Learning objectives :
- Identify the features that will add the most value in the initial version of a product or service
- Create a low-cost version to gauge customer interest before investing further
- Validate a strategy on a small scale using a structured testing approach
Course :
Session 1: Key Steps in the MVP Approach
A common mistake is trying to do everything before showing anything. The MVP approach flips this logic: test first, invest later. Participants learn how to structure this process using the MVP Canvas, to move from an idea to a concrete test plan.
Case Study : Discover the key steps of the MVP approach through a concrete example, understand the logic behind each section of the canvas, and identify common mistakes that turn an MVP into a "full-scale" project in disguise.
Step 2: Identify Priority Features
Deciding what to include in V1 is the most difficult decision in the MVP process. This step guides participants in making the right trade-offs: distinguishing between what is essential for testing the hypothesis and what can wait for future versions.
Case Study Example : Using a real-world scenario, review a list of potential features, collectively decide which ones should be included in the first release and which ones should be excluded, and justify each decision in terms of customer value and development cost.
Session 3: Creating Your MVP Canvas
The final session is the most hands-on: participants work in small groups to create a complete MVP Canvas for a real-world project and leave with an actionable testing plan.
Case Study Example : In small groups, build a complete MVP Canvas for a real-world project, present it to the other groups, and receive feedback on the soundness of the assumptions, the relevance of the selected features, and the feasibility of the test plan.
When you leave this workshop, you'll know...
- Using the MVP Canvas to Structure a Low-Cost Testing Approach
- Decide which features are essential for Version 1 and which ones to develop later
- Validate a product or strategic hypothesis on a small scale before investing further
And it'll come in handy for...
- Launch a new product, service, or project while minimizing investment risks
- Make development decisions based on evidence rather than assumptions
- Develop a culture of testing and iteration within your team or organization


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