--- title: "Product qualified accounts (PQA): fit and readiness routing | Skene" description: "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." url: https://www.skene.ai/resources/frameworks/product-qualified-account doc_version: "51e269c7d116" last_updated: 2026-10-30 --- Resources / Frameworks # Product-qualified accounts: fit, readiness, and a guard before the sales call Product-qualified account (PQA). Credit: A product-led growth term with no single originator; Dave Boyce's _Freemium_, Chapter 14. By [Teemu Kinos](https://www.skene.ai/about), co-founder of Skene. A product-qualified account, or PQA, scores the company rather than the person. Dave Boyce sets it out in _Freemium_ as two scores read together: fit, from firmographic data, and readiness, from what the account's users have done in the product. The combination decides which accounts get a salesperson, which get help and which are left to succeed on their own. ## How it works Boyce's routing has three outcomes. Low-fit accounts stay on the self-serve path. High-fit accounts with low readiness get an assistance play, run by people measured on usage rather than quota, whose question is how to help the account reach value. High-fit, high-readiness accounts get a sales play about expanding that value. Boyce reports PQAs converting at four to seven times the rate of marketing-qualified leads (Ch 14). He adds a rule that matters more than either score: no sales contact before the account reaches First Impact. That turns one product event into a gate on the entire sales motion, and it means a tracking break does not just skew a report. It decides who gets a phone call. ## What it assumes your events already do | Event | What the framework uses it for | What it reports when the event breaks | | --- | --- | --- | | User-to-account join `account_id on every event` | Every readiness signal rolls up to an account. | Users without an account identifier vanish from readiness, so the best-fit companies look idle. | | Activation or First Impact event `first_report_shared` | The First Impact guard before any sales contact. | The guard either blocks every account from sales or lets reps call accounts that have not seen value. | | Invite sent and accepted `invite_sent, invite_accepted` | Seat growth as a readiness signal. | Untracked seat additions make expanding accounts look flat, and they are routed to self-serve. | | Core value action `report_viewed` | Usage depth inside the account. | A change in how the action fires moves readiness for every account at once, which no real buying pattern does. | 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 account called on day one _Illustrative example. The company and the figures are invented to show the mechanism._ A refactor moves the First Impact event, `first_dashboard_published`, behind a feature flag, and it stops firing for new workspaces. The guard now reads every new account as not activated, so sales stops calling any new account. Pipeline dries up within a fortnight. An engineer removes the guard to unblock it, and reps start calling three-user accounts on the day they sign up. Both failures trace back to one event that nobody was watching. ## Checks before you trust it - Check that every user event carries the account identifier before building readiness on it. - Report the First Impact rate by signup week; a sudden change after a release is a tracking question first. - Keep fit and readiness as separate scores, so a broken usage signal cannot hide inside a blended number. - Log each routing decision with the events that triggered it, so a wrong call can be traced. ## What Dave Boyce adds in _Freemium_ Boyce's capacity model ties sales headcount to PQA supply: new self-serve accounts, times the PQA rate, times the close rate, gives sales. His example is 50 accounts at 10% and 20%, which yields one deal (Ch 13). If the PQA rate is wrong because its events are, the hiring plan is wrong with it. ## Check the events under it Before Product-qualified account (PQA) 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 - [Product-qualified leads: a sales signal built from in-product events](https://www.skene.ai/resources/frameworks/product-qualified-lead) - [The SaaS bowtie: the half of the journey after closed-won](https://www.skene.ai/resources/frameworks/saas-bowtie) - [Net revenue retention: the investor number, and the product events that explain it](https://www.skene.ai/resources/frameworks/net-revenue-retention) - Glossary: [Activation event](https://www.skene.ai/resources/glossary/activation-event) - Glossary: [Cohort](https://www.skene.ai/resources/glossary/cohort) - Glossary: [Conditional firing change](https://www.skene.ai/resources/glossary/conditional-firing-change) - 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.