--- title: "GTM frameworks and the product events each one depends on | Skene" description: "Fourteen growth, retention and revenue frameworks, from AARRR to the SaaS bowtie, mapped to the events each one assumes are correct and what it reports when they break." url: https://www.skene.ai/resources/frameworks doc_version: "0b979c27954f" last_updated: 2026-10-30 --- Resources / Frameworks # 14 GTM frameworks and the product events each one assumes By [Teemu Kinos](https://www.skene.ai/about), co-founder of Skene. This page maps fourteen GTM frameworks to the product events each one assumes. Every GTM framework here is a calculation over product events. AARRR divides one event by another, and a churn model learns from their history. None of them checks whether the events are right. When an engineering release renames, moves or drops an event, the framework the growth team reads keeps producing a number that looks as confident as it did the day before. Each page below sets out a framework as it is usually defined, credits its originator where there is one, then lists what it assumes your events already do and what it reports when one of them breaks. The map further down runs the other way: start from an event and see which frameworks go wrong with it. ## The fourteen frameworks - [AARRR (pirate metrics)](https://www.skene.ai/resources/frameworks/aarrr-pirate-metrics). Credit: Dave McClure. Dave McClure's AARRR pirate metrics count acquisition, activation, retention, referral and revenue. Each stage rests on one or two events; activation breaks most often. - [North Star Metric](https://www.skene.ai/resources/frameworks/north-star-metric). Credit: Popularised by Amplitude and Sean Ellis. 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. - [Product-qualified lead (PQL)](https://www.skene.ai/resources/frameworks/product-qualified-lead). Credit: A product-led growth term with no single originator. A product-qualified lead is scored on what someone did in the product, not on a form they filled in. That makes the score exactly as accurate as the events behind it. - [Product-qualified account (PQA)](https://www.skene.ai/resources/frameworks/product-qualified-account). Credit: A product-led growth term with no single originator; Dave Boyce's _Freemium_, Chapter 14. In Dave Boyce's Freemium, a product-qualified account is scored on fit and product usage, then routed to self-serve, assistance or sales. Both scores need clean events. - [SaaS bowtie](https://www.skene.ai/resources/frameworks/saas-bowtie). Credit: Winning by Design. Winning by Design's SaaS bowtie extends the funnel past the close into onboarding, adoption and expansion, which is where most teams' tracking is at its thinnest. - [Aha moment and time to value](https://www.skene.ai/resources/frameworks/aha-moment). Credit: No single originator; Dave Boyce's _Freemium_ calls it First Impact. 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. - [Cohort retention analysis](https://www.skene.ai/resources/frameworks/cohort-retention-analysis). Credit: A standard analytics method with no single originator. Cohort retention analysis tracks each signup group's curve, so you can see whether retention is improving. It assumes 'active' means the same thing every month. - [Net revenue retention (NRR)](https://www.skene.ai/resources/frameworks/net-revenue-retention). Credit: A standard SaaS finance metric with no single originator. Net revenue retention is the revenue kept from existing customers, expansion included, net of churn. Finance owns the number; product events explain why it moved. - [Customer health score](https://www.skene.ai/resources/frameworks/customer-health-score). Credit: A customer success practice with no single originator. A customer health score blends usage, support and sentiment to predict renewal risk. Usage carries the most weight, and nothing in the score checks the usage events. - [Churn prediction scorecard](https://www.skene.ai/resources/frameworks/churn-prediction). Credit: A customer success practice with no single originator. A churn prediction model flags at-risk accounts from leading indicators like falling usage. A broken event looks just like falling usage, and it trains the model wrong. - [Viral loop and K-factor](https://www.skene.ai/resources/frameworks/viral-loop-k-factor). Credit: A growth metric with no single originator; Dave Boyce's _Freemium_ calls it R0. The K-factor is invites sent per user times the rate at which invites convert. Above 