MVP Types — Pick the Right One
Concierge, Wizard-of-Oz, smoke test, landing page, prototype — when to use which.
Attributed to Eric Ries (popularized the concept of concept MVP and methodology)
What it is
A Minimum Viable Product (MVP) is the version of a new product that allows a team to collect the maximum amount of validated learning about customers with the least effort. The concept was popularized by Eric Ries as part of the Lean Startup methodology. An MVP is not necessarily the smallest product, but rather the product that facilitates the quickest learning cycle. Different types of MVPs are suitable for different stages of product development and different types of assumptions that need to be validated. The selection of an MVP type often depends on the core risk or hypothesis being tested, the resources available, and the desired fidelity of the user experience. By choosing the right MVP, teams can efficiently test critical assumptions, gather user feedback, and iterate on their product with minimal investment.
When to use it
- When validating a core problem or solution hypothesis.
- When resources (time, money, engineering effort) are limited.
- When seeking early user feedback to guide product development.
- When de-risking a new product or feature before full-scale development.
- When exploring market demand for a novel offering.
- When quickly iterating on product ideas in a "build-measure-learn" loop.
How to use it
- 1
Identify Your Core Hypothesis
- 2
Determine Desired Learning Outcome
- 3
Explore MVP Types
- 4
Match MVP Type to Hypothesis and Learning
- 5
Define Scope and Metrics
- 6
Execute and Test
- 7
Analyze and Iterate
Key concepts
Validated Learning
The process of demonstrating empirically that a business hypothesis is true. It emphasizes learning what customers want by observing their reactions to actual product iterations.
Lean Startup
A methodology for developing businesses and products, which aims to shorten product development cycles by adopting a combination of business-hypothesis-driven experimentation, iterative product releases, and validated learning.
Hypothesis Testing
The process of making an assumption, setting up an experiment to validate or invalidate that assumption, and then drawing conclusions based on the results.
Concierge MVP
A manual service that replicates the intended automated product experience, allowing for direct interaction with users and deep learning about their needs.
Wizard of Oz MVP
Appears fully functional to the user, but the back-end automated processes are actually performed manually by the team, simulating the future product experience.
Smoke Test
An experiment to gauge demand for a product or feature before building it, often through advertisements or landing pages that measure interest.
Fidelity
Refers to the level of detail and realism in a product or prototype. Low-fidelity implies basic representations, while high-fidelity is closer to the final product.
Common pitfalls
- Building too much functionality into the MVP, delaying launch and learning.
- Not clearly defining the hypothesis or metrics for success before launching.
- Failing to iterate or act on the learning derived from the MVP.
- Confusing an MVP with a poorly designed or incomplete product.
- Not selecting the appropriate MVP type for the specific learning goal.
- Ignoring user feedback or misinterpreting data gathered from the MVP.
Further reading
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