# SaaS activation rate benchmarks, with the definitions

SaaS activation rate benchmarks from Lenny’s Newsletter and Userpilot, side by side with each one’s definition and sample, and why yours may not compare.

Canonical: https://ignura.com/blog/saas-activation-rate-benchmark
Last updated: 2026-10-25
Author: Jawad Jalal

The published SaaS activation rate benchmarks sit between 30% and 37.5%. [Lenny's Newsletter](https://www.lennysnewsletter.com/p/what-is-a-good-activation-rate) puts the SaaS average at 36% and the median at 30%, and [Userpilot's 2025 benchmark report](https://userpilot.com/saas-product-metrics/) puts the average at 37.5% and the median at 37% across 62 B2B SaaS companies.

Those numbers only mean something next to their definitions, so this post puts each one beside its source, sample and definition. To get your own number first, put two counts into our free [activation rate calculator](https://ignura.com/tools/activation-rate-calculator). It runs in your browser and shows your rate against the same benchmarks.

## The short version

- Activation rate is the share of new sign-ups who reach one milestone that shows they got value.
- Lenny's survey of more than 500 products found a SaaS average of 36% and a median of 30%. Across all products in the survey, the average was 34% and the median 25%.
- Userpilot's data from 62 B2B SaaS companies found an average of 37.5% and a median of 37%.
- Both sources say the same thing about variance: the milestone you pick changes the number more than anything else.
- Compare yourself with your own past cohorts first. Use the benchmarks as a rough check, not a target.

## What activation rate means

Lenny's Newsletter gives the formula in one line: users who hit your activation milestone, divided by users who completed your sign-up flow. The milestone is "the earliest point in your onboarding flow that, by showing your product's value, is predictive of long-term retention."

Userpilot's report describes it as the percentage of new users who complete a specific action that demonstrates the initial value of the product.

The two definitions agree on the idea. They differ on who picks the milestone and how it gets counted. In Lenny's survey, each team reported its own rate using its own milestone. In Userpilot's report, the figures come from Userpilot's own product data, collected through its activation dashboard. Neither source fixes one milestone for everyone, which is why the rest of this post matters.

## Published benchmarks

Last checked on 25 October 2026. Each figure links to the page it comes from.

| Source | Date | Sample | Definition | Average | Median |
|---|---|---|---|---|---|
| [Lenny's Newsletter, "What is a good activation rate"](https://www.lennysnewsletter.com/p/what-is-a-good-activation-rate) | 25 Oct 2022 | SaaS products only, from a survey of 500+ products (marketplaces, e-commerce and DTC removed) | Users who hit the team's own activation milestone ÷ users who completed sign-up, self-reported | 36% | 30% |
| [Lenny's Newsletter, same survey](https://www.lennysnewsletter.com/p/what-is-a-good-activation-rate) | 25 Oct 2022 | All 500+ products surveyed | Same as above | 34% | 25% |
| [Userpilot, SaaS Product Metrics Benchmark Report 2025](https://userpilot.com/saas-product-metrics/) | Page updated Sept 2025 | 62 B2B SaaS companies | New users who complete an action that shows initial value, from Userpilot's activation dashboard | 37.5% | 37% |

Userpilot also splits its activation data by industry, and the spread is wide. On the same page, AI and ML products show 54.8%, CRM and sales 42.6%, MarTech 24%, healthcare 23.8%, HR 8.3% and fintech and insurance 5%. Those are slices of a 62-company sample, so each industry rests on a handful of companies. Read them as a sign of how much the number moves, not as a target for your industry.

Lenny's post adds one more layer. It labels the 60th percentile for each product type as GOOD and the 80th percentile as GREAT. The per-type figures are in a chart in the post, so open it if you want the line for your product type.

## Why your number differs

You can have a healthy product and a rate far below 30%, or a weak one well above 37%. Four things cause most of the gap.

**The milestone.** Lenny's post says the variance is "mostly due to the milestone definition, and how much effort it takes to get there." Marketplaces and e-commerce products count a first purchase, so their rates are lowest. B2C freemium products count something light, like starting one session, so theirs are highest. A SaaS product that counts "connected a data source and invited a teammate" will sit lower than one that counts "opened a template."

**The denominator.** Lenny's formula divides by users who completed sign-up. If you divide by everyone who started sign-up, or by every account including invited teammates, you get a different number.

