Author: Khang Trần Hoàng

Lead trainer: Phan Hoang Lan

Program Manager: Nguyen Vi Hoa

Blog Advisor: Phuong-Linh Le-Nguyen 

Summary

• Day 3 focused on testing and refining solutions using the Lean Experiment framework: Assumptions, Hypotheses, Planning, and Metrics.

• Critical assumptions should identify if the chosen pain points are real and if the solution’s core values truly address them.

• Hypotheses describe the expected outcome if assumptions hold true, shifting from problem-focused to solution-focused statements.

• Students built prototypes to gather quick feedback internally, then created MVPs to test core functions with real customers.

• Two main testing approaches were shared: A/B Testing (compare versions) and Time Phase Testing (measure changes over time).

• Metrics act as measurable KPIs to decide if experiments succeed; targets should be realistic and based on context.

• Students tested ideas, iterated prototypes, then developed MVPs in Canva, and gained practical insights from mentoring sessions with entrepreneurs.

• Key reflection: testing is ongoing, most first solutions fail, and teams must keep refining assumptions, prototypes, MVPs, and benchmarks. 

Key Learnings: Identify Solution 

Yesterday, our students poured their hearts into creating their solutions. Today, our trainers, Ms. Phan Hoang Lan and Mr. Pham Quang Huy, want to emphasize the importance of testing and reevaluating those solutions. 

Since 90% of initial tests tend to yield negative results, it’s crucial to follow a scientific experimentation loop—repeating tests, learning from failures, and continuously improving. The process CEI introduces today will ensure that with every setback, we gain valuable insights and steer the product toward success:

• Lean Experiment: A quick process to test whether someone will buy your product. The first solution often fails, and if it is not carefully tested, it can result in product failure.

There are many reasons why you should test your product multiple times before releasing it to the public, but they all stem from one root cause: the vision you put on paper often does not match the customer’s reality. This mismatch can lead to miscommunication and a series of cascading losses. The Lean Experiment framework helps create a constant feedback loop, allowing you to evaluate and adjust without risking fatal consequences for your startup.

The lean framework has 4 steps:

• Assumption: An idea that something is true or will happen, accepted without proof or evidence. It usually involves taking something for granted or treating it as fact without real validation. You need to find actual evidence to confirm whether it is true or false.

• Hypothesis: The expected outcome if your assumption is true. This serves as the foundation for your testing, so it needs to be reevaluated and refined multiple times.

• Planning: What form will your idea take? How will you introduce it to the market? What does the initial product (visual or physical) look like? How many resources and how much time do you have to carry out the test? Which testing approach will you use?

• Metrics: How will you know whether you have succeeded or failed? Set clear, minimum targets for your experiment. If the results meet or exceed these targets, you can consider the test a success.

Let’s go through each step to identify what to focus on when doing the Lean experiment!

(1) Critical Assumptions

When you experiment, you don’t want to test everything. The keywords are “cause” and “factor.” The assumptions that you write should be:

• About whether the Job-to-be-done/Pain point you chose is real and big enough

• About whether the main values of your solution address the JTBD/Pain

Critical assumptions are derived from the main problem statement and represent the key factors that must be tested during an experiment. The flow of logic should be:

The big Problem Statement => Assumptions A, B, C => Critical factors D, E, F

(2) Hypothesis of the Solution

To oversimplify, if the assumption is related to the problem, the hypothesis is related to the solution. A hypothesis is a strong statement developed from the assumptions (i.e., flipped to the other side of the assumption): 

• [Concept] Hypothesis: 

If (we do this….) then (this…would happen)

Or

(Doing this) leads to (that effect)

The hypothesis is the follow-up to the assumption, or the question of “if the assumption is correct, what is the outcome when we implement this action?” When working with a hypothesis, your job is to choose what is most likely to be right and test it out. Different from the scientific approach of trying to prove the hypothesis wrong, the hypothesis of a solution is about the success factors that will lead to the desired outcome. The “this… would happen” part for a startup often is “people use/buy the product.”

When the experiment turns out to have a negative result, there are usually two scenarios:

1. The assumption is wrong

2. Or the solution does not fit the problem

It is easy to read and remember all of this, but in reality, startups and firms have to test up to three-digit numbers of assumptions and hypotheses to find the right one. But what does it even mean to “test” an assumption/hypothesis?

(3) Planning: Prototype, Minimum Viable Product (MVP), and Testing Approaches

After writing down your assumption and hypothesis, it’s time to design the experiment. This is the part where you create the prototype, a small and simple form of your solution to be tested.

• Prototype: an early, preliminary version or model of a product or service created to test and validate its functionality, usability, and feasibility before full-scale production or implementation. By iterating on prototypes, teams can reduce costs, avoid costly mistakes, and increase the likelihood of creating a successful product that meets user needs and expectations.

• With a prototype, you conduct the test on internal or close ones, who are not exactly the target customers but are still valuable for getting initial reactions and insights. A prototype of the product could be a drawing sketch, a Lego model, a digital visualization, a live demo, etc. 

The ultimate goal when designing a prototype is to keep the continuous improvement cycle of the product. The keyword is “small”: a small product with just enough basic functions; small effort with low or no cost and minimal risk; small to test and quick to validate. 

After getting the results you want from testing the prototype, you move on to develop the mini product, or the Minimum Viable Product.

• Minimum Viable Product (MVP): the simplest version of your product that allows you to validate a critical assumption with real users. It includes only the essential features needed for early customers to use and provide feedback.

  • Minimum: Refers to the smallest set of features or effort necessary to create a product. It means avoiding any non-essential features and focusing only on what’s needed to test the core idea. This helps reduce development time, cost, and risk.

