brainstorm-experiments-new

Lean Startup Experiment Design Assistant – A Fast Method for New Product Validation

Skill Overview

brainstorm-experiments-new helps entrepreneurs and product managers quickly validate new product ideas using Lean Startup methodology. By designing XYZ hypotheses and pretotype experiments, it tests market demand at minimal cost and avoids investing resources in products nobody needs.

Applicable Scenarios

New Product Concept Validation

When you have a product idea but are unsure whether the market needs it, use this skill to design low-cost experiments to validate your assumptions—for example, testing users’ willingness to sign up through a landing page or their genuine willingness to pay through a pre-order page.

Limited Resources in the Early Startup Stage

When you have no development team or a limited budget, use pretotype methods such as explainer videos, email marketing, or manual services to quickly collect real user data instead of immediately investing in developing a complete product.

Comparative Testing of Multiple Options

When you need to choose among multiple product directions, you can design and run several small experiments in parallel. Use actual user behavior data—such as click-through rates, sign-ups, and pre-orders—to determine which idea is most worth pursuing.

Core Features

XYZ Hypothesis Generator

Helps you create structured validation hypotheses in the format: “At least X% of the Y group will take action Z.” For example: “At least 5% of small and medium-sized business owners who visit the landing page will leave their email addresses to request a trial.” Clear hypotheses make validation results measurable and actionable.

Pretotype Experiment Design

Recommends the most suitable experimental method based on your product type and available resources: landing page tests to attract traffic, explainer videos to test comprehension, email marketing to test demand, pre-orders/waitlists to test willingness to pay, and manual MVPs to test core value. Each experiment includes validation metrics and success thresholds.

Lean Validation Principles

Based on Alberto Savoia’s “Skin in the Game” principle and the YODA (Your Own Data and Analysis) method, this ensures that you measure real user behavior rather than opinions. It validates whether users are willing to invest time, money, or reputation—not merely express interest.

Frequently Asked Questions

What is the difference between a pretotype and an MVP? Which should I use first?

A pretotype (pretended prototype) is earlier-stage and lower-cost than an MVP (minimum viable product). A pretotype is made to look like a real product to test user reactions, but it may be operated manually behind the scenes. An MVP, by contrast, is a simplified product that can run automatically. It is recommended that you use a pretotype first to quickly validate whether an idea is worth pursuing, and then develop an MVP after validation succeeds. For example, to test the idea of an “online course,” you could first use a pretotype involving manually sending PDFs to validate demand, and then develop a course platform after validation.

How do I determine whether a product validation experiment is successful?

Each experiment needs a clear success threshold, usually based on your XYZ hypothesis. For example, if your hypothesis is that “at least 3% of visitors will sign up,” but the actual result is 2.8%, the experiment did not meet the threshold. It is recommended that you establish decision rules in advance: meet or exceed the threshold → continue; approach the threshold (80% or more) → refine the hypothesis and retest; fall significantly below the threshold → abandon or pivot. Do not continue investing simply because “the data looks fairly good.” Respect the experiment results.

What is the “Skin in the Game” principle? Why is it important?

The “Skin in the Game” principle, from Alberto Savoia’s The Right It, emphasizes that only actions in which users are willing to invest real costs—time, money, or reputation—are reliable market signals. For example, “I would buy it” is merely an opinion, whereas “I will prepay 50 yuan” is a commitment. When validating a product, design experiments that measure real commitment—such as pre-orders, waitlists, or having users invest time to provide detailed requirements—instead of relying on surveys or user interviews. Other people’s data (ODP) cannot predict whether your product will succeed; you must collect your own data (YODA).