**The time window.** Lenny's survey found about 6% of respondents used a time-bound milestone, with a median of 10 days and a mode of 7 days. A rate measured within 7 days of sign-up will read lower than one with no deadline.

**The cohort.** Count the users who signed up in a period, then count how many of those same users activated. If you count everyone who activated this month, including last month's sign-ups, the rate looks better than it is. Small cohorts also swing a lot. Under about 30 sign-ups, a few users either way move the rate by many points.

So before you compare with the table above, write down your milestone, denominator, window and cohort. If any of them differ from the source's, the comparison is loose at best.

## How to pick your activation event

Lenny's post gives two tests for a good milestone. It should be highly predictive, meaning users who reach it retain at least 2x better than users who don't. And it should be actionable, meaning your team can change what happens before it.

It also names the mistakes it saw most:

- **Too early.** Finishing sign-up is not activation. It shows nothing about value.
- **Too late.** Several purchases or a month of use is proof of a habit, not the start of one.
- **Not predictive.** The users who reach it don't retain much better than those who don't.
- **Not actionable.** It depends on something offline or takes months to measure.
- **Too complicated.** A multi-step, time-bound rule where a simple one predicts nearly as well.

A practical way to start:

1. List three or four early actions a new user can take, such as creating a first project, importing data or inviting a teammate.
2. For each, compare 30-day retention between users who did it in their first week and users who didn't.
3. Pick the earliest action with a clear gap. Lenny's post suggests at least 2x.
4. Write the definition down, add the window, and keep it fixed so your rate means the same thing each week.

Lenny's post also says not to overthink it: pick a reasonable milestone and start testing against it. For the step-by-step version, our guide on [how to improve activation rate](https://ignura.com/blog/how-to-improve-activation-rate) walks through defining the event and tracking the path to it.

## What to fix first

Once the rate is measured the same way each week, find the step between sign-up and the milestone that loses the most people. Fix that one step before anything else.

Lenny's survey asked teams which change had the biggest effect on their activation rate. The eight most common answers were simpler onboarding, less friction in onboarding, emails and follow-ups, better copy, smarter targeting at the top of the funnel, sales outreach, incentives and showing value earlier.

In practice that usually means one of these, depending on where your drop is:

- **A long path to value.** Count the screens and fields between sign-up and the milestone, then cut the ones you don't need yet.
- **An empty first screen.** Give new users a template, sample data or one clear first task.
- **No follow-up.** People who leave in the first session hear nothing. One email or message with the next step is often missing.
- **The wrong people signing up.** If the ads or the landing page promise something the product doesn't do first, activation falls no matter how good onboarding is.

If most people leave before they even sign up, the problem is earlier in the funnel. Our guide to [a landing page hero that says what the app does in one line](https://ignura.com/blog/landing-page-one-line) is the place to start.

Test each change against what you have now, and keep the winner. If you can't tell which step loses people, that's a tracking problem to fix first. Our guide to [improving activation rate](https://ignura.com/blog/how-to-improve-activation-rate) covers how to track each step from sign-up to first use and test fixes side by side.

Run your numbers through the [activation rate calculator](https://ignura.com/tools/activation-rate-calculator) again after each change, using the same milestone and window, so the before and after compare.

## FAQ

### What is a good activation rate for SaaS?

Lenny's Newsletter found a SaaS average of 36% and a median of 30%. Userpilot found an average of 37.5% and a median of 37% across 62 B2B SaaS companies. If about a third of your new sign-ups reach your milestone, you're near the middle of both, as long as your milestone is similar.

### How do you calculate activation rate?

Divide the users who reached your activation milestone by the users who signed up in the same period, then multiply by 100. So 30 activated users out of 100 sign-ups is 30%. Use the same cohort for both numbers.

### Why is my activation rate lower than the benchmark?

Usually because your milestone asks more of the user than the ones in the benchmark, or because you count over a shorter window. Check your definition first. If it matches and the rate is still low, look for the single step that loses the most new users.

### Are these benchmarks current?

Lenny's survey was published in October 2022. Userpilot's page was last updated in September 2025. Both state their sample and definition. We checked both pages on 25 October 2026.

### Should I fix activation before buying more traffic?

Usually, yes. If most sign-ups never reach value, more traffic means paying for more people who leave. Fix the biggest drop first, then add traffic.

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