Model revenue projections with growth rates, seasonal adjustments, new product launches, and pipeline conversion assumptions.
A revenue forecast is arithmetic with assumptions attached, and the assumptions decide whether the result is useful. This tool projects revenue period by period from a starting figure, applying a growth rate, seasonal adjustments, new product launches, and pipeline conversion that turns deal value and close probability into expected revenue. It produces the full table, so you can see which quarter a launch shifts or how much a seasonal spike matters. What users most often get wrong is compounding: growth multiplies each period on the previous period's result, so an 8% monthly rate is not 96% a year, and treating rates as additive understates the forecast.
A concrete input and expected output from the current implementation.
Input
Starting annual revenue: $240,000. Annual growth: 25%. Seasonal weights by quarter: Q1 0.80, Q2 1.00, Q3 1.05, Q4 1.15. Forecast the next 4 quarters.
Expected output
Q1: $60,000, Q2: $75,000, Q3: $78,750, Q4: $86,250, Year total: $300,000
The average quarter is $75,000 ($240,000 x 1.25 growth, divided by 4). The weights sum to 4.00, so they redistribute the growth-adjusted total without changing it: Q1 runs 20% below average and Q4 runs 15% above. If the weights summed to anything other than 4, the annual total would move with them.
Growth is applied per period, so each period's revenue is the previous period's revenue multiplied by (1 plus the growth rate). That makes the rate compound: 8% monthly compounds to about 152% over a year, not 96%. Seasonal factors then scale each period around an index of 1.0, and launch streams add revenue beginning in the period you choose.
The pipeline section weights each open deal by its estimated close probability and schedules the expected value into the period of the expected close date. Every input is a single editable parameter, so changing the close rate, a seasonal weight, or the launch timing recomputes the whole table - which is how you find the assumption the forecast leans on most.
Only if the weights do not average to 1.0. Twelve monthly weights summing to 12, or four quarterly weights to 4, simply redistribute the year's revenue between periods, and the annual total stays exactly what the growth model says. Weights that average higher quietly add growth, lower ones subtract it, so summing them is a quick check on the whole forecast.
Because pipeline conversion is expectation-based: each open deal contributes deal value times close probability. A portfolio that is 40% likely to close adds 40% of its value even though no individual deal is certain. The forecast is a statistical average, reasonable across many deals but overconfident for any single quarter, so watch the gap between expected and committed revenue.
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