A practical guide to funnel analysis: define the funnel, read step conversions, split the weakest step and size the fix, with a worked example.
By Sergey BruhPublished 8 min read
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Start the free courseFunnel analysis measures how many users get through each step of a path, such as sign-up → first project → first purchase, and shows where the biggest share of them stops. You define who enters, the steps, their order and a time window; count the users who reach each step; divide each step by the one before; then split the weakest step to find whose drop it is. The arithmetic takes minutes. The definition is where most funnel reports go wrong, so this guide starts there and then walks through a full example on an onboarding funnel.
A funnel is a sequence of steps and the share of users who reach each one. It is a metric like any other, which means it only means something with its definition next to it. Four choices change the result:
Write these four choices under every funnel chart. Otherwise two people will build "the onboarding funnel" and get 37% and 62% from the same data.
Halves is a made-up app for splitting shared bills between flatmates, couples and friends on trips. The product lead wants to know where new users get stuck before they split their first bill. The funnel:
The overall rate is . It tells you the funnel leaks but not where. Step conversion divides each step by the previous one: , , and .
The weakest step is "invite accepted": only 55.9% of users who sent an invitation had it accepted within the window. That is where to look first. Note that the column "from sign-up" can't show this: it always falls, so the biggest absolute drop in it is not necessarily the weakest step.
A drop is always somebody's drop. Split the funnel by a dimension that could explain it: platform, acquisition channel, country, app version. Here, by channel:
Step conversions:
Two different findings come out of one table:
With a few hundred users per segment, check before you conclude. The 95% Wilson intervals:
Why Wilson and how to compare two rates properly is covered in confidence intervals explained.
The team can fix one thing this sprint. Instead of fixing the biggest drop by reflex, estimate what each fix would add at the bottom of the funnel, holding later steps at their current rates:
The targets are assumptions, so treat the result as a ranking, not a forecast. It still settles the argument: a better group-creation screen, often the first thing teams want to redesign, is worth less than half of a better invitation.
A funnel chart by week or month has two traps of its own.
Right censoring. Users who signed up yesterday have had one day, not seven. Their conversion looks low now and will rise. In one course example, the latest week showed 29.2% "so far" against 49.4% for complete cohorts, and the team nearly rolled back a release because of it. Show only cohorts whose window has closed, or grey out the incomplete one.
Date ranges that mix people. "First expenses in August ÷ sign-ups in August" puts late-July users in the numerator and not the denominator. The date range should pick who enters; then follow those same people through their window.
A funnel shows where people stop. It doesn't say which step matters for whether they stay. For that, compare later retention of users who reached a step with those who didn't: the step that separates them best is a candidate for your activation criterion. That is the subject of activation rate, and the retention side is covered in how to calculate retention rate.
It is measuring the share of users who complete each step of a defined path, such as onboarding or checkout, to find where they drop off and which fix would help most.
There is no universal benchmark: it depends on the steps, the window and who enters. Compare your funnel with itself over time and across segments, using the same definition.
Use strict order when the sequence is what you want to test, for example "invited before adding an expense". If many users complete the steps in a different order, look at their path before calling them failures.
Long enough for a normal user to finish at the product's pace, short enough to get an answer in time. For a first-week onboarding funnel, 7 days is a common choice. Whatever you pick, write it on the chart.
The free lesson Funnels: order, window and who counts lets you build a funnel on twelve Halves users and watch the result change with each choice. Then Reading a funnel without fooling yourself covers censoring and date ranges, and the onboarding funnel case asks you to write the recommendation. All three are part of the free course Product Analytics: From Data to Decisions.
