Do not read this book straight through. There is no plot or narrative that you’re going to miss.
This book is not a Step 1, Step 2, Step 3 guide to building a startup. Startups don’t work like that.
Start by reading the intro and index to figure out which parts of the book are relevant to you and your startup. Read the relevant chapters, then reference the methods described in the remaining pages as needed.
Think of this book as a toolbox.
It’s organized to help us find what we’re looking for when we need it. When we need a way to test market demand, there’s a section on evaluative market experiments. When we’re looking to prioritize our ever-growing feature list into a minimum viable product, there’s a section on generative product research.
We must know which tool we need before taking it out and using it. As innovators, we must be wary of Maslow’s hammer and understand which research/experimental method or technique we need so that we don't end up repeatedly hitting ourselves in the thumb.
The index for navigating this book is not alphabetical, chronological, or ontological. The index is ordered by what you’re trying to learn. Are you trying to learn about your customer? How to price your product? What will make your users come back?
It is highly recommended that you thoroughly read the index. You will not get the major benefit of this book without it.
When faced with an unknown aspect of your business model, first figure out what you need to learn. What’s your learning goal? What question are you trying to ask?
Once you know what you need to learn, use the index to find a list of relevant research and experimental methods. Then read each method and determine which will work best for your situation and resources.
In each method section, you will find the following headers:
A quick 2-3 sentence description of the method.
A list of common questions about a business model that this method helps answer.
A list of terms that can be used to navigate the book, such as B2B (for methods commonly used for business-to-business models) and qualitative (for the type of data used by this method).
A more detailed description of the steps normally taken to run a research or experiment method, including:
Time commitment and resources needed to run the method
How to run the method
Interpreting results in a meaningful way
Common biases or pitfalls that may distort the results of the method and lead to bad conclusions based on incorrect data
Field tips from startup practitioners who have used this method
Links to various case studies that serve as examples and inspiration.
A list of additional materials or resources for those who want more information.