What is a good activation rate? Benchmarks by product type and how to define yours

A good activation rate for B2B software is roughly a third of new users. Lenny Rachitsky and Yuriy Timen's survey of over 500 responses found a 34% average and 25% median, and SaaS alone averaged 36%. Compare your number only against a benchmark with the same milestone, time window and unit. Then check that activated users retain at least 2x better than users who skip the step.

What is a good activation rate?

The short answer: across the B2B software samples, roughly a third of new users activate. That figure only means something next to its sample size and its definition of activation, because each benchmark counts a different thing.

Benchmark	Sample	How activation is counted	Headline figure
Lenny Rachitsky and Yuriy Timen	Over 500 survey responses	Users who hit the activation milestone divided by users who completed signup	34% average, 25% median; SaaS only: 36% average, 30% median
Amplitude	Data from over 2,600 companies	Percentiles reported at Day 1, Day 7 and Day 14	Median (50th), good (75th) and great (90th) percentile bands

What "good" means in each

In our reading, the spread between types came mostly from the milestone chosen. Marketplaces and e-commerce had the lowest rates because they define activation as the first transaction, while B2C freemium products had the highest because their milestone is low-friction, such as starting a meditation session.

Amplitude takes a time-boxed view instead. Its percentile bands at Day 1, Day 7 and Day 14 let you compare a cohort at the same age, which matters if your time to value runs longer than a day.

Check your number before you compare it

Take an example, not a benchmark: 1,000 users complete signup this month and 370 reach your milestone, so 370 divided by 1,000 gives a 37% rate.

Run one test before you trust the comparison. Rachitsky's rule is that activated users should retain at a rate at least 2x better than users who skip the step. Pull 30-day retention for both groups this week. If the gap is under 2x, redefine the milestone in your activation spec first, then benchmark.

Activation rate benchmarks by industry and growth motion

Industry and growth motion change the number you should compare against. A human resources (HR) tool and an artificial intelligence (AI) assistant can both be healthy and still sit far apart.

Activation rates ranged from 54.8% in AI and machine learning to 5% in fintech and insurance.

Activation rate by industryPer cent

1. AI and machine learning54.8%
2. CRM and sales42.6%
3. Martech24%
4. Healthcare23.8%
5. HR8.3%
6. Fintech and insurance5%

Activation rate by industry

Item	Value, %
AI and machine learning	54.8%
CRM and sales	42.6%
Martech	24%
Healthcare	23.8%
HR	8.3%
Fintech and insurance	5%

Segment	Activation rate	Userpilot's label
AI and machine learning	54.8%	High
Customer relationship management (CRM) and sales	42.6%	High
Martech	24%	Average
Healthcare	23.8%	Average
HR	8.3%	Low
Fintech and insurance	5%	Low

In our reading, the low verticals ask more of a new user before value shows up: payroll setup, compliance steps, account verification. A fintech team at 7% may be doing well for its category even though the overall average looks far higher.

Sales-led versus product-led

Userpilot's broader report analyzed 547 SaaS companies and found that sales-led growth (SLG) companies had higher user activation (41.6% vs. 34.6%) than product-led growth (PLG) companies. That is a gap of 7 percentage points between 41.6% and 34.6%. Userpilot's explanation is commitment: in SLG, monetization happens earlier, so paying customers push to get value.

PLG is the more common motion. In the same Userpilot study, 79% of SaaS companies adopt product-led growth and only 21% are sales-led. Mixpanel puts the share at 58% of companies using a PLG model. We think the two figures differ because the samples differ: one counts SaaS companies only. Quote the source when you cite either one.

If you run product-led growth, compare yourself to the PLG figure, not the blended average.

Worked example

Take 1000 new signups as an example input, not a benchmark. If your rate matches the CRM and sales figure of 42.6%, 426 of 1000 users activate. A self-serve CRM tool at that rate is above the PLG figure for its motion. A sales-led CRM tool at the same rate is close to its peers.

One pattern to test this week: Userpilot found that 80% of companies achieving activation rates above 50% used videos, GIFs or animations in onboarding. That shows correlation, not cause. Add a short video to one onboarding step, then size the test with the A/B test sample size calculator before you read results.

