based. For improved accuracy with rich historical data consider statistical time-series models (moving averages, ARIMA, seasonal decomposition) and validate forecasts using backtesting and forecast accuracy metrics such as Mean Absolute Percentage Error (MAPE).
Worked examples
Example: current_revenue = 10,000, monthly_growth_pct = 2, periods = 12 → forecasted_revenue_end = 10,000 × (1.02)^12 ≈ 12,682.
Example with churn: current_revenue = 10,000, monthly_growth_pct = 2, monthly_churn_pct = 0.5, periods = 12 → churn-adjusted uses net monthly growth 1.5%.
Further resources
External guidance
Expert Q&A
What inputs produce the most reliable forecasts?
Quality forecasts start with accurate base-period revenue and realistic month-over-month growth estimates derived from historical trends, marketing plans, and pipeline conversion rates. Use backtesting against past months to calibrate the monthly growth rate.
How should I choose the forecast horizon?
Short horizons (1–6 months) are more reliable for operational planning; medium horizons (6–24 months) help budgeting; longer horizons require scenario analysis and should incorporate structural assumptions (new products, pricing changes, market shifts).
Why does the total projected revenue formula divide by the growth rate?
The division arises from summing a geometric series of monthly values. If you expect zero growth, interpret the total as current_revenue × periods (a flat-line scenario).
How do I account for seasonality or step changes?
For seasonality or discrete changes, run scenario runs per season (adjust monthly_growth_pct for each period) or use a statistical time-series model that supports seasonal components. This calculator assumes constant monthly growth for simplicity.
How can I measure forecast accuracy?
Track actuals versus forecast and compute accuracy metrics such as MAPE, MAE, or RMSE. Use historical holdouts (backtesting) to estimate expected error and widen scenario bands accordingly.
Is churn handled automatically?
Churn is modeled as a steady monthly percentage that reduces net growth. For cohort-based churn or non-linear retention, use cohort analysis or a customer lifetime value model for more precision.
What are the tool's limitations?
This deterministic calculator uses simple compound growth and constant churn assumptions. It does not infer seasonality, correlations, or volatility from raw historical series—statistical forecasting models are needed for those capabilities.
Sources & citations
- U.S. Small Business Administration — Financial Forecasting — https://www.sba.gov/business-guide/plan-your-business/market-research-competitive-analysis
- Bureau of Labor Statistics — Data and Methods — https://www.bls.gov/
- MIT OpenCourseWare — Time Series and Forecasting resources — https://ocw.mit.edu
- National Institute of Standards and Technology (NIST) — Reference on quantitative methods — https://www.nist.gov