Glossary of Product Management Terms
Plain-language definitions for the vocabulary I use across the Atlas. Each entry runs a sentence or two, and where a full note exists, the term links to it. It's alphabetical so you can scan it fast. When a definition reflects a specific author or model, I credit them.
#A
A/B Test: A controlled experiment that splits users between two (or more) variants to measure which moves a metric, with statistical significance. See Experimentation and A-B Testing.
Acceptance Criteria: The specific, testable conditions a feature must meet to be considered done; the contract between a PM and engineering inside a spec. See Writing PRDs and Specs.
Agile: An iterative delivery philosophy (Scrum, Kanban) favoring short cycles and feedback over big up-front plans. A how-to-deliver method, not a product strategy. See Agile Delivery and Team Cadence.
APM (Associate Product Manager): The entry rung of the ladder; executes well-scoped work for a feature with manager support. See Associate Product Manager.
#B
Backlog: The prioritized list of work a team could do. Managing a backlog is a task, not the job, and confusing the two is a core myth.
Build Trap: Melissa Perri's term (from Escaping the Build Trap) for an org stuck shipping features (output) without connecting them to outcomes. See Outcomes Over Outputs and The Product Management Canon.
Build-Measure-Learn: Eric Ries's loop at the heart of Lean Startup: build the smallest thing, measure, learn, iterate. See Build-Measure-Learn and the MVP.
Business Model: How a product creates, delivers, and captures value (who pays, for what, at what margin). See Business Acumen and Models.
#C
Continuous Discovery: Teresa Torres's practice of weekly customer touchpoints by the product trio, so discovery never stops and never splits from delivery. See Continuous Discovery.
Conversion Rate: The share of users who complete a target action (sign-up, purchase). A foundational product metric.
Cohort Analysis: Grouping users by a shared trait (e.g., signup week) to compare behavior over time, the cleanest way to see retention. See Product Analytics and Metrics.
#D
DAU / MAU: Daily / Monthly Active Users; the DAU/MAU ratio is a rough stickiness measure. See Product Analytics and Metrics.
DHM Model: Gibson Biddle's strategy test: a good strategy should Delight customers, be Hard to copy, and be Margin-enhancing. See The DHM Model.
Discovery: The work of deciding what to build and whether it's worth building, before committing to delivery. See User Research and Discovery and The Four Big Product Risks.
Dual-Track Agile: Running discovery and delivery as parallel, continuous tracks rather than sequential phases. See Continuous Discovery.
#E
Eisenhower Matrix: A 2×2 of urgent × important used to triage tasks: do, decide, delegate, delete. See The Eisenhower Matrix.
Eval: A test that measures whether an AI system's output meets a defined quality bar; the AI-era analog of a unit test, and the most-cited new PM skill. See Writing Evals for AI Products.
Experiment: Any structured test of a hypothesis, from a painted-door test to a full A/B test. See Hypothesis-Driven Development.
#F
Feature Factory: John Cutler's anti-pattern: a team that measures success by features shipped, not outcomes moved. The enemy of Outcomes Over Outputs.
Feature Team: A team handed features to build (output, low autonomy), contrasted with an empowered product team. See Product Teams vs Feature Teams.
Feasibility: Whether a solution can actually be built with available tech and skill; one of The Four Big Product Risks, owned by engineering.
#G
Go-to-Market (GTM): The plan to launch and commercialize a product: positioning, pricing, channels, enablement. See Shipping and Launch.
Growth Plan: A structured development plan that targets a PM's lowest competencies with concrete tasks. See Building a Growth Plan.
#H
Hypothesis-Driven Development: Framing work as falsifiable bets ("we believe X will cause Y") and shipping to learn. See Hypothesis-Driven Development.
HITL (Human-in-the-Loop): A design pattern that inserts human review/approval into an automated or AI workflow, non-negotiable for high-stakes actions. See Designing for Trust and Probabilistic Systems.
#I
Impact-Effort Matrix: A prioritization 2×2 plotting expected impact against effort to find quick wins. See Prioritization Frameworks.
Initiative Pyramid: Curtis Stanier's model relating initiative risk and count to how involved senior leadership should be. See The Initiative Pyramid.
#J
Jobs To Be Done (JTBD): originated by Tony Ulwick and popularized by Clayton Christensen: customers "hire" a product to make progress on a job; you compete with every other way they could get that job done. See Jobs To Be Done.
Journey Map: A visualization of a user's end-to-end experience across stages, surfacing pain points. See User Personas and Journey Mapping.
