--- title: "How to Define an Activation Metric in SaaS" description: "Setup is not activation. Define your activation metric in three rows (ideal user, setup event, aha event), test it on retention, and measure it per account." canonical_url: "https://builderscamp.com/guides/other/how-to-define-an-activation-metric" date_published: "2026-09-27" date_modified: "2026-09-27" author: "Andre Albuquerque, Mário Araújo" publisher: "Builders Camp" guide_class: "other" --- # How to Define an Activation Metric **TL;DR:** An activation metric names the first event where a new user or account gets real value, plus a threshold and a time window, and it must sit after setup, not at the end of it. Define it in three rows (ideal user, setup event, aha event), each with a metric, then check that activated accounts retain far better than the rest: Lenny Rachitsky and Yuriy Timen suggest at least 2x. ## What is an activation metric, and how is it different from setup? An activation metric is a named event, with a threshold and a time window, that marks the first moment a new user or account gets the value your product exists to deliver. Lenny Rachitsky and Yuriy Timen, in their [activation benchmark for Lenny's Newsletter](https://www.lennysnewsletter.com/p/what-is-a-good-activation-rate), describe the activation milestone as "the earliest point in your onboarding flow that, by showing your product's value, is predictive of long-term retention." Their survey of over 500 products found an average activation rate of 34 percent and a median of 25 percent, which means most products lose the majority of new signups before value ever happens. Setup is the step before that. Setup is everything an account has to do to become able to get value: create a workspace, connect a data source, import contacts, invite a colleague. Activation is the first time the product actually pays off. A team that reports setup completion as activation gets a flattering number that predicts nothing, because a configured account that never gets value churns just as fast as one that never finished configuring. A quick test separates the two. Ask: if the account stopped right here, would the person tell a colleague the product worked? Imported a customer list: no. Sent an invoice through the product and saw it get paid: yes. The first is setup, the second is activation. ## How do you define an activation metric in three rows? Write three rows, and give each one a plain-language definition and a metric you can compute from your event data. The example below is fictional: a shift-scheduling app for restaurants, used by managers who plan the week and hourly staff who confirm their shifts. | Row | Definition (fictional scheduling app) | Metric | |---|---|---| | Ideal user | A restaurant manager at a location with 10 to 40 hourly staff, who builds the schedule every week | Share of new signups that match this profile | | Setup event | Staff roster imported and a first weekly template saved | Setup rate, and median time from signup to setup | | Aha event | First schedule published where at least 5 staff confirm their shifts in the app, within 14 days of signup | Activation rate, and median time from signup to activation | The ideal-user row matters more than it looks. If a third of signups are single-location cafés with three staff, their activation rate drags the average down and hides whether the product works for the customers it was built for. Report activation for the ideal-user segment first, then for everyone. The aha row has to name a specific event, a threshold and a window. [Amplitude's activation guide](https://amplitude.com/explore/digital-analytics/what-is-activation-rate) puts the reason plainly: the aha moment "is not a universal fixed point; rather, it's a carefully defined event unique to each product and its value proposition." "Uses scheduling" is not a definition. "Publishes a schedule that 5 or more staff confirm within 14 days" is: an engineer can instrument it and an analyst can compute it without asking you what you meant. ## How do you know you picked the right aha event? Test the candidate event against retention before you adopt it. Split the last few months of new accounts into those that hit the event inside the window and those that did not, then compare how many are still active 8 weeks later. Rachitsky and Timen give a working threshold: users who hit the activation milestone "should retain at a rate at least 2x better than those who do not complete the activation step." If your gap is smaller, the event is either too easy (almost everyone who signs up does it) or it measures something adjacent to value rather than value itself. The same article names the two errors most teams make. Too early: "Often companies define activation as simply completing the sign-up flow, which alone is unlikely to show a user the value of your product." Too late: marketplaces and e-commerce companies that wait for repeat purchases, which is closer to retention than activation. The right event sits between those: after setup, before habit. Check the time window with the same data. Plot time from signup to aha event for accounts that eventually activated, and set the window where the curve levels off. Rachitsky and Timen found that about 6 percent of respondents used a time-bound definition, with a median window of 10 days. That is a self-reported survey figure, so treat it as a description of common practice rather than a target; the point is that a window turns a vague milestone into a number you can move. ## What if different users activate by different paths? Many products deliver value by more than one route. Take a fictional design-feedback tool: a designer activates by uploading a file and receiving a first comment from someone else, while a reviewer invited by that designer activates by leaving three comments on a shared file. Forcing both into one event undercounts one group. Define each path as its own named event, count an account as activated when any path completes inside the window, and report the share of activations each path contributes. The split tells you where onboarding should point new signups. If 80 percent of activated accounts came through the designer path, a reviewer-first onboarding flow is working against the grain of how the product gets adopted. ## How do you measure activation at the account level in B2B? Measure it per account whenever value depends on more than one person. Rachitsky and Timen make the same point for multi-user SaaS, giving the example of a workspace-level milestone such as "workspace with 10 items created and 2+ active editors by W4" instead of a single user's action. A fictional invoicing tool for agencies shows why: one account manager creating a draft invoice proves nothing, but a draft created by one