How this calculation works
Each percentage applies only to the people who reached its preceding stage. The end-to-end probability is the product of all three. The breakdown partitions the original cohort into departures at each stage and final conversions, so the categories add back to the starting cohort.
Final conversions = starting cohort × sign-up rate × qualification rate × close rateA worked example
10,000 × 20% × 50% × 10% gives 100 expected final conversions, an end-to-end probability of 1%.
Good to know
A transparent arithmetic model using your inputs. Example prices, rates and multiples are demonstrations, not benchmarks or offers. No tax, exchange-rate conversion, live account access or automatic attribution is supplied. Monetary display is rounded; calculations retain additional precision.
Conditional percentages use different bases
A 20% sign-up rate applies to the starting cohort. A 50% qualification rate then applies to sign-ups, not again to all visitors. A 10% closing rate applies to qualified leads. Multiplying those rates gives a 1% end-to-end probability.
Adding the three percentages would not describe the funnel. Each stage has already selected a smaller group. The output keeps those expected counts visible so the bases remain understandable.
Customer acquisition cost uses new customers as a denominator. A funnel can help organize a customer-count scenario, but its assumed conversion rates are not measured acquisitions.
Use the breakdown to ask a focused next question
Hold two stages fixed and vary the third to see how it changes expected final conversions. This is a sensitivity exercise; it does not prove that a particular intervention will cause the assumed rate improvement.
The breakdown is a partition of the original cohort into departures at each stage and final conversions. It assumes one pass through the funnel without retries, branching or delayed returns. Actual event analytics may use a different counting convention.
A few questions, answered
Why are some expected counts fractional?
Multiplying a cohort by probabilities gives expectations. A realized sample consists of whole people or events and can differ from the expectation.
What if a stage has zero conversion?
No modeled participants reach later stages, so final expected conversions are zero.