--- title: "Aha moment and time to value: finding your activation event | Skene" description: "The aha moment is the action after which users become far more likely to stay. A borrowed number misleads; your own needs a validated event and a fixed time window." url: https://www.skene.ai/resources/frameworks/aha-moment doc_version: "51e269c7d116" last_updated: 2026-10-30 --- Resources / Frameworks # The aha moment: finding your own number instead of borrowing Slack's Aha moment and time to value. Credit: No single originator; Dave Boyce's _Freemium_ calls it First Impact. By [Teemu Kinos](https://www.skene.ai/about), co-founder of Skene. The aha moment is the action or milestone after which a user becomes far more likely to stay, and time to value is how long it takes to get there. Growth writing repeats two examples, Slack's 2,000 messages and Facebook's seven friends in ten days. The useful lesson in both is that each company found its own number in its own data. A borrowed threshold measures someone else's product. ## How it works Finding an aha moment means comparing users who stayed with users who left, and looking for an early action that separates them sharply. The candidate then has to pass two more tests: it delivers real value to the user, and it is reachable without much friction. Time to value is the median time from signup to that action, and shortening it is one of the most dependable ways to lift retention. Dave Boyce's _Freemium_ calls this event First Impact and makes it the gate for product-market fit: at least 70% of new users reaching First Impact within their attention span (Ch 4 and Ch 8). He reports Snyk's activated teams still active at week 15 at 48%, against 3% for teams that never activated (Ch 11). A gap that wide is exactly why the event under it has to be right. ## What it assumes your events already do | Event | What the framework uses it for | What it reports when the event breaks | | --- | --- | --- | | Signup `account_created` | Starts the time-to-value clock. | Reset signup timestamps after a migration make time to value look instant for everyone moved. | | Activation or First Impact event `first_report_shared` | The aha event itself. | If a release changes where it fires, time to value jumps or collapses overnight and stops comparing with last month. | | Return visit `session_started` | Proves the event predicts retention. | A broken return event weakens the correlation, and the team abandons an aha moment that was right. | Event names are illustrative; yours will differ. The [dependency map](https://www.skene.ai/resources/frameworks#dependency-map) shows which other frameworks share each event. ## The aha moment that was a side effect _Illustrative example. The company and the figures are invented to show the mechanism._ A team finds that users who connect a data source on their first day retain at three times the rate of those who do not, so they make the step mandatory. Retention does not move. Months later they find that `source_connected` also fired whenever the product imported a sample dataset in the background, which only happened for users who stayed long enough for it to finish. The correlation was real. The event did not mean what the chart said it meant. ## Checks before you trust it - Confirm the candidate event fires once per real occurrence, from every path, including imports and automated setup. - Validate the threshold against retention for several signup cohorts, not one. - Re-check the definition after every onboarding change, because onboarding is where the event moves. - Report time to value as a median alongside the share who never get there. ## Heard in The Question Interviews in [The Question](https://www.skene.ai/resources/the-question) that bear on this framework: - [Nima Aksoy: demand is the first dashboard](https://www.skene.ai/resources/the-question/nima-aksoy). Which data deserves trust depends on the product's stage: market demand first, the activation drop-off next, financial reporting at maturity. - [Maria Petrova: data tells you what happened, never why](https://www.skene.ai/resources/the-question/maria-petrova). Data establishes what happened and never why, and tracking is infrastructure to build before the first real user, not something to add later. - [Shahin Katebi: data finds the stuck user, a person asks why](https://www.skene.ai/resources/the-question/shahin-katebi). When users are not from the tech world, domain expertise decides what to build, and product data has one narrow job: locating where a user got stuck. ## Check the events under it Before Aha moment and time to value goes into a plan, find out whether the events it needs exist and still fire the way it assumes. The [free audit](https://www.skene.ai/signup) maps what your product collects today: sign in, connect a GitHub repository or Supabase, and Skene returns one audit, with no card. On Pro, Skene Cloud reviews every pull request on a linked GitHub repository for removed or broken tracking, so a pull request that removes or renames one of these events is flagged before it merges. ## Related - [AARRR pirate metrics: five stages, each one event deep](https://www.skene.ai/resources/frameworks/aarrr-pirate-metrics) - [Cohort retention analysis: why the blended number hides the trend](https://www.skene.ai/resources/frameworks/cohort-retention-analysis) - [Freemium vs free trial: the metrics that settle it, and how they break](https://www.skene.ai/resources/frameworks/freemium-vs-free-trial) - On the blog: [What is activation rate and how to improve it (with a scoped benchmark)](https://www.skene.ai/resources/blog/activation-rate-benchmarks-improvement) - Glossary: [Activation event](https://www.skene.ai/resources/glossary/activation-event) - Glossary: [Side effect](https://www.skene.ai/resources/glossary/side-effect) - Glossary: [Conditional firing change](https://www.skene.ai/resources/glossary/conditional-firing-change) - Glossary: [Event name](https://www.skene.ai/resources/glossary/event-name) - Glossary: [Cohort](https://www.skene.ai/resources/glossary/cohort) ## Sitemap - [Markdown sitemap](https://www.skene.ai/sitemap.md): every page on this site, with its markdown counterpart. - [Index for language models](https://www.skene.ai/llms.txt): the same set as a link-dense index. - [Full corpus](https://www.skene.ai/llms-full.txt): reference content concatenated as plain text.