  • Viable: Means the product must be functional and usable enough to satisfy early customers’ basic needs. It should deliver real value and solve the core problem, even if it lacks polish or extra features. The product must be viable in the sense that users can meaningfully interact with it and provide feedback.

  • Product: Indicates that the MVP is a tangible offering, not just a concept or prototype, but something customers can experience, touch, and feel. It is a real product version, however minimal, that can be released to the market.

Different from the prototype, the MVP will be experimented with in the external environment, on real potential customers. It is easier to think of the MVP as the product's skeleton: it only has the bones but lacks the muscle and functions beyond the fundamental ones. For example, let’s look at the Skateboard-to-Car analogy:

The customer is not going to wait for you to build a full car, but the customer could give feedback on a bike. However, a bike is not an MVP for a car since it does not have the core values of a car to be a test for the future car.

The image below shows the five types of MVPs that you can choose from, each suited to different situations: 

For simplicity, here are 4 things you need to pay attention to when designing an MVP:

• Core Problem: Clearly define the specific problem your solution addresses.

• Target User: Identify who your solution is designed for.

• Core Feature(s): What's the absolute minimum functionality needed to deliver value?

• Visual Flow: Show a basic visual path of how a user would interact with it.

Finally, in the planning phase, you have to choose what type of experiment framework would fit your assumption, hypothesis, and MVP the most. For this article, CEI will recommend to you two frameworks for testing:

• A/B Testing: a method of comparing two versions of a product at the same time to see which one is better. You create a control version (A) and a variant (B), then show each to your potential customers with all the other factors equal except for the main differentiator between versions A and B. By analyzing user behavior and the quantitative results, you identify which version achieves better outcomes.

  • Example: You want to increase sign-ups on the landing page, so you test two versions of the call-to-action button: the original blue button and a larger, higher-contrast red one to see which gets more people to sign up.

• Time Phase Testing: evaluating a product or feature’s performance over different periods to observe changes, trends, or improvements. For each phase 0, 1, 2, …, you test the hypothesis at baseline, 1, 2, …, respectively.

Example: In Phase 1, you test if the content of the landing page affects the sign-up rate. In Phase 2, you test if the color of the landing page affects the sign-up rate. 

(4) Metrics of Measurement

Think of metrics as KPIs you want to reach in an experiment. Same with KPIs, you have to know specifically what type of number you want to set and measure, and choose the right ones to use as the benchmark in your feasible context. If the experiment produces numbers that reach the benchmark, that means your assumptions and hypothesis have a chance to be right (considering all the related factors that can affect the numbers). The keyword is “measurable.”

For example:

• Increase conversion rate from 10% to 12.5%.

• The Click-Through Rate (CTR) on the Call-to-Action (CTA) Button reaches 20%.

• After 1 week, the total sign-ups are 100 people.

Student Progress Highlights: Conducting Experiment

On the morning of Day 3, the students worked on drawing up their prototypes and went around testing whether other groups would use or buy their products. The session was lively and chaotic but filled with entrepreneurial spirit. Each team received a wealth of customer feedback and adjusted their prototypes based on real demands. Afterwards, the students created an MVP using Canva to test their ideas with potential customers. 

In the afternoon, each group was paired with a mentor whose expertise matched the theme they had chosen at the start of the program. This open session provided an opportunity for students to present their initial ideas, receive direct feedback, and troubleshoot practical challenges encountered during their research, observation, and solution development. The mentors the students met included:

• Mr. Tuấn Nguyễn – Founder & CEO of SEAMI, a music education platform serving learners in 12 countries.

• Ms. Nhật Tân – Founder & CEO of Seesaw Vietnam, who successfully raised 2 billion VND and secured two golden tickets, marking the fastest deal closed in Season 5 of Shark Tank Vietnam.

• Ms. Kimmi Ha – Founder of UPGREEN Vietnam, a business offering creative solutions using recycled materials.

• Mr. Ân Phan – Founder & CEO of WordsMine, part of the portfolios of Block71, Founder Institute, Startup Wheel, Qualcomm Vietnam Innovation Challenge, Google for Startups, Microsoft for Startups, and UNDP Youth Co:Lab. 

More than just mentoring, this session was a valuable opportunity for students to learn how to ask the right questions and listen to real entrepreneurial stories from their mentors. 

Reflection: Actionable Tips For You To Do 

Now that’s a long recap! For those who made it through, here is your desirable advice of the day:

• Rethinking the Solution: 90% of the time, the first solution you test will fail, and no one will buy your product. Remember, testing isn’t a one-time task; it needs to be repeated in a feedback loop.

• Assumption and Hypothesis: Break down the adjectives in your hypothesis/pain points one by one. Each adjective should be a side/factor that you need to consider separately, but also in parallel with each other. Which aspects do you think are not critical but become critical in the target customer’s context and beliefs?

• Planning: Your prototype and MVP are tied to what kind of testing you should use. Make sure to lower your expectations for both to simplify them as much as possible by reflecting on the time, resources, and even the abilities/skills needed to execute the testing by the people you work with.

• Metrics: Setting the right number for the benchmark is hard. Look at your competitors and scale it down significantly to have a realistic target to reach. Again, reflect on what you have! 

What’s Next? Teaser For Tomorrow! 

The team’s product is coming close to completion! Tomorrow, our students will learn about pitching and the skills to persuade the audience about their product.

👉 Stay tuned as we’ll share more reflections from FIB 2025 soon.

🔗 Curious? Drop us a message if you’d like to learn how to run your own innovation bootcamp or partner with us.