Product Management Glossary

The PM vocabulary, defined plainly — no fluff, practitioner framing. Put these to work in the PLA community or test yourself with the credential.

Product Manager (PM): The person accountable for a product's outcomes: deciding what to build, why, and in what order — by connecting user needs, business goals, and technical reality.
Product Discovery: The continuous work of learning what customers actually need and which solutions are worth building, before committing engineering time. Interviews, prototypes, and experiments over opinions.
Product-Market Fit (PMF): The point where a product satisfies a strong market demand — retention, organic growth, and willingness to pay signal it far better than survey enthusiasm.
Roadmap: A communication artifact expressing product direction over time. Modern roadmaps are outcome-oriented (problems to solve) rather than feature-and-date lists.
OKRs (Objectives & Key Results): A goal-setting framework pairing a qualitative objective with 2–5 measurable key results. Key results measure outcomes, not task completion.
North Star Metric: The single metric that best captures the value a product delivers to customers, used to align teams — e.g. weekly active learners rather than signups.
MVP (Minimum Viable Product): The smallest version of a product that can generate validated learning about customers — a learning tool, not an excuse for a weak v1.
Jobs to Be Done (JTBD): A framing where customers 'hire' products to make progress in a circumstance. Focuses discovery on the underlying job, not demographics or feature requests.
User Story: A short requirement expressed from the user's perspective — 'As a [user], I want [capability] so that [benefit]' — meant to trigger conversation, not replace it.
Backlog: The ordered list of everything a team might build. Healthy backlogs are ruthlessly pruned; a 500-item backlog is a graveyard, not a plan.
Prioritization Frameworks (RICE, ICE, MoSCoW): Structured ways to rank work: RICE scores Reach, Impact, Confidence, and Effort; ICE drops Reach; MoSCoW buckets into Must/Should/Could/Won't. Inputs matter more than the arithmetic.
A/B Testing: Running two variants simultaneously with randomized audiences to measure causal impact on a metric. Requires enough traffic and a pre-registered success metric to be honest.
Activation: The moment a new user first experiences a product's core value. Activation-rate improvements compound through every downstream metric.
Retention: Whether users keep coming back over time, usually read as cohort curves. The single strongest signal of real product value.
Churn: The rate at which customers stop using or paying for a product. The inverse lens on retention, and the first place to look when growth stalls.
Go-to-Market (GTM): The plan for reaching customers and winning them: positioning, pricing, channels, sales motion, and launch sequencing.
Positioning: The deliberate choice of market frame — what category you're in, who it's for, and why you win — so buyers instantly understand the product's value.
PLG (Product-Led Growth): A go-to-market motion where the product itself drives acquisition, conversion, and expansion — free tiers, self-serve onboarding, and in-product upgrade paths.
Technical Debt: The accumulated cost of past shortcuts in a codebase — paid as slower delivery. PMs negotiate its paydown like any other investment: by expected outcome.
Agile / Scrum: Iterative delivery in short cycles with regular inspection. Scrum's mechanics (sprints, standups, retros) implement it; the point is fast feedback, not ceremony.
Sprint: A fixed timebox (usually 1–2 weeks) in which a team commits to a set of work and demonstrates a shippable increment at the end.
Stakeholder Management: Keeping the people who influence your product's success informed, heard, and aligned — proactively, with evidence, before decisions harden.
Product Ops: The discipline of scaling product-team effectiveness: tooling, data access, experiment infrastructure, and consistent processes across squads.
AI Product Management: Product management for AI-powered features: managing probabilistic behavior, evaluation datasets, model cost/latency tradeoffs, and user trust — plus using AI agents in the PM workflow itself.

The PM vocabulary, defined plainly — no fluff, practitioner framing. Put these to work in the PLA community or test yourself with the credential.

Product Manager (PM)

The person accountable for a product's outcomes: deciding what to build, why, and in what order — by connecting user needs, business goals, and technical reality.

Product Discovery

The continuous work of learning what customers actually need and which solutions are worth building, before committing engineering time. Interviews, prototypes, and experiments over opinions.

Product-Market Fit (PMF)

The point where a product satisfies a strong market demand — retention, organic growth, and willingness to pay signal it far better than survey enthusiasm.

Roadmap

A communication artifact expressing product direction over time. Modern roadmaps are outcome-oriented (problems to solve) rather than feature-and-date lists.

OKRs (Objectives & Key Results)

A goal-setting framework pairing a qualitative objective with 2–5 measurable key results. Key results measure outcomes, not task completion.

North Star Metric

The single metric that best captures the value a product delivers to customers, used to align teams — e.g. weekly active learners rather than signups.

MVP (Minimum Viable Product)

The smallest version of a product that can generate validated learning about customers — a learning tool, not an excuse for a weak v1.

Jobs to Be Done (JTBD)

A framing where customers 'hire' products to make progress in a circumstance. Focuses discovery on the underlying job, not demographics or feature requests.

User Story

A short requirement expressed from the user's perspective — 'As a [user], I want [capability] so that [benefit]' — meant to trigger conversation, not replace it.

Backlog

The ordered list of everything a team might build. Healthy backlogs are ruthlessly pruned; a 500-item backlog is a graveyard, not a plan.

Prioritization Frameworks (RICE, ICE, MoSCoW)

Structured ways to rank work: RICE scores Reach, Impact, Confidence, and Effort; ICE drops Reach; MoSCoW buckets into Must/Should/Could/Won't. Inputs matter more than the arithmetic.

A/B Testing

Running two variants simultaneously with randomized audiences to measure causal impact on a metric. Requires enough traffic and a pre-registered success metric to be honest.

Activation

The moment a new user first experiences a product's core value. Activation-rate improvements compound through every downstream metric.

Retention

Whether users keep coming back over time, usually read as cohort curves. The single strongest signal of real product value.

Churn

The rate at which customers stop using or paying for a product. The inverse lens on retention, and the first place to look when growth stalls.

Go-to-Market (GTM)

The plan for reaching customers and winning them: positioning, pricing, channels, sales motion, and launch sequencing.

Positioning

The deliberate choice of market frame — what category you're in, who it's for, and why you win — so buyers instantly understand the product's value.

PLG (Product-Led Growth)

A go-to-market motion where the product itself drives acquisition, conversion, and expansion — free tiers, self-serve onboarding, and in-product upgrade paths.

Technical Debt

The accumulated cost of past shortcuts in a codebase — paid as slower delivery. PMs negotiate its paydown like any other investment: by expected outcome.

Agile / Scrum

Iterative delivery in short cycles with regular inspection. Scrum's mechanics (sprints, standups, retros) implement it; the point is fast feedback, not ceremony.

Sprint

A fixed timebox (usually 1–2 weeks) in which a team commits to a set of work and demonstrates a shippable increment at the end.

Stakeholder Management

Keeping the people who influence your product's success informed, heard, and aligned — proactively, with evidence, before decisions harden.

Product Ops

The discipline of scaling product-team effectiveness: tooling, data access, experiment infrastructure, and consistent processes across squads.

AI Product Management

Product management for AI-powered features: managing probabilistic behavior, evaluation datasets, model cost/latency tradeoffs, and user trust — plus using AI agents in the PM workflow itself.