#K
Kano Model: Noriaki Kano's classification of features into basic, performance, and delighter categories by their effect on satisfaction. See The Kano Model.
KPI (Key Performance Indicator): A metric a team commits to moving as a measure of success. See Product Analytics and Metrics and OKRs and Goal-Setting.
#L
LNO Framework: Shreyas Doshi's effort-allocation model sorting tasks into Leverage, Neutral, and Overhead. See The LNO Framework.
Leading vs Lagging Indicator: A leading indicator predicts future outcomes (input you can move now); a lagging one confirms results after the fact. See The North Star Framework.
#M
MVP (Minimum Viable Product): The smallest build that lets you test a hypothesis and learn. A learning vehicle, not a tiny v1. See Build-Measure-Learn and the MVP.
MoSCoW: A prioritization method labeling requirements Must / Should / Could / Won't have. See MoSCoW Prioritization.
Missionaries vs Mercenaries: Cagan's contrast between teams that own a mission and teams that just build what they're told. See Product Teams vs Feature Teams.
#N
North Star Metric: The single value metric that best captures the value a product delivers to customers, paired with input metrics. See The North Star Framework.
#O
OKR (Objectives and Key Results): A goal-setting framework pairing a qualitative objective with measurable key results. See OKRs and Goal-Setting.
Opportunity Solution Tree: Teresa Torres's map connecting a desired outcome to opportunities, solutions, and experiments. See The Opportunity Solution Tree.
Outcome vs Output: Outcome = a change in customer or business behavior; output = the thing you shipped. Outcomes are the point. See Outcomes Over Outputs.
#P
Persona: A research-grounded archetype of a user segment, used to keep decisions user-centered. See User Personas and Journey Mapping.
PRD (Product Requirements Document): The artifact that captures the problem, scope, and success criteria of a feature; the spec is the shared understanding it creates. See Writing PRDs and Specs.
Product Sense: The intuition for what makes a good product: taste plus judgment about users and value. Trainable, not innate. See Product Sense and Judgment.
Product Trio: Torres's pairing of PM + design + engineering as the core discovery unit. See Continuous Discovery.
Porter's Five Forces: Michael Porter's framework for industry attractiveness: rivalry, new entrants, substitutes, supplier and buyer power. See SWOT and Porter's Five Forces.
#R
RACI: A responsibility chart marking who is Responsible, Accountable, Consulted, and Informed for each decision. See RACI and Decision Rights.
RICE: A scoring model ranking ideas by Reach × Impact × Confidence ÷ Effort. See RICE and Scoring Models.
Retention: The share of users who keep coming back over time; usually the truest signal of product-market fit. See Product Analytics and Metrics.
Roadmap: An outcome-focused communication of where a product is heading and why, not a dated list of promised features. See Building and Managing a Roadmap.
#S
Spec: Short for specification: the shared understanding of what to build and why. The document is the artifact, not the spec itself. See Feature Specification.
Statistical Significance: The confidence that an experiment's measured difference is real, not noise. See Experimentation and A-B Testing.
SWOT: A scan of Strengths, Weaknesses, Opportunities, and Threats. See SWOT and Porter's Five Forces.
#T
T-Shaped: Deep expertise in one or two areas (the vertical) plus working breadth across many (the horizontal). See The T-Shaped PM and Knowing Your Shape.
Technical Debt: The future cost incurred by choosing a fast, imperfect solution now; a recurring quality trade-off.
#U
Usability: Whether users can actually figure out how to use a solution; one of The Four Big Product Risks, owned by design. See User Experience Design.
Unit Economics: The revenue and cost of a single unit of product (a user, a transaction, an inference), and why AI products behave differently from SaaS. See The Unit Economics of AI.
#V
Value Risk: Whether customers will choose or buy a solution at all; the first and biggest of The Four Big Product Risks.
Viability (Business Viability): Whether a solution works for the business (legal, financial, brand, GTM); the PM's to own. See The Four Big Product Risks and Business Outcome Ownership.
Vision: The compelling long-term picture of the future a product is working toward. See Crafting a Product Vision.
Voice of the Customer: The discipline of using user feedback and research to drive decisions. See Voice of the Customer.
#Continue Reading
- The Frameworks Toolkit for the full set of frameworks named above, each with its own note.
- The Master Competency Matrix for where these terms map onto the competency model.
- The PM Tool Landscape for the software that operationalizes many of these concepts.
- The Product Management Canon for the books these ideas come from.