MVP & Product Vocabulary

MVP and the product validation vocabulary:

MVP (Minimum Viable Product) — coined by Eric Ries in "The Lean Startup" (2011). The smallest version of a product that delivers value to early adopters AND generates enough feedback to validate or invalidate the core business hypothesis.

Key properties of a true MVP:
• Has the core functionality only — not polished, not full-featured
• Goes to real users in the real world (not internal testing)
• Designed to answer a specific hypothesis
• Success = learning, not revenue (at this stage)

The "core assumption" / hypothesis:
Every product is built on beliefs that haven't been proven yet. The MVP's job is to test the most important one. Examples:
• "Developers will pay $20/month for this linting tool"
• "Users will onboard without requiring a tutorial"
• "Companies with 50+ employees need a different tier"

Related vocabulary (product development lifecycle):
• hypothesis — an unproven belief the product tests
• validate — confirm an assumption is true using real data
• invalidate / falsify — prove an assumption is wrong
• iteration — a cycle of build → measure → learn
• feature creep — adding more features than needed; the enemy of MVP discipline
• scope — the set of features included in a release ("scope this down" = reduce features)
• dogfooding — using your own product internally before releasing it ("eating your own dog food")
• alpha — very early internal testing; beta — controlled external testing before full launch
• GA (General Availability) — public release, available to all users

North Star Metric — the concept and the engineering implication:

The North Star Metric (NSM) is the single measurement that best represents the value your product delivers to users. It is leading (predicts long-term success) rather than lagging (reflects what already happened, like revenue).

Examples by product:
• Slack: messages sent per active user (are people actually communicating?)
• Airbnb: nights booked (are people actually staying?)
• Spotify: time spent listening per user
• GitHub: pull requests merged
• Linear (project tool): issues closed per engineer

Why not just use revenue?
Revenue is a lagging indicator — by the time it drops, you've already lost users. The NSM captures user engagement before it converts to revenue, giving earlier signal.

The engineering implication:
"Did this feature move our North Star?" is a more rigorous success criterion than "did we ship on time?" Engineers who understand this can challenge product decisions: "This feature adds to our feature count but doesn't move WAU — should we build it?"

Metrics vocabulary (product & engineering):
• North Star Metric (NSM) — single metric capturing the core value delivered
• leading indicator — early signal of future outcome (engagement, sign-ups)
• lagging indicator — outcome that comes after the fact (revenue, churn)
• vanity metric — looks impressive but doesn't reflect real value (total registered users, page views)
• actionable metric — a metric you can actually change with product decisions
• WAU / MAU / DAU — weekly/monthly/daily active users

Product-Market Fit (PMF) — the most important milestone in a startup:

Marc Andreessen's definition (2007): "Product-market fit means being in a good market with a product that can satisfy that market."

Sean Ellis's test: Ask users "How would you feel if you could no longer use this product?" If 40%+ answer "very disappointed" — you have PMF.

Signs of PMF (you feel it):
• Word-of-mouth growth without paid acquisition
• Users return without reminders; email notifications are opened eagerly
• Customer success team can't keep up with inbound requests
• Press writes about you without being paid
• You have a waiting list

Signs of no PMF:
• High churn (users sign up but don't return)
• Sales cycle is painful — convincing each customer requires extensive effort
• Users say "it's nice" but don't use it regularly
• You keep pivoting features

What changes after PMF — engineering implications:
Before PMF: move fast, iterate, focus on learning. Technical debt acceptable.
After PMF: scale, reliability, observability become critical. Engineers now face growth engineering challenges: database scaling, performance, infra cost optimisation, feature flagging for gradual rollouts.

Vocabulary:
• PMF (product-market fit) — your product satisfies strong market demand
• discovery mode — pre-PMF: exploring, validating, pivoting
• growth mode — post-PMF: scaling, optimising, expanding
• churn — rate at which users stop using the product
• retention — rate at which users continue using the product
• pivot — a fundamental change in product strategy based on learning

Pivot — the startup vocabulary for strategic change:

Eric Ries (The Lean Startup) defines a pivot as: "a structured course correction designed to test a new fundamental hypothesis."

Types of pivots:
• Zoom-in pivot — a single feature becomes the whole product (Instagram pivoted from Burbn to photo sharing)
• Zoom-out pivot — the whole product becomes one feature of a larger product
• Customer segment pivot — same product, different target customer
• Platform pivot — change from an application to a platform (or vice versa)
• Business model pivot — change how you make money (free to paid, SaaS to marketplace)
• Technology pivot — same customer problem, different underlying technology

Famous pivots:
• Slack — started as a gaming company (Tiny Speck / Glitch); the internal chat tool became the product
• Instagram — pivoted from Burbn (check-ins) to photo sharing
• YouTube — started as a video dating site
• Twitter — started as a podcasting platform (Odeo)

Pivot vs. iteration:
• Iteration: "We're changing the onboarding flow" — minor product change
• Pivot: "We're changing who we sell to" — fundamental strategy change

Vocabulary for strategic decisions:
• pivot — structured change in strategy, keeping company alive
• persevere — continue current strategy with more time/data
• traction — evidence of user adoption and growth
• runway — how long the company can operate before running out of money

Priority levels and dogfooding — startup engineering vocabulary:

Priority labels (P0-P4):
• P0 — Critical / SEV-1. Drop everything. Site down, security breach, data loss, completely blocked. Fix now.
• P1 — High priority. Major feature broken, blocking many users. Fix this sprint / this week.
• P2 — Medium. Important but has a workaround. Scheduled work.
• P3 — Low. Nice to have. Backlog.
• P4 — Minimal impact. Maybe someday.

(Priority labelling systems vary — some companies use Severity 1-5, Critical/High/Medium/Low, or Blocker/Must/Should/Could/Won't from MoSCoW.)

Dogfooding:
Origin: 1988 Microsoft manager Eat Our Own Dog Food — encouraging Microsoft to use Microsoft software internally. Now a standard tech industry practice.
• Dogfooding = using your own product internally before releasing to customers
• Purpose: find issues that would embarrass you with real users, build team empathy with the product
• Dogfooding team — internal users of pre-release builds
• Beta — the version released to external early adopters (after dogfooding, before GA)

Related product release vocabulary:
• internal beta — dogfooding; only internal team uses it
• closed beta — invited external users only
• open beta — any user can opt in; still pre-release
• GA (General Availability) — full public release
• canary deployment — deploy to small % of users first to catch issues before full rollout
• feature flag — turn a feature on/off for specific users without deploying new code

MVP stands for 'Minimum Viable Product,' which is a term used in startups and product development. It refers to the simplest version of a product that can be released to early adopters to validate assumptions and gather feedback – this comment highlights the focus on core functionality for initial validation.

The term /users is a common convention in API design, where it typically refers to a resource endpoint that manages data related to users within the system – this is fundamental to any application dealing with user accounts. It's a concise way to identify the core functionality exposed through the API.

OAuth 2.0 is commonly used in MVPs to simplify user authentication by allowing users to leverage existing accounts (like Google) – this reduces friction and focuses development on core functionality rather than complex account creation processes. It's a strategic choice for initial user adoption.

In an MVP scenario, 'relevance' refers to how well a feature meets the core needs of early adopters – this is directly tied to validating the product's value proposition. Poor relevance signals that the product isn't solving a key problem for users, which is why it's critical for the MVP.

Dogfooding is the practice of using a product internally – in this case, by the team themselves – to identify bugs and usability issues before releasing it to external users. It's a common technique for validating product quality and ensuring a smooth initial user experience.