--- title: "North Star Metric: choosing one number and keeping it true | Skene" description: "A North Star Metric is the one number that captures the value customers get. It is usually built from several events, so a break in any one of them moves every team." url: https://www.skene.ai/resources/frameworks/north-star-metric doc_version: "51e269c7d116" last_updated: 2026-10-30 --- Resources / Frameworks # North Star Metric: one number for the whole team, built from several events North Star Metric. Credit: Popularised by Amplitude and Sean Ellis. By [Teemu Kinos](https://www.skene.ai/about), co-founder of Skene. A North Star Metric is the one number that best captures the value a product delivers to its customers, chosen so every team can see how its work moves it. Amplitude and Sean Ellis popularised the idea, and it helps most at a company with dozens of dashboards and no number everyone agrees on. Its strength and its risk are the same thing: when the whole company steers by one number, an error in that number steers the whole company. ## How it works A good North Star counts customers getting value, not the company getting paid: teams active on at least three days in the week that also shared a report, say, rather than revenue. It sits above a few input metrics that teams own directly, such as activation rate or reports created per team, and the inputs are where day-to-day work happens. Almost no North Star is a single event. It is a filter for who counts, a qualifying action for what counts as value, and a time window, stitched together in an analytics tool. Each part is a place where a renamed event, a dropped property or a changed definition can move the number while every customer behaves exactly as before. ## What it assumes your events already do | Event | What the framework uses it for | What it reports when the event breaks | | --- | --- | --- | | Core value action `report_viewed` | The qualifying value action. | If it is renamed, the North Star drops on release day and teams start explaining a decline that never happened. | | User-to-account join `account_id on every event` | Counts teams rather than individual users. | Events missing the account property drop users out of their team, so the newest setups are undercounted. | | Return visit `session_started` | Decides which teams meet the active-days filter. | If the return event changes, teams fall in or out of the active-days filter, and the North Star moves a few percent with no change in sharing. | 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 North Star that moved with the mobile release _Illustrative example. The company and the figures are invented to show the mechanism._ A company's North Star is teams active on at least three days in the week that shared a report. The mobile app ships a new share sheet that fires `report_exported` rather than `report_shared`. Mobile is a fifth of usage, so the North Star falls 9% in a week. A product squad spends a sprint on an experiment to win the users back. The users never left. One event changed its name on one platform, and the company's single most watched number followed it. ## Checks before you trust it - Write the North Star as a definition with named events, properties and a window, not as a sentence. - Name one owner who signs off any change to an event the definition uses. - Break a move down by platform and app version before reacting; a drop confined to one release is usually tracking. - Keep the input metrics next to the North Star, so a move in the total traces to the input behind it. ## Heard in The Question Interviews in [The Question](https://www.skene.ai/resources/the-question) that bear on this framework: - [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. - [Dino Repo: one metric, five numbers, and a ten-minute fix](https://www.skene.ai/resources/the-question/dino-repo). Untrustworthy data is an ownership problem before it is a tooling problem, and the cheapest fix is a product-data check that runs every time the build loop turns. - [Niko van Eeghen: one metric, three meanings at Evondos](https://www.skene.ai/resources/the-question/niko-van-eeghen). The number is rarely the hard part: one metric means different things to different people, and in parts of the economy you cannot or may not collect it. - [John Adib: when agents read your analytics, event names break first](https://www.skene.ai/resources/the-question/john-adib). Once an agent rather than an analyst queries the analytics, ambiguous event names stop being hygiene and start producing wrong numbers in the business review. ## Check the events under it Before North Star Metric 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) - [Customer health score: a renewal forecast resting on usage events](https://www.skene.ai/resources/frameworks/customer-health-score) - [Net revenue retention: the investor number, and the product events that explain it](https://www.skene.ai/resources/frameworks/net-revenue-retention) - [Jobs to be done: the progress a customer hires your product to make](https://www.skene.ai/resources/frameworks/jobs-to-be-done) - Glossary: [Conversion event](https://www.skene.ai/resources/glossary/conversion-event) - Glossary: [Event taxonomy](https://www.skene.ai/resources/glossary/event-taxonomy) - Glossary: [Renamed event](https://www.skene.ai/resources/glossary/renamed-event) - Glossary: [Event name](https://www.skene.ai/resources/glossary/event-name) ## 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.