MVP & No-Code Launch
Build and launch rapid prototypes to test product demand quickly
1 step
The Core Philosophy and Taxonomy of MVP
Define the fundamental purpose of an MVP and understand the transition from "Words to Actions" through the categorization of the six primary experiment types.
The first high-stakes problem a founder faces is the misconception that an MVP is a "low-quality" or "v1.0" version of their final vision. According to experts, you must reject the idea that you already know what the final product will be. An MVP is not a product; it is an experimental tool built to collect the maximum amount of validated learning with the least amount of effort and resources. As the curriculum explicitly states: "Truth is not in words—it is in deeds." You cannot rely on customer interviews alone because people are notoriously poor at predicting their own future behavior. To prove a business model is viable, you must move into the "Action Phase" where customers are forced to make a decision—usually a financial one—to validate your hypotheses.
An effective MVP should not take months to develop. If it does, you are already over-investing. The "minimum" in MVP refers to obtaining vital information at the lowest cost of time and capital. "Viability" means the experiment must provide enough value that the customer can actually evaluate the solution. Many startups fail because they focus on "Product Risk" (can we build it?), whereas the real danger lies in "Market Risk" (does anyone want it?). To solve this, you must choose an experiment that answers four critical questions:
Targeting: Can we find and reach the exact customer segment that has this pain?
Willingness to Pay: Are they ready to put "skin in the game" for this value proposition?
Value Measurement: How exactly do they perceive and measure the utility of what we are offering?
Pricing Model: Does our revenue logic match the customer’s readiness to pay?
The materials identify six distinct taxonomies for these experiments: the Pre-order MVP, the Audience-Building MVP, the Concierge MVP, the Wizard of Oz MVP, the Single-Feature MVP, and the Other’s Product MVP. Choosing the right one depends on whether your biggest risk is distribution, complex technology, or pricing logic.
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Commitment-Based Validation
Use high-intent experiments to force potential customers to take on obligations or join a community before you invest in development.
The Pre-order MVP is described as the most universal tool in your arsenal. Its primary goal is to convince potential consumers to order a product before it is even developed. This is not about "gauging interest" or collecting emails; it is about establishing a formal commitment. Experts suggest using a landing page to describe the problem and the proposed solution, and then asking for a credit card number. While you promise not to charge the card until the product is ready, the act of a customer providing their data is the ultimate proof of desirability. A prime example is the Finale Fireworks case. The founders had a pyrotechnic game but realized through a simple demo at a convention that professionals didn't want virtual games—they wanted a tool to manage real shows. They sold 60 pre-orders with a 50% discount for a product they hadn't even finished writing. This volume of sales was the only evidence needed to justify the full development budget.
A variation of this is the Audience-Building (Customer-Forming) MVP. This model focuses on building a base of loyal users through content and community before the product is released. By creating a hub—like the MintLife forum for Mint.com—you find where your customers gather to exchange ideas. This allows you to observe which features they talk about most, effectively conducting customer development at scale. You are "developing the consumer" in the literal sense.
Consider the Product Hunt case study. Founder Ryan Hoover didn't start with a complex ranking site. He spent just 20 minutes setting up a "Linkydink" group—a simple email service for sharing links. He invited a few friends to contribute and monitored the engagement. When he saw that the activity and enthusiasm grew rapidly, eventually attracting 4,000 users in two months, he knew he had a market. Only then did he begin developing the actual platform. This "minimal" approach ensures that you never build a "ghost town." You launch into an already active market of loyal users who have already validated the core concept.
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Manual Value Proof
Solve customer problems manually to learn the internal logic of your business model before attempting to automate it with software.
When your product requires complex algorithms or high-cost infrastructure, you should start with the Concierge MVP. Like a hotel concierge, you work with each customer personally, "by hand" helping them solve their problems. This is a high-touch, labor-intensive approach where you spend hours a week on a single customer. It is not meant to scale; it is meant to provide you with the "Experience of the Solution." For example, StyleSeat founders didn't build an automated booking system for salon owners first. Instead, they held weekly meetings and manually taught stylists how to use existing tools like Facebook and email to grow their businesses. Through this "individual approach," they discovered that stylists weren't just looking for a booking app—they needed a full-service business growth partner. This deep immersion allowed them to win customer trust and refine their value proposition before a single line of code was written.
The Wizard of Oz MVP (often called "Flinstonization" or "Manualization") differs because the user believes they are interacting with a finished, automated product. In reality, the "backend" is a human manually performing the tasks. This is an exceptional tool for testing behavior in the field without the engineering cost of automation. Porch.com (originally HelpScore) used this to validate a homeowner-to-contractor scoring system. They created a website with an "algorithm," but the scores were actually calculated by employees who manually researched contractors and wrote reports. They then watched how homeowners interacted with the data. They quickly discovered their hypothesis was wrong: users didn't care about a numerical score; they wanted recommendations from friends. Because the system was manual, Porch was able to pivot their entire model in two months. Had they built the automated scoring system first, the cost of that failure would have been ten times higher.
4 step
Post-MVP Logic
Analyze the evidence from your experiments to determine your next strategic move and avoid the trap of "perfect" development.
The final business problem in the MVP cycle is knowing when the "search" is over. According to the curriculum, you can never definitively "prove" your product will be a global success, but you can continuously clarify the path. The goal is to "check your course using reliable data" so you can move your resources in the right direction. Once your MVP has been tested by real users, you must return to your original hypotheses. Did they pay? Did they use the manual tool? Did they complain about the lack of sorting?
The most valuable signal from an MVP is often negative feedback. If users are complaining, it means they are trying to use the product and want it to work better—this is a strong indicator of desirability. If they are silent, or if they use it once and never return, you cannot solve that problem by "adding more features." It usually means your value proposition is not acute enough, or your target audience is wrong. You must then face the Pivot/Persevere decision:
Persevere: If your metrics (like the 100/3/10 rule) are hit, you continue to enhance and automate your MVP.
Pivot: If the evidence refutes your hypothesis (e.g., homeowners didn't care about scores), you change one or more elements of your business model and start a new experiment loop.
Kill: If the profit potential is insufficient or the problem is non-existent, you must be professional enough to stop and restart.
Remember, the goal of this unit is to create a validated business model, not just a working piece of software. As Kevin Dewalt states, a Genius decision is made on available information, not on an "ideal" feature set. Stay in the Discovery and Test stages until the evidence is "Strong"—meaning you have payment info, observed behavior in the real world, and a clear understanding of the "why" behind user actions. This disciplined approach ensures that when you finally enter the Scale stage, you are scaling a proven machine, not a hallucination.
Expected Results
Improve Trial Conversion Rate and increase Total Users by 20% through the rapid deployment of functional, action-based experiments.
Deliverables
A document identifying the top 3 assumptions (e.g., pricing, distribution) that your MVP must prove.
A live link to a landing page or form designed to capture credit card numbers or signed Letters of Intent (LOI)
A spreadsheet or process map showing exactly how your team will handle the first 5 transactions "by hand."