1.0 growth compounds. Both halves are events, and invite paths multiply quickly. - [Hook Model](https://www.skene.ai/resources/frameworks/hook-model). Credit: Nir Eyal, _Hooked_. Nir Eyal's Hook Model builds habits through trigger, action, variable reward and investment. Most steps leave events; the reward step barely does, so measure with care. - [Freemium vs free trial](https://www.skene.ai/resources/frameworks/freemium-vs-free-trial). Credit: A pricing choice with no single originator; Dave Boyce's _Freemium_, Chapters 9 and 10. Freemium gives a permanent free tier for volume; a free trial gives full access for a limited time. Dave Boyce adds the reverse trial and an experiment to decide. - [Jobs to be done (JTBD)](https://www.skene.ai/resources/frameworks/jobs-to-be-done). Credit: Clayton Christensen; interviewing method by Bob Moesta. Clayton Christensen's jobs to be done says customers hire a product to make progress. The job decides which events matter, so interviews come before the tracking plan. ## If this event breaks, these frameworks go wrong The same handful of events sits under most of the list. Ordered by how many frameworks depend on each one, this is where a single renamed or dropped event does the most damage. Event names are illustrative. ### Core value action `report_viewed` - [North Star Metric](https://www.skene.ai/resources/frameworks/north-star-metric): The qualifying value action. - [Product-qualified lead (PQL)](https://www.skene.ai/resources/frameworks/product-qualified-lead): Depth of use. - [Product-qualified account (PQA)](https://www.skene.ai/resources/frameworks/product-qualified-account): Usage depth inside the account. - [SaaS bowtie](https://www.skene.ai/resources/frameworks/saas-bowtie): Adoption. - [Net revenue retention (NRR)](https://www.skene.ai/resources/frameworks/net-revenue-retention): Adoption that predicts expansion. - [Customer health score](https://www.skene.ai/resources/frameworks/customer-health-score): Adoption of the features that predict renewal. - [Churn prediction scorecard](https://www.skene.ai/resources/frameworks/churn-prediction): Usage decline, the strongest leading indicator. - [Hook Model](https://www.skene.ai/resources/frameworks/hook-model): The action step. - [Jobs to be done (JTBD)](https://www.skene.ai/resources/frameworks/jobs-to-be-done): The job done again: the reason customers stay. ### Activation or First Impact event `first_report_shared` - [AARRR (pirate metrics)](https://www.skene.ai/resources/frameworks/aarrr-pirate-metrics): The numerator of the activation rate. - [Product-qualified account (PQA)](https://www.skene.ai/resources/frameworks/product-qualified-account): The First Impact guard before any sales contact. - [SaaS bowtie](https://www.skene.ai/resources/frameworks/saas-bowtie): Onboarding: the first stage after the knot. - [Aha moment and time to value](https://www.skene.ai/resources/frameworks/aha-moment): The aha event itself. - [Churn prediction scorecard](https://www.skene.ai/resources/frameworks/churn-prediction): Early-life features, such as whether the account finished setup. - [Viral loop and K-factor](https://www.skene.ai/resources/frameworks/viral-loop-k-factor): Whether invited users reach value. - [Freemium vs free trial](https://www.skene.ai/resources/frameworks/freemium-vs-free-trial): The guardrail: charging earlier must not cut the share who reach value. - [Jobs to be done (JTBD)](https://www.skene.ai/resources/frameworks/jobs-to-be-done): The first moment the job gets done. ### Return visit `session_started` - [AARRR (pirate metrics)](https://www.skene.ai/resources/frameworks/aarrr-pirate-metrics): Defines who counts as retained. - [North Star Metric](https://www.skene.ai/resources/frameworks/north-star-metric): Decides which teams meet the active-days filter. - [Aha moment and time to value](https://www.skene.ai/resources/frameworks/aha-moment): Proves the event predicts retention. - [Cohort retention analysis](https://www.skene.ai/resources/frameworks/cohort-retention-analysis): Defines active in each period. - [Customer health score](https://www.skene.ai/resources/frameworks/customer-health-score): Active users against seats bought. - [Churn prediction scorecard](https://www.skene.ai/resources/frameworks/churn-prediction): Login recency. - [Hook Model](https://www.skene.ai/resources/frameworks/hook-model): The proxy for the reward. - [Jobs to be done (JTBD)](https://www.skene.ai/resources/frameworks/jobs-to-be-done): Whether the