8 User Onboarding Metrics You Have to Track for Better Results

Most lists of user onboarding metrics name completion rate, activation and retention. What the lists rarely say is which event to log, what to divide by and over what window, and the denominator changes the answer. If 1,000 people sign up, 400 open your onboarding checklist and 200 finish it, your checklist completion rate is 50% when you divide by the people who opened it and 20% when you divide by everyone who signed up. Both numbers are true; only one belongs in a report about the checklist.

This post covers the in-product side: eight metrics about individual users in their first days and weeks, each as a formula with the events behind it. Two companion posts cover the rest. Onboarding KPIs sets business targets such as trial-to-paid conversion and churn benchmarks, and customer onboarding metrics measures onboarding at the level of a paying account.

How do you measure user onboarding effectiveness?

You measure user onboarding effectiveness by tracking what new users do, not what they say, at each step between sign-up and regular use. Onboarding is hard to put into numbers because no single figure defines it, but a small set of metrics, read together, shows where users get stuck.

Along the onboarding funnel there are moments of friction that shape the whole experience, from engagement to satisfaction. Small points of confusion can move acquisition and retention numbers on their own. The manager responsible for onboarding needs metrics that catch those moments, and depending on the product and the business goals, they range from churn and adoption to usage and trial conversion.

The reason for tracking a given metric differs from company to company, but the goal is always the same: a more effective onboarding experience, and in extreme cases, evidence that the current onboarding strategy should be replaced.

Which user onboarding metrics should you track?

The eight metrics below cover a new user's first weeks. Log the events once, with a user ID and a timestamp, and every formula can be computed from them.

1- Churn rate

Churn rate is the share of users who stop using your product in a period. It matters for onboarding because poor onboarding is one of the main causes of early churn, and churn is the clearest sign of an unstable business.

Pick a period, divide the number of users who churned during it by the number you had at the start, and multiply by 100. For paying customers, "churned" means they cancelled. For free users, set an inactivity threshold, such as no activity for 30 days, and keep it fixed.

For onboarding, calculate churn separately for users in their first 90 days. A rising churn rate among new users while older users stay stable points at onboarding, not at the product. For what counts as a good churn rate, the KPI post cites a sourced benchmark.

2- Average DAU, WAU and MAU, and stickiness

Daily, weekly and monthly active users (DAU, WAU, MAU) count unique users who did something meaningful in a day, week or month. Their averages show how many people use the product in a typical period:

• Average DAU for a month = the sum of each day's DAU ÷ the number of days in that month.
• Average WAU for a quarter = the sum of each week's WAU ÷ the number of weeks.
• Average MAU for a year = the sum of each month's MAU ÷ 12.

The ratio that matters for onboarding is stickiness: average DAU ÷ MAU × 100, the share of a month's users who come back on a typical day. (It's often written "DAU/MAU", and it's DAU divided by MAU, not the other way round.) Compare stickiness for users who finished onboarding with those who didn't, before and after you launch a new onboarding flow or feature onboarding. Define "active" as a meaningful action, not a page load, or every visit to the login screen counts.

3- Average session duration

Average session duration is the total time of all sessions in a period ÷ the number of sessions in that period. It's mostly a customer success metric, but it shows how onboarding changes behaviour: compare first sessions under the old and new onboarding flows.

Read it alongside stickiness and churn. In onboarding, a longer first session isn't automatically better: a user who spends 25 minutes on a five-minute setup is probably lost. Pair it with completion: long sessions with high completion are good, long sessions with low completion mean confusion.

4- Onboarding completion time

Onboarding completion time is how long users take to get from sign-up to the last step of your onboarding flow. Use the median, not the average: a handful of users who finish three weeks later will drag an average far from what most users experience.

Log each step, and you can see which step takes longest and which material is hard to learn, which can point at the product itself, not just the guidance. The target is short but not too short: a completion time well under what it takes to read the steps usually means users skipped through without reading.

5- Checklist completion rate

An onboarding checklist keeps first steps organised and gives users a sense of progress. Its completion rate is the number of users who completed every item ÷ the number who saw the checklist × 100.

Track each item as well: the share of users who completed item n out of those who completed item n − 1. The item with the steepest drop is the one to rewrite, shorten or remove. If you built the checklist in-house, ask your developers to log "shown" and "item completed" events; most no-code onboarding tools report them out of the box. Asana's onboarding checklist is a good model: five items, one button each.

6- Feature adoption rate and product adoption rate

Feature adoption rate is the share of active users who use a given feature regularly:

Feature adoption rate = users who performed the feature's key action at least N times in the period ÷ active users who had access to the feature in the period × 100

Divide by active users with access, not by every customer: users on a plan without the feature, or who haven't logged in this month, can't adopt it. Setting N above 1 separates users who adopted the feature from users who clicked it once out of curiosity.

Product adoption rate works the same way for the product's core action. It's the reason onboarding exists: if adoption is high, you're getting users familiar with the product, and retention follows. Our product adoption guide gives a rule for when a user counts as adopted and the formula per sign-up cohort.

7- Customer feedback

Feedback isn't a behaviour metric, but it explains the behaviour metrics: users can tell you why a step is confusing in a way that drop-off data can't.

Ask at the moment onboarding ends, with one question about the experience, for example "How easy was it to set up your first project?" on a 1 to 7 scale, plus an optional comment. Most onboarding tools in our reviews include in-app surveys; without one, a feedback button in the interface or a short email works. For NPS and CSAT formulas, see customer onboarding metrics.

8- User retention rate (day N)

Day-N retention is the share of a sign-up cohort that is still active on a given day after signing up:

Day-N retention = users from the cohort who were active on day N ÷ users in the cohort × 100

Measure it on day 1, day 7 and day 30 (or week 1, 4 and 12 for a weekly product) and plot the curve. A curve that drops and then flattens means a group of users found value and stayed; a curve that never flattens means onboarding isn't getting anyone to the core value. Compare cohorts that went through different onboarding flows to see which one flattens higher.

Which mistakes make onboarding metrics look better than they are?

Four mistakes inflate onboarding numbers more than any other:

1. The wrong denominator. Dividing by users who started a flow instead of all who were eligible hides everyone who never saw it.
2. Averages for time. A few very slow users distort average completion time; use the median.
3. All-time numbers. All-time retention mixes old and new users; compare sign-up cohorts.
4. A loose definition of "active". If a login counts as activity, stickiness and retention include people who did nothing.

Fix those, and the eight metrics above will show where onboarding loses people. For the events and tools that feed them, see customer onboarding analytics.

User onboarding can matter as much as the product itself, because a value proposition is hard to see without it. Track it with the same care you'd give any other part of the product.