Why the published benchmarks disagree

In our reading, the figures in the earlier sections do not disagree because some companies are better at onboarding. They disagree because each source builds the number differently. Before you set a target, check three things about any benchmark: who chose the milestone, how the data was collected, and over what time window.

Three sources, three constructions

Amplitude's figures come from product event data. Its behavioral database "tracks billions of users across thousands of digital products," and the team excluded customers who opted out and applied z-score-based local baseline methods to remove outliers. Those figures are reported at fixed day-based windows, as covered above.

The Lenny Rachitsky and Yuriy Timen survey worked the other way. It was a global benchmarking survey asking readers to contribute their activation rate, so each number is self-reported against that company's own milestone. The survey defines the milestone as the earliest point in onboarding that, by showing the product's value, is predictive of long-term retention. Even the unit can change. For multi-user SaaS, teams often set their activation metrics on workspace- or account-level actions rather than on individual users.

Geckoboard names the problem directly. The milestone will differ from company to company, depending on your product and your business goals: a social platform might track users who add their first friend. Its advice is to use caution and judgment with any industry benchmark.

Source	How the data was gathered	Who sets the milestone	What to match before comparing (our reading)
Amplitude	Anonymized product event data	Measured inside the analytics product	Same time window, same industry cut
Lenny survey	Self-reported by respondents	Each company, its own milestone	A milestone of similar effort and the same unit (user or account)
Geckoboard	Guidance, no dataset	You	Treat any external figure as context only

How to compare like with like

1. Match the window. A day-based figure from event data is not the same measure as a survey figure with no fixed window.
2. Match the effort. The survey warns that too early would be simply finishing the sign-up process for a SaaS product, and too late would be three purchases in a marketplace or e-commerce site. If your milestone sits near either end, an external figure will mislead you.
3. Match the unit. An account-level rate and a user-level rate on the same product will differ, so compare one with the other only after converting.
4. Track your own trend by signup cohort. In our reading, your own week-over-week movement on a fixed definition is the comparison that will hold up, because it is the only one where every input matches.

If your activation milestone changes, restart the trend line. A new definition makes the old numbers a different metric.

How is activation rate calculated?

The arithmetic is one division. Geckoboard writes the formula as [No. users who complete the set milestone / Total no. users who signed up] × 100 = Activation rate (%), in its activation rate guide. The top line is people who reached your activation event. The bottom line is everyone who signed up.

That formula alone leaves out the time limit. Amplitude lists "Not setting a timeframe for activation" among the common mistakes when defining or tracking activation.

Without a window, the number drifts. Your rate then rises or falls with the age of your signups instead of the quality of your onboarding. A fixed window makes every cohort comparable.

The calculation, step by step

1. Pick one signup cohort, such as everyone who created an account in a calendar month.
2. Set the window, such as 7 days from each user's own signup date. Do not use 7 days from the start of the month.
3. Count the users in that cohort who completed the milestone inside their window.
4. Divide by the cohort size and multiply by 100.

A worked example

These are example inputs, not a benchmark.

Input	Example value
Signups in the month	1,200
Window	7 days from each signup

Geckoboard gives a similar reading of a low rate: "Say your activation rate is 25%. That means 3/4 of your people signing up for your product are quitting somewhere in the onboarding process," according to its guide.

Why the number earns its place on the dashboard

Geckoboard notes that "Activation rate can also act as a proxy metric for sign up to subscriber conversion rate," per its guide. That makes it useful when payment comes weeks after signup. You can read a 7-day number long before the free-to-paid conversion data arrives.

This week, write your milestone and window into one sentence, run it on last month's cohort, and pair it with your time to value. In our reading, a short time to value usually means a 7-day window will capture most of the users who activate.

How do I choose my activation event?

A follow-up post on Lenny's Newsletter lays out a three-step process. Each step narrows the list.

1. Brainstorm candidates. Explore your product usage data for potential aha moments, the points where a user first gets value.
2. Check for a retention inflection. Run a regression analysis to see whether retention bends upward when someone hits each candidate. This only shows correlation.
3. Test for cause. Run experiments that push more users to the moment, then watch retention. The post's bar: "A good activation metric is causal for your retention, not just correlative."

