Minimum Viable Product Planner
Determine the smallest testable product scope by connecting the target user, critical problem, learning objective, end-to-end journey, candidate features and practical delivery constraints.
An MVP is the smallest coherent product scope that can deliver meaningful learning—not simply the fewest features.
The planner connects one target user and problem to a critical learning objective, a complete minimum journey, a deliberately constrained feature set and enough evidence capability to test whether the concept deserves further investment.
Minimum Viable Product Planner — Step by Step
Eight locked stages move from context through user, learning goal, core journey, feature scope and testability constraints, then apply a capacity challenge and conclude with management review.
User, Learning Goal, Minimum Journey, Scope and Testability
Reveal the product context to begin.
Track what is in the MVP, what is deferred and why.
Test an MVP Scope Trade-off
Experiment Mode is isolated from the guided demonstration. Adjust capacity, feature count, learning coverage and test fidelity to observe the balance between scope economy and evidence strength.
Test MVP planning judgement.
Minimum viable product planning cues
Target User
Define the user whose behaviour and outcome matter for this learning cycle. Avoid designing for everyone at once.
Critical Problem
Anchor the MVP in the problem or job that creates the reason to use the product—not in a list of preferred features.
Learning Goal
State the uncertainty the MVP must reduce. The scope should exist to generate evidence for that learning goal.
Minimum Journey
Keep the smallest end-to-end path that lets the user reach the intended outcome. A broken journey is not viable.
Feature Discipline
Include only features required for the journey, critical learning or safe operation. Defer convenience and scale features.
Decision Rule
Define what evidence will justify continuing, changing or stopping before expanding the product beyond the MVP.
Protect the minimum user journey and evidence capability, remove features that do not change the decision, and use the resulting learning to decide what deserves to be built next.