b2KIT

Sales Forecast Calculator

Build sales forecasts using pipeline, historical trends, and growth rate methods with monthly/quarterly revenue projections.

Tested tool guide Tested browser tools Checked August 16, 2026

What Sales Forecast Calculator does, with a checked example

A sales forecast is a guess with arithmetic, and this calculator makes the arithmetic visible. It projects revenue three ways: expected pipeline value (each open deal counted at its win probability), historical trends carried forward from recent periods, and growth rates compounded over months or quarters. The surprise most users hit is the gap between the pipeline total and the forecast: a $60,000 pipeline weighted by realistic probabilities can project $24,000, and that gap is the method working, not an error. The output is an expected value, not a promise.

Worked example

A concrete input and expected output from the current implementation.

Input

Three deals open in the pipeline: Deal A $10,000 at 80% win probability, Deal B $20,000 at 50%, Deal C $30,000 at 20%.

Expected output

Expected revenue for the period: $24,000. The deals' face value totals $60,000.

Each deal contributes its value times its probability: 8,000 + 10,000 + 6,000 = 24,000. The weighted sum is the forecast; $60,000 is what the pipeline would be worth only if every deal closed.

How the result is produced

1

Pipeline method: probability weighting

Each open deal contributes its value times its win probability, so a $10,000 deal at 80% adds $8,000 to the period forecast while a $30,000 deal at 20% adds only $6,000. The forecast is the sum of these weighted contributions. The result is an expected value - the average outcome if this pipeline were repeated many times - not a count of what will close.

2

Trend and growth extrapolation

The historical-trend method averages or extends recent periods, treating the pattern of past months as the baseline for the next one. The growth-rate method compounds multiplicatively: the rate applies to each successive period, so three quarters of 5% growth multiply revenue by 1.05 x 1.05 x 1.05, about 15.8% total, not 15%.

Good uses

  • Turning a CRM pipeline into a quarterly revenue expectation: weight open deals by stage probability instead of counting them at face value.
  • Stress-testing a growth claim before it goes in a board deck - what a promised 10% monthly growth rate actually means over six months (about 77% cumulative) once compounding is applied.
  • Building a next-month baseline from history when there is no pipeline - a cash-and-carry retailer averaging the last three months of sales.

Limits and checks

  • Expected value is not a guarantee. A $24,000 forecast from a $60,000 pipeline has wide variance; if the deals share a customer or a market, outcomes cluster near all-or-nothing, not near the average.
  • The probabilities are your inputs, not measured facts. The calculator multiplies whatever win likelihoods you enter, so optimistic stage percentages produce optimistic forecasts; it cannot validate that an 80% stage truly closes 80% of the time.
  • Every method assumes its past or its rate holds. Trend forecasts miss seasonality, price changes, and account losses; growth-rate forecasts are sensitive to small rate differences - 5% versus 7% quarterly becomes 21.6% versus 31.1% over a year.

Common questions

Why is my forecast much lower than the total value of my open deals?

Because each deal is discounted by the probability you assigned it. Only a deal at 100% counts in full; the others count at fractions of their value. The difference between the pipeline's face value and the expected total is the point of the method - it converts optimistic totals into a realistic central estimate.

Can this calculator tell me whether we will hit our sales target?

No - it produces a central estimate from whatever inputs you give it, not a certainty. Actual revenue scatters around the forecast, and how wide the scatter is depends on how reliable your probabilities and growth rates are. Use it to size the gap to the target and test scenarios, not to promise a number to the board. Inputs stay in the browser and are not uploaded.

References and verification

The example and behavioral notes were checked against the browser implementation. Standards and primary references below define the relevant format, formula, or platform behavior.

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