Funnel Chart Generator

Build conversion funnel visualizations with stage labels, counts, and drop-off percentages.

Tested tool guide Tested browser tools Checked August 16, 2026

What Funnel Chart Generator does, with a checked example

Funnel Chart Generator converts ordered stage labels and counts into a funnel showing how much of an initial group remains as it progresses. Each transition includes a drop-off percentage, making large losses easier to spot than they are in a count table. Enter the stages in actual process order and use counts from the same cohort and time window. The common surprise is that drop-off is measured against the immediately preceding stage, not necessarily against the first stage.

Worked example

A concrete input and expected output from the current implementation.

Input

Visitors: 100
Purchases: 25

Expected output

A two-stage funnel labeled Visitors (100) and Purchases (25), with a 75% drop-off from Visitors to Purchases.

The count decreases by 75, and 75 divided by the preceding count of 100 is 75%. The remaining 25 represents a 25% conversion between the two stages.

How the result is produced

Ordered funnel stages

Provide one label and count for every stage in the order that people, applications, orders, or other units pass through the process. The generator places that sequence into the funnel and associates each loss with a transition between neighboring stages. Reordering stages changes the meaning of the resulting drop-off percentages even when the counts themselves stay unchanged.

Drop-off calculation

For a transition from count A to count B, the drop-off percentage is (A - B) divided by A, multiplied by 100. A move from 80 to 20 therefore has 75% drop-off and 25% retention. The first stage has no preceding stage, while a transition beginning with zero does not have a defined percentage.

Good uses

Chart product views, cart additions, checkout starts, and completed orders from one reporting period to identify the ecommerce handoff with the largest proportional loss.
Summarize a software signup cohort across account creation, email verification, first-use activation, trial completion, and paid conversion.
Present applicants, screened candidates, interviews, offers, and acceptances as a recruiting funnel instead of making stakeholders compare a raw count table.

Limits and checks

Use the same cohort and observation window throughout. Combining this month's visitors with all-time purchases can produce valid arithmetic but a meaningless conversion funnel.
Make stage definitions mutually consistent. Counts based on unique people cannot be compared cleanly with counts of repeat events, sessions, or transactions.
Do not infer causes from segment size alone. A large drop-off identifies where the recorded count changed, but it does not explain whether the cause was abandonment, eligibility rules, tracking gaps, or delayed completion.

Common questions

Is each drop-off measured from the first stage?

No. A stage-to-stage drop-off compares a later count with the immediately preceding count. With counts of 100, 50, and 20, the adjacent losses are 50% and 60%. The cumulative loss from the first stage to the last is 80%. These percentages answer different questions and should not be substituted for one another.

Can a later funnel stage have a larger count?

It should not in a conventional conversion funnel unless the increase has a clear explanation. A larger later count can indicate repeat events, re-entry, mixed cohorts, or inconsistent stage definitions. The ordinary drop-off formula then produces a negative value. Review the underlying counts before presenting that result as conversion attrition.

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.