The reader stories in that post show the range. One team building an e-commerce self-checkout product found that customers who placed at least two orders retained at a 2x higher rate than with one order, so two orders became the milestone.

If your data is thin, Retention.Blog suggests it is likely okay to stop at trying to understand correlation in Mixpanel or Amplitude and move to experiments. Use the A/B test sample size calculator before you commit to one.

A worked template

As an example, not a benchmark, take 1,000 signups from one monthly cohort. List four or five candidate events. Tag each user with the candidates they completed inside your activation window. Plot retention for completers against non-completers, keep the two candidates with the widest gap, and log one experiment per candidate in the growth experiment backlog.

The aha moment is not the metric

Intercom's research adds a caution. "As the phrase implies, “aha” moments are an emotional reaction to discovery of a feature." They also vary by intent: on Dropbox, one person may need to access documents from multiple devices, while another may want to share folders with others. Intercom's conclusion is that activation is a business metric that suggests retention, but is not a reflection of the user’s experience.

Retention.Blog lists simple first-value moments that make good starting candidates:

Product	Candidate aha moment
Canva	Exporting their first design
Duolingo	Completing their first lesson
MyFitnessPal	Tracking their first meal

The metric can sit further along than the moment. For a workout app, the aha could be a first guided workout, while the activation metric may be completing two workouts in the first 7 days. In our reading, that gap is the reason to run all three steps: the moment gives you candidates, and the data tells you which one to measure.

Worked example: defining activation from retention data

Here is the method applied to a hypothetical B2B team tool. Take an example cohort of 1000 signups (an example, not a benchmark) and three candidate events from your brainstorm: invited a teammate, created a first project, connected an integration.

For each event, split the cohort into users who hit it within 7 days of signup and users who did not. Then compare day-30 retention for the two groups.

The 30-day check has precedent. One team described in Lenny's Newsletter ran regressions to find which action "correlated with long-term retention (30 days in our case)". Another team, with a complex enterprise product, measured events against "trial retention (two weeks)" in the same post. Match the window to your trial or billing cycle.

The cohort table

Fill this in from your product analytics. Each row uses the same 1000 signups.

Candidate event (first 7 days)	Users who hit it (of 1000)	Day-30 retention, hit	Day-30 retention, did not hit	Gap (our method)
Invited a teammate	your count	your %	your %	hit minus did not hit
Created a first project	your count	your %	your %	hit minus did not hit
Connected an integration	your count	your %	your %	hit minus did not hit

How to read it

Choose the event with the largest retention gap that a meaningful share of users reach. Write down your minimum share before you run the query, so the result cannot move the bar.

The two columns pull against each other. An event that almost every signup completes barely separates retained users from churned ones, so its gap stays small. An event with a large gap that only a handful of users reach will not shift your overall numbers when you move it. In our reading, the useful candidate sits between those two cases.

When two events land close together, do not decide on the table alone. Take both into the experiment step covered earlier and use the sample size calculator before you launch the test.

If you have no usage data yet

New products can start from interviews. One team asked its target segment, "What are the signals that this product is solving your problem?" It then surveyed a list of matching actions. In the end, it "picked the milestone that had been selected by the majority." Rerun the cohort table once you have a few months of signups.

Definition template

Activation event: [specific action, e.g. invited a teammate]
Threshold: [how many times, e.g. at least 1]
Time window: [days from signup, e.g. within 7 days]
Segment: [who this applies to, e.g. self-serve workspaces]
Retention check: [metric and window, e.g. day-30 retention]
Gap observed: [hit % minus did-not-hit %]
Share reaching it: [users who hit it / total signups]
Next review date: [date]

Log the event in your glossary entry for activation so every team counts it the same way.

How long should time to value be?

Time to value (TTV) is the gap between signup and the moment a new user first gets what they came for. Userpilot defines it as "the time it takes the user to get value out of the product" (Userpilot). Its dashboard measures that span from sign-up to the Activation Point, which makes TTV the clock on the activation event you chose earlier.

