Product Management Glossary

The percentage of new signups who reach a meaningful action, the moment they truly understand your product's value.

Automatically identifying and merging similar feature requests using semantic similarity instead of keyword matching.

A 2x2 framework mapping four growth strategies across products and markets.

Releasing a feature to a limited group before general availability to catch bugs and validate assumptions.

A running log of what's changed in your product, new features, improvements, and fixes, written for your customers.

The percentage of customers who cancel or stop using your product in a given period.

The process of collecting customer feedback, acting on it, and communicating back to the customer what you did about it.

The practice of having regular, ongoing customer touchpoints to inform product decisions, not just one-time research sprints.

The revenue or value you lose for every week a feature isn't shipped.

A select group of customers who give structured strategic feedback and input on product direction.

A metric that measures how easy it was for a customer to complete a task or get help, lower effort means higher loyalty.

A composite metric that predicts churn risk by combining usage, support activity, NPS, and engagement signals.

A metric that measures how customers feel about your product, derived from surveys, reviews, and support signals.

Driver, Approver, Contributor, Informed, a decision-making framework that clarifies who does what on any decision.

A product team that ships features nonstop without measuring outcomes, optimizing for output instead of impact.

A toggle that controls whether a feature is visible to users, letting you ship code without shipping the feature.

A customer-submitted idea or ask for new functionality, an improvement, or a change to your product.

A shared space, public or private, where customers can submit ideas, vote on requests, and see what the team is working on.

A pricing model where a free tier drives product adoption, with paid upgrades for more features or usage.

How often an AI model confidently generates false or made-up information as if it were fact.

A survey that appears inside your product while the customer is using it, higher context, higher response rates.

A framework focused on the outcome customers want, not the feature they request.

A framework for categorizing features by how they affect customer satisfaction.

A self-serve library of documentation that helps customers answer their own questions without contacting support.

A short 1-3 question survey triggered in-context at a specific moment in your product.

A backlog triage method: Must-have, Should-have, Could-have, Won't-have.

A loyalty metric based on one question: how likely are you to recommend this product? Scored 0–10.

Objectives and Key Results, a goal-setting framework that connects team work to measurable company outcomes.

A visual framework that connects business outcomes to customer opportunities and the experiments designed to address them.

The AARRR framework, Acquisition, Activation, Retention, Revenue, Referral, for measuring SaaS growth.

The operational backbone that keeps product teams efficient through data hygiene, tooling, and process standardization.

A user who's shown purchase intent through product usage, not just marketing signals.

A plan that shows where your product is headed, what you're building, when, and why.

A go-to-market strategy where the product itself drives acquisition, expansion, and retention.

When your product satisfies a strong market demand, the milestone every early-stage startup is chasing.

Writing instructions that reliably get useful, consistent outputs from an AI model.

A roadmap shared externally showing customers what's planned, in progress, and shipped.

A summary of what changed in a specific release, distinct from the ongoing changelog record.

An AI approach that pulls in relevant external data before generating a response, reducing made-up answers.

A prioritization formula weighing Reach, Impact, Confidence, and Effort.

A signup and setup experience that lets users reach product value entirely on their own, without sales involvement.

Using NLP to classify feedback as positive, negative, or neutral, with real limits around sarcasm and nuance.

The meeting where a team decides what to build in the next sprint, based on priorities.

The accumulated cost of saying yes to one-off requests instead of following a coherent product vision.

Shortcuts taken during development that create future rework, and like financial debt, it compounds over time.

A roadmap organized around strategic problems to solve, not a list of features to ship.

How long it takes a new user to reach their first meaningful outcome in your product.

A short description of a feature from the user's perspective, in a standard format.

A database that stores text as numerical embeddings, enabling fast semantic similarity search.

When a few loud users distort your sense of what most customers actually want.