What inputs does the Sales Forecast Calculator need?

It takes 6 inputs: startingMrr, leadsPerMonth, conversionRate, arpuPerCustomer, monthlyChurn, horizonMonths (default 12). Outputs returned: trajectory, endingMrr, cumulativeRevenue.

Can I verify the Sales Forecast Calculator with a worked example?

Yes. With startingMrr = $500, leads = 200, conv = 2%, arpu = $25, churn = 4%, horizon = 12. the tool returns endingMrr ≈ $1,700, cumulative ≈ $13,500 over 12 months at baseline inputs.

Does the Sales Forecast Calculator bundle any external benchmark data?

No. It runs standard arithmetic on the values you enter; no external benchmark dataset is bundled. Industry references, where relevant, are cited on the adjacent tools' methodology pages.

What can the Sales Forecast Calculator not tell me?

Known limitations: Deterministic, not probabilistic. A ±20% range on any input cascades across the horizon; the tool does not surface that uncertainty. No seasonality layer. E-commerce and consumer products with Q4 spikes will be off by a meaningful margin.

Methodology: Sales Forecast Calculator

1. Scope

Projects MRR and cumulative revenue over a fixed horizon using deterministic growth, conversion, and pipeline assumptions. It is not a probabilistic forecast and does not quantify forecast error.

2. Inputs and outputs

Inputs

startingMrr number (currency)
leadsPerMonth number
conversionRate percent
arpuPerCustomer number (currency)
monthlyChurn percent
horizonMonths number default: 12

Outputs

trajectory

Array of monthly MRR, new, churned, and net new MRR.
endingMrr

MRR at the final month.
cumulativeRevenue

Sum of MRR across the horizon.

Engine source: src/lib/sales-forecast-calculator/engine.ts

3. Formula / scoring logic

for m = 1..horizon:
  new_customers = leads_per_month * conversion_rate
  new_mrr       = new_customers * arpu
  churned_mrr   = mrr_{m-1} * monthly_churn
  mrr_m         = mrr_{m-1} + new_mrr - churned_mrr

4. Assumptions

Lead volume and conversion are constant. Ramp periods (new channel launching) require running the tool in segments.
Churn is applied to the prior-month MRR in aggregate; cohort-level retention curves would produce different results.
ARPU is flat — no price changes, no tier upgrades.

5. Data sources

This tool relies on user inputs and standard arithmetic; no external benchmark data is bundled. When a question depends on an industry reference (for example, typical churn rates or hourly-wage medians), the linked adjacent tools cite their primary sources on their own methodology pages.

6. Known limitations

Deterministic, not probabilistic. A ±20% range on any input cascades across the horizon; the tool does not surface that uncertainty.
No seasonality layer. E-commerce and consumer products with Q4 spikes will be off by a meaningful margin.

7. Reproducibility

Input
startingMrr = $500, leads = 200, conv = 2%, arpu = $25, churn = 4%, horizon = 12.

Expected output
endingMrr ≈ $1,700, cumulative ≈ $13,500 over 12 months at baseline inputs.

8. Change log

2026-04-24 methodology page first published.

Worked example

Run live against the same engine this site ships (/engines/sales-forecast-calculator.js). The inputs and outputs below are recomputed on every build and independently re-verified in CI — they are never hand-authored.

Input

tool: sales_forecast
starting_mrr: 80000
monthly_growth_percent: 5
pipeline_conversion_percent: 22
avg_deal_size: 1200
new_opportunities_per_month: 40
months: 12

Output

primaryLabel: Projected MRR at horizon
primaryValue: 311753.36
primaryFormat: currency
summary: Combines organic growth with conversion-weighted pipeline additions each month.
metrics[0].label: Cumulative forecast revenue
metrics[0].value: 2332420.6
metrics[0].format: currency
metrics[1].label: Monthly pipeline contribution
metrics[1].value: 10560
metrics[1].format: currency
metrics[2].label: Growth rate
metrics[2].value: 5
metrics[2].format: percent
metrics[3].label: Forecast horizon
metrics[3].value: 12
metrics[3].format: months
assumptionsEcho.starting_mrr: 80000
assumptionsEcho.monthly_growth_percent: 5
assumptionsEcho.pipeline_conversion_percent: 22
assumptionsEcho.avg_deal_size: 1200
assumptionsEcho.new_opportunities_per_month: 40
assumptionsEcho.months: 12

Frequently asked questions

What does the Sales Forecast Calculator calculate?: Projects MRR and cumulative revenue over a fixed horizon using deterministic growth, conversion, and pipeline assumptions. It is not a probabilistic forecast and does not quantify forecast error.
What inputs does the Sales Forecast Calculator need?: It takes 6 inputs: startingMrr, leadsPerMonth, conversionRate, arpuPerCustomer, monthlyChurn, horizonMonths (default 12). Outputs returned: trajectory, endingMrr, cumulativeRevenue.
What formula does the Sales Forecast Calculator use?: The exact computation is: for m = 1..horizon:; new_customers = leads_per_month * conversion_rate; new_mrr = new_customers * arpu; churned_mrr = mrr_{m-1} * monthly_churn; mrr_m = mrr_{m-1} + new_mrr - churned_mrr
Can I verify the Sales Forecast Calculator with a worked example?: Yes. With startingMrr = $500, leads = 200, conv = 2%, arpu = $25, churn = 4%, horizon = 12. the tool returns endingMrr ≈ $1,700, cumulative ≈ $13,500 over 12 months at baseline inputs.
Does the Sales Forecast Calculator bundle any external benchmark data?: No. It runs standard arithmetic on the values you enter; no external benchmark dataset is bundled. Industry references, where relevant, are cited on the adjacent tools' methodology pages.
What can the Sales Forecast Calculator not tell me?: Known limitations: Deterministic, not probabilistic. A ±20% range on any input cascades across the horizon; the tool does not surface that uncertainty. No seasonality layer. E-commerce and consumer products with Q4 spikes will be off by a meaningful margin.