user, approved by a finance lead and sent to a client is the moment the agency's billing process runs through the product. Account-level activation needs account-level instrumentation. Each event has to carry the account or workspace it belongs to, and each user record has to say which account and role the person has. Without that, you can count active people but you cannot say which companies got value, and in B2B the company is who renews. User-level events still matter: when an account fails to activate, they show whether the blocker was the admin never inviting anyone or the invited people never showing up. ## Is one activation metric too simple for a real product? The honest objection is that a single event compresses a messy reality, and some users get value in ways no event captures. That is true, and it is why the metric should come with its diagnostics: setup rate, time to activation, and activation by segment and by path. The single metric is for the weekly conversation and the onboarding roadmap; the diagnostics are for working out why it moved. The opposite risk is worse. A team with no agreed activation metric argues about whether onboarding works using whichever chart supports each person's view. Andrew Chen, writing about retention data from Quettra covering over 125 million Android devices, argued that "the best way to bend the retention curve is to target the first few days of usage, and in particular the first visit." A named activation event is how you know whether those first days are working. For how the resulting rate is calculated and benchmarked, see [activation rate](https://builderscamp.com/guides/glossary/activation-rate); for where it sits among the other steps, see [product funnel](https://builderscamp.com/guides/glossary/product-funnel). ## How do you check activation later with cohorts? Once the metric exists, track it by signup cohort. A weekly [cohort analysis](https://builderscamp.com/guides/glossary/cohort-analysis) of activation rate and time to activation shows whether an onboarding change helped the accounts that signed up after it, instead of mixing old and new signups into one number. Then compare retention curves for activated and non-activated accounts in each cohort; if the gap between them narrows over time, the definition is drifting away from value and it is time to revisit it. The [retention analysis guide](https://builderscamp.com/guides/other/retention-analysis-for-product-managers) covers that second step in more depth. ## Where does activation fit in Builders Camp's Product Analytics bootcamp? The [Product Analytics bootcamp](https://builderscamp.com/bootcamps/product-analytics) is a one-week program with 2 live sessions and 9 microlessons, directed by Mário Araújo and part of the Growth Specialist Track and the Data & Analytics Specialist Track. Its published topics include activation metrics and aha moments, account-level analysis, B2B analytics instrumentation and cohort retention analysis. Its practical challenge asks members to distinguish setup from activation and define a meaningful first-time-to-value event for a B2B field service tool; the [write-up of that challenge](https://builderscamp.com/guides/challenges/product-analytics-activation-cohort-rebuild) shows the brief. For activation inside a wider growth model, [Growth for Product Managers](https://builderscamp.com/bootcamps/growth-product-manager) is a two-week bootcamp whose published topics include retention and engagement loops and growth measurement with cohort analysis. Builders Camp runs live and self-paced bootcamps in product management and AI product building. See the Product Analytics bootcamp for dates and the full syllabus. ## Frequently asked questions ### What is the difference between an activation metric and an activation rate? The activation metric is the definition: the named event, threshold and time window that count as a new user or account getting value. The activation rate is the share of new signups that meet that definition. Change the definition and the rate moves, which is why the definition has to be agreed first. ### Is completing onboarding a good activation metric? Usually not. Finishing an onboarding checklist is a setup event: the account is ready to get value, not proof that it got any. Lenny Rachitsky and Yuriy Timen list defining activation as completing the sign-up flow as the most common too-early mistake. ### Should an activation metric have a time window? Yes. Without one, an account that reaches the aha event on day 90 counts the same as one that got there on day 2, and the metric stops telling you whether onboarding works. Pick a window from your own time-to-activation data, for example the point by which most eventually-activated accounts have activated. ### How do I know my activation metric predicts retention? Split new users or accounts into activated and not activated, then compare their retention a few weeks later. Rachitsky and Timen suggest activated users should retain at least 2x better than those who do not activate. If the gap is small, the event is too easy or measures the wrong thing. ### Can a product have more than one activation event? Yes, when different kinds of users reach value by different routes. Define each path as its own event, count an account as activated when it completes any of them, and report the share of activations each path contributes so you can see which one onboarding should push. ### Should B2B activation be measured per user or per account? Per account whenever value depends on more than one person, such as an approval, a handoff or a shared workspace. Track user-level events too, because they explain why an account did or did not activate, but report the activation rate at the account level. ### How often should I revisit the activation definition? Whenever the product's core value changes (a new primary use case, a pricing model change, a new ideal customer) and at least once a year otherwise. Rerun the retention comparison each time; an event that predicted retention two years ago may not predict it now. ## Sources - [Lenny Rachitsky and Yuriy Timen, Lenny's Newsletter: What is a good activation rate (2022)](https://www.lennysnewsletter.com/p/what-is-a-good-activation-rate) - [Amplitude: What Is Activation Rate for SaaS Companies?](https://amplitude.com/explore/digital-analytics/what-is-activation-rate) - [Andrew Chen: New data shows losing 80% of mobile users is normal, and why the best apps do better](https://andrewchen.com/new-data-shows-why-losing-80-of-your-mobile-users-is-normal-and-that-the-best-apps-do-much-better/) ## How this guide was made Researched from Builders Camp's bootcamp, track and masterclass material and the sources listed on this page, drafted with AI, and fact-checked against every source cited.