customer comes back when the situation recurs. ### User-to-account join `account_id on every event` - [North Star Metric](https://www.skene.ai/resources/frameworks/north-star-metric): Counts teams rather than individual users. - [Product-qualified lead (PQL)](https://www.skene.ai/resources/frameworks/product-qualified-lead): Rolls users up to the company sales will call. - [Product-qualified account (PQA)](https://www.skene.ai/resources/frameworks/product-qualified-account): Every readiness signal rolls up to an account. - [SaaS bowtie](https://www.skene.ai/resources/frameworks/saas-bowtie): Ties adoption to the account the CRM and billing know. - [Cohort retention analysis](https://www.skene.ai/resources/frameworks/cohort-retention-analysis): Builds account cohorts for B2B products. - [Net revenue retention (NRR)](https://www.skene.ai/resources/frameworks/net-revenue-retention): Matches product usage to the billing account. - [Customer health score](https://www.skene.ai/resources/frameworks/customer-health-score): Rolls user activity up to the account being scored. ### Invite sent and accepted `invite_sent, invite_accepted` - [AARRR (pirate metrics)](https://www.skene.ai/resources/frameworks/aarrr-pirate-metrics): The referral stage. - [Product-qualified lead (PQL)](https://www.skene.ai/resources/frameworks/product-qualified-lead): A common PQL signal. - [Product-qualified account (PQA)](https://www.skene.ai/resources/frameworks/product-qualified-account): Seat growth as a readiness signal. - [SaaS bowtie](https://www.skene.ai/resources/frameworks/saas-bowtie): Spread inside the account. - [Net revenue retention (NRR)](https://www.skene.ai/resources/frameworks/net-revenue-retention): Seat growth inside the account. - [Viral loop and K-factor](https://www.skene.ai/resources/frameworks/viral-loop-k-factor): Invites sent, the first half of K. ### Plan change `plan_changed` - [AARRR (pirate metrics)](https://www.skene.ai/resources/frameworks/aarrr-pirate-metrics): The revenue stage. - [SaaS bowtie](https://www.skene.ai/resources/frameworks/saas-bowtie): Expansion and renewal. - [Net revenue retention (NRR)](https://www.skene.ai/resources/frameworks/net-revenue-retention): Expansion and downgrade made in the product. - [Freemium vs free trial](https://www.skene.ai/resources/frameworks/freemium-vs-free-trial): Conversion, and downgrade at the end of a trial. ### Acquisition source on signup `utm_source, referrer` - [AARRR (pirate metrics)](https://www.skene.ai/resources/frameworks/aarrr-pirate-metrics): Splits acquisition by channel. - [Viral loop and K-factor](https://www.skene.ai/resources/frameworks/viral-loop-k-factor): Invite conversion, the second half: which signups came from an invite. ### Signup `account_created` - [Aha moment and time to value](https://www.skene.ai/resources/frameworks/aha-moment): Starts the time-to-value clock. - [Cohort retention analysis](https://www.skene.ai/resources/frameworks/cohort-retention-analysis): Assigns each user to a cohort. ### Plan limit reached `limit_hit` - [Product-qualified lead (PQL)](https://www.skene.ai/resources/frameworks/product-qualified-lead): The strongest upgrade-intent signal. - [Freemium vs free trial](https://www.skene.ai/resources/frameworks/freemium-vs-free-trial): Where the free plan stops. ### Trigger delivered and opened `notification_opened` - [Hook Model](https://www.skene.ai/resources/frameworks/hook-model): The trigger step, as far as it is visible. ### Stored investment `item_saved` - [Hook Model](https://www.skene.ai/resources/frameworks/hook-model): The investment step. ## What practitioners said about the same failures Fourteen founders, product leaders and engineers answered one question for [The Question](https://www.skene.ai/resources/the-question): how their organisation decides what to trust in its product data. Several describe the events under these frameworks failing in their own products, and each framework page links the interviews that bear on it. ## Start with the events, not the framework Pick the event at the top of the map and check that it exists, fires once per real occurrence and carries the account it belongs to. The [free audit](https://www.skene.ai/signup) maps what your product collects today from a connected GitHub repository or Supabase project, once, with no card. On Pro, Skene Cloud also reviews every pull request on a linked GitHub repository for removed or broken tracking. ## 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.