Definition
Minimum Viable Product (MVP)
A minimum viable product is the smallest version of a product that can be released to learn the most about customers with the least effort. Rather than building everything, a team ships just enough to test whether the core idea solves a real problem, then uses real-world feedback to decide what to build, change, or abandon next.
Key takeaways
- A minimum viable product (MVP) is the smallest releasable version that produces the most customer learning for the least effort.
- Coined by Frank Robinson and popularized by Eric Ries in The Lean Startup, an MVP is a hypothesis test, not a low-quality product.
- Its job is to validate the riskiest assumption — usually whether anyone wants it — before investing in scale or polish.
- Pair an MVP with success metrics up front so the build-measure-learn loop yields a decision: persevere or pivot.
The term was coined by Frank Robinson around 2001 and popularized by Steve Blank and Eric Ries as a cornerstone of the Lean Startup method. Ries defined an MVP as the version of a new product that lets a team gather the maximum amount of validated learning about customers with the least amount of effort. The emphasis is on learning, not on shipping a stripped-down feature list.
An MVP is a hypothesis test, not a low-quality release. The goal is to validate the riskiest assumption — usually whether anyone wants the thing at all — before investing in scale, polish, or breadth. That can mean a single workflow, a concierge service done by hand, or even a landing page measuring sign-ups. What makes it viable is that a real customer can use it and give real signal.
The discipline is resisting feature creep: every addition beyond what's needed to learn delays feedback and inflates sunk cost. Teams pair the MVP with clear success metrics up front, so the build-measure-learn loop produces a decision — persevere or pivot — rather than just an opinion.
In Planoda, you can scope an MVP as its own initiative with a tight set of issues and a measurable north-star metric, so the learning goal stays visible alongside the work.
Related terms
- Lean StartupThe Lean Startup is a methodology, formalized by Eric Ries, for building products under extreme uncertainty by treating ideas as hypotheses to be tested cheaply. Teams cycle through a build-measure-learn loop — ship a minimum viable product, measure real behavior, learn, then persevere or pivot — to find a sustainable business with minimal wasted effort.
- Product-Market FitProduct-market fit is the point at which a product satisfies a strong market demand — the right product serving the right market so well that growth begins to pull rather than push. It is the milestone before which a startup should focus on finding fit, and after which it should focus on scaling, often felt as demand outrunning the team's ability to keep up.
- Jobs to Be Done (JTBD)Jobs to Be Done is a framework for understanding why customers adopt a product: people "hire" products to make progress on a specific job in a given circumstance. It shifts focus from customer demographics and product features to the underlying goal — the job — revealing the real competition and the true criteria by which customers judge success.
- PRD (Product Requirements Document)A PRD (product requirements document) defines what a product or feature should do and why, before it is built. It captures the problem, the target users, the goals and success metrics, the scope, and the requirements — giving design, engineering, and stakeholders one shared reference so everyone builds toward the same outcome rather than their own interpretation.
- Continuous DiscoveryContinuous discovery is the practice of engaging with customers regularly — ideally weekly — to inform product decisions, rather than running discovery as a one-off phase before delivery. Small cross-functional teams interview users, spot unmet needs, test solution ideas, and feed that learning directly into what they build, keeping discovery and delivery running in parallel.