What it is
A funnel calculator for onboarding and activation. Paste counts per step, or an event log with user, event and time, and it builds a strict ordered funnel.
Onboarding funnel analysis
Find the step where new users quietly leave.
Onboarding Drop turns step counts or a raw event log into a clear funnel. It shows where most people are lost, which step converts worst, how long each step takes, whether a new version really did better, and how many more activated users a fix would bring.
Who it is for
Product managers, growth and lifecycle teams, founders and designers who need to decide which onboarding step to fix next and want numbers they can defend.
What it solves
Dashboards show totals, not where the funnel breaks or whether a redesign helped. Here you see the leak, the weak step, the slow step and the real difference between two versions.
Map your onboarding funnel
Pick the kind of data you have, paste it and press Find the drop. Everything is calculated in this page.
No funnel yet
Paste step counts or an event log, or load the sample to see the full analysis.
How to read the numbers
Plain arithmetic and one standard statistical test. Nothing is guessed.
Step conversion
Users at a step divided by users at the step before. This is the number a fix to that screen can move.
From start
Users at a step divided by users at the first step. The last one is your activation rate.
Biggest leak and weakest step
The leak loses the most people. The weakest step has the lowest rate. They are often different steps, and both matter.
Real difference
When you compare two versions, a two proportion z test checks each step. Yes means the gap is unlikely to be chance at the 95 percent level.
Median time
From an event log, the typical wait between finishing one step and the next. Long waits often mean people left to fetch something.
What if
Raises one step by the points you choose and keeps every later rate as it is. A simple, honest estimate of what a fix is worth.
How to use it
Choose your data
Step counts from any dashboard, or a raw export of events with user ids.
Paste or open the file
For counts, add a second column to compare an old and a new flow. For events, a variant column does the same.
Press Find the drop
Read the four headline numbers first, then the funnel and the table.
Try a fix
Use What if to see how many more users a better step would bring before you build it.
Share the summary
Copy the Markdown summary into your product doc or ticket. The hash shows it was not edited.
What it does not do
- It does not connect to your analytics. You paste or open exported data.
- Event logs are processed in your browser. Very large exports above about 200,000 lines may be slow.
- The significance test assumes users are independent and were split fairly between versions.
- It shows where people leave, not why. Pair it with interviews or session notes.
Questions
Is my data uploaded?
No. Parsing and math run in this page and nothing is stored.
Why does a user disappear from later steps in the event log?
Steps are counted in strict order. A user who skipped a step, or did it before the step before it, stops at the last step done in order.
What if my counts go up between steps?
That usually means the steps are out of order or counted differently. You will get a note so you can check.
Which step should I fix first?
Usually the biggest leak. If the weakest step comes late, after people invested effort, it can be worth more. Use What if to compare.