How fast do users get to first value?
№ 192 · Time-to-value distribution
Definition
MetricElapsed time from signup to the first value moment, shown as a distribution
Unithours or days from signup to activation milestone
Histogram of days from signup to first-value-event. Long tail means poor onboarding. Build from event data joining signup to aha-moment event. Mode tells you the natural activation window. Example: 38% same day, 22% within week, 16% within month, 24% never (the never bucket is the loss).
Benchmarks
| Bottom 30% | Median | Top 30% |
|---|---|---|
| Over 3 days. HR software sits there as an entire category at close to 3 days 19 hours, so a slow number in a slow category is a positioning question rather than an onboarding failure. | About 1 day 2 hours on the median and 1 day 12 hours on the mean for B2B SaaS, per Userpilot's panel. The gap between the two is itself the diagnostic. | Under 12 hours from signup to first value for self-serve B2B SaaS, and same-session for the fastest products. CRM and sales tools sit closest to this at a category average of 1 day 4 hours. |
B2B SaaS products instrumented in Userpilot's activation dashboard, n=62 companies drawn from a 547-company panel with average revenue of $63.9M and average head count of 327, so mid-market vendors rather than early stage. 2024 to 2025 data. Time-to-value is measured from signup to the product's own defined activation point. · One publisher carries the distribution, on 62 companies, and reports segment averages rather than a percentile curve, so the top30 and bottom30 above are inferred from the industry spread and should be treated as indicative. The metric is also fully dependent on where the activation point is set: a product that calls account creation its value moment will report hours where a product that calls a completed workflow its value moment reports days, on identical user behaviour. Consumer subscription apps are not comparable at all, since RevenueCat measures time to a revenue milestone rather than time to a value moment, and reports a 3x spread between categories on that basis.
| Category | Bottom 30% | Median | Top 30% |
|---|---|---|---|
| CRM and sales software | Not separately published | 1 day 4 hours 43 minutes, fastest vertical in the panel | Not separately published |
| Healthcare software | Not separately published | 1 day 7 hours 11 minutes | Not separately published |
| FinTech, insurance and AI or ML software | Not separately published | 1 day 17 hours 11 minutes FinTech and Insurance, 1 day 17 hours 19 minutes AI and ML | Not separately published |
| MarTech software | Not separately published | 1 day 20 hours 47 minutes | Not separately published |
| HR software | Beyond 4 days | 3 days 18 hours 59 minutes, roughly 3x the panel median | Not separately published |
When it looks bad
The distribution is bimodal, with a spike inside the first hour and a second hump past day seven and almost nothing between them, which means two different user types are being pushed down one onboarding path.
Median time to value 1 day 4 hours against a mean of 6 days 2 hours. 34% of signups reach value inside two hours, 41% never reach it at all, and the mean is carried entirely by a tail of accounts that needed a human to finish setup.
What to do about it
- Report the median and the 90th percentile and stop reporting the mean. Userpilot's own panel shows the mean running roughly 10 hours above the median, and on a bimodal distribution the mean describes no real user.
- Split the onboarding path on intent captured at signup rather than serving one path to everyone. The bimodal shape is almost always two populations, and the second hump usually needs a different sequence rather than a faster version of the first one.
- Ship a read-only or preview mode that removes the blocking dependency, typically data import, integration credentials or an admin approval. Amplitude's finding that the day 14 activation rate at the 90th percentile is only about 9% means anything that pushes first value past week one is competing against a very short attention window.
- Set the activation milestone at an outcome and hold it fixed across releases. Moving the milestone is the easiest way to improve this chart and the fastest way to make it meaningless, and outcome-based milestones are the ones that predict retention.
Sources
- Userpilot Average time to value of 1 day 12 hours 23 minutes and median of 1 day 1 hour 54 minutes. By industry: CRM and Sales 1 day 4 hours 43 minutes, Healthcare 1 day 7 hours 11 minutes, FinTech and Insurance 1 day 17 hours 11 minutes, AI and ML 1 day 17 hours 19 minutes, MarTech 1 day 20 hours 47 minutes, HR 3 days 18 hours 59 minutes. userpilot.com ↗
- Amplitude Day 1 activation rate for technology B2B SaaS runs 35% below the all-industry average, the behavioural counterpart to a slower time to first value in this segment. amplitude.com ↗
- Amplitude Users decide whether a product is worth keeping within the first seven days, and the day 14 activation rate at the 90th percentile is about 9%, so even top performers lose most users inside two weeks. amplitude.com ↗
- Amplitude Recommends delivering value inside the first week, and reports that 69% of products with strong early activation were also strong three-month retention performers. amplitude.com ↗
- RevenueCat Time-to-revenue varies by more than 3x between categories, with Gaming reaching $1,000 MRR in a median of 32 days and Business taking 113 days. A revenue milestone, not a value milestone, and included to mark the boundary. revenuecat.com ↗
- Mixpanel Names feature adoption and time-to-value as the paired engagement metrics that convert new users into retained customers. mixpanel.com ↗
- Mixpanel Average week one retention across industries fell from 50% to 28% in 2023, compressing the window in which a product has to deliver first value. mixpanel.com ↗
- Appcues Treats time-to-adopt as its own metric and argues that a long gap points to a release and discovery problem rather than a value problem, which applies equally to first-value timing. appcues.com ↗
- feeqd Defines median time from release to first use per user, with under a week indicating strong discoverability and over a month indicating passive encounter rather than active seeking. feeqd.com ↗
- ProductQuant Notes that outcome-based activation milestones produce lower raw rates and longer measured times than activity-based ones, while predicting retention more reliably. The definitional caveat that governs this whole card. productquant.dev ↗