Drop-off Rate
Drop-off rate is the percentage of users who reach a step in a journey, workflow or funnel but do not continue to the next step within a defined time window. It is the inverse of the step conversion rate, and it pinpoints where people abandon a process such as onboarding, checkout, setup or a multi-page form.
How Drop-off Rate Works
The basic calculation compares two consecutive steps:
Drop-off rate = (users who reached step A minus users who reached step B) ÷ users who reached step A × 100
If 500 users open a setup wizard and 350 finish its first screen, the drop-off rate at that screen is 30 percent. Drop-off can also be measured across a whole flow, from the first step to the last, which gives the inverse of overall conversion.
Three choices affect the number, so they should be fixed and stated:
- The time window. A user who finishes the next step three days later counts as dropped off in a one-day window but converted in a seven-day window.
- The counting unit. Counting unique users, sessions or individual attempts gives different results. A user who fails twice and succeeds once looks very different under each.
- The step definitions. Steps must map to reliable events. If the "completed" event fires inconsistently, drop-off looks worse than it is.
Drop-off rate is usually read inside a funnel analysis, where it appears for every step alongside the conversion rate.
Why Drop-off Rate Matters
A single overall number hides where the problem sits. Step-level drop-off shows which screen, field or decision loses people, which turns a vague goal like "improve onboarding" into a specific target. It also helps teams size the opportunity: a step with high drop-off and high traffic is usually worth fixing before a step with high drop-off and little traffic.
Not all drop-off is bad. Some users leave because the product is not for them, and a qualifying step that filters them out early can be healthy. The number says where users leave, and research or segmentation explains why. For a walkthrough of finding friction by segment and turning it into requirements, see automating user journey maps from behavioral data.
Drop-off Rate Example
An expense app's receipt upload flow has four steps. Of 2,000 users who open it in a week, 1,800 take a photo, 1,080 confirm the extracted amount and 1,026 submit. Drop-off is 10 percent at the photo step, 40 percent at confirmation and 5 percent at submission. Session recordings at the confirmation step show users struggling to edit an amount the scanner read wrongly. The team makes the amount field editable inline and tracks whether drop-off at that step falls in the following weeks.
Drop-off Rate vs. Churn
Both describe users leaving, but at different scales. Drop-off rate measures abandonment inside a specific flow, often within minutes or days. Churn measures customers who stop using or paying for the product altogether over a period such as a month. High drop-off in onboarding can lead to churn later, but the two are tracked and fixed differently.