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.