Amplitude's benchmark found that high-retention products stand out at showing new users the value of their product within the first week. It also found that 69% of top performers in seven-day activation were also top performers in three-month retention. The first week is the window to win.

Sales-led products reached value sooner

Userpilot ran its TTV analysis on the same 62 companies as in our activation rate study. Sales-led growth (SLG) companies had a shorter time to value (1 day 11 hours vs 1 day 12 hours) than product-led growth (PLG) companies, so 1 day 12 hours minus 1 day 11 hours leaves a gap of one hour.

Userpilot's explanation is that SLG teams involve sales engineers and customer success managers in onboarding, which makes it more guided. It also notes that in SLG, monetization happens earlier, so a paying customer wants value sooner. In our reading, a one-hour gap is small, and the bigger lesson is that guidance shortens the path for either motion.

How to set your activation window

An activation rate with no time limit keeps rising forever. Fix the window with these steps:

1. Pull every user who hit your activation event in the last full quarter.
2. For each, compute hours from signup to that event.
3. Plot the cumulative share activated by day since signup.
4. Find the day where the curve flattens. Round up to a whole day.
5. If that day falls past day seven, treat the length as an onboarding problem to fix, not a window to accept.

Then check the result against retention. Amplitude ties having at least 7% of your original cohort of users return on day seven to stronger three-month retention.

Your motion	What the sources found	Window starting point (our reading)
Sales-led, guided onboarding	Shorter time to value	Start short and tighten
Product-led, self-serve	Slightly longer time to value	Start at seven days, then shorten with guided steps

How to improve your activation rate this quarter

Pick one change per week and measure it against the activation event you defined earlier. Four moves cover most of the work, and each one feeds the next.

1. Map the drop-off from signup to activation

Build a funnel from signup to your activation event, with every required step in between. Amplitude's list of ways to improve activation includes "Optimize the funnel continuously" and "Collect and act on user feedback", so pair the funnel with a one-question survey at the step where people stall.

As an example, not a benchmark: take a cohort of 1000 signups and count how many of the 1000 reach each step. Start with the step that loses the most people in absolute terms. A step with an ugly percentage but few users behind it can wait.

2. Cut friction and show value before the paywall

OpenView's Blake Bartlett and Kyle Poyar set out five product-led growth (PLG) pillars. Two of them apply directly here: "Create a frictionless onboarding experience" and "Show value before asking users for money". End users, they argue, expect self-service, such as automated onboarding with social login.

This week, remove one field from signup, move the card request after the activation event, or test a reverse trial. If you use an onboarding checklist, Userpilot advises keeping it to up to 7 steps, with progress bars and tasks the user has already completed.

3. Test video or animation in the product

In Userpilot's benchmark report, 80% of companies achieving activation rates above 50% use multimedia elements such as videos, GIFs, or animations in onboarding. We read this as a correlation, not proof. Teams that produce onboarding video may also be better at onboarding in general. Test one short clip or animation on your worst step and let the result decide.

4. Run each change as a sized experiment

Score ideas with an ICE score, log them in the growth experiment backlog template, and work out the sample you need with the A/B test sample size calculator before you launch. If your weekly signups cannot reach that sample, test bigger changes rather than smaller ones.

Use this row format for each test:

Field	What to write
Step targeted	The funnel step with the largest absolute loss
Change	One change only, such as moving the paywall after activation
Primary metric	Activation rate within your activation window
Guardrail metric	Later retention for the same cohort
Sample and duration	From the calculator, fixed before launch
Decision rule	Ship, revert or retest, written before results arrive

Check the guardrail before you ship. A change that lifts activation but not retention has moved the metric, not the habit.

What this page does not cover

This page covers activation rate benchmarks, how to define and calculate activation, and how to set a time window. It does not cover retention or free-to-paid conversion benchmarks in depth, and it does not compare or recommend analytics or onboarding tools. Nothing here is financial or legal advice.

Related on Race to Scale: Free-to-paid conversion rate benchmarks for freemium, opt-in trials and card-required trials; SaaS churn rate benchmarks by price point and segment; A/B test sample size and duration calculator, with the formula.