Eric Ries

1. Entrepreneurs are everywhere. You don’t have to work in a garage to be in a startup. The concept of entrepreneurship includes anyone who works within my definition of a startup: a human institution designed to create new products and services under conditions of extreme uncertainty. That means entrepreneurs are everywhere and the Lean Startup approach can work in any size company, even a very large enterprise, in any sector or industry.
2. Entrepreneurship is management. A startup is an institution, not just a product, and so it requires a new kind of management specifically geared to its context of extreme uncertainty. In fact, as I will argue later, I believe “entrepreneur” should be considered a job title in all modern companies that depend on innovation for their future growth.
3. Validated learning. Startups exist not just to make stuff, make money, or even serve customers. They exist to learn how to build a sustainable business. This learning can be validated scientifically by running frequent experiments that allow entrepreneurs to test each element of their vision.
4. Build-Measure-Learn. The fundamental activity of a startup is to turn ideas into products, measure how customers respond, and then learn whether to pivot or persevere. All successful startup processes should be geared to accelerate that feedback loop.
5. Innovation accounting. To improve entrepreneurial outcomes and hold innovators accountable, we need to focus on the boring stuff: how to measure progress, how to set up milestones, and how to prioritize work. This requires a new kind of accounting designed for startups—and the people who hold them accountabl

Startups also have a true north, a destination in mind: creating a thriving and world-changing business. I call that a startup’s vision. To achieve that vision, startups employ a strategy, which includes a business model, a product road map, a point of view about partners and competitors, and ideas about who the customer will be. The product is the end result of this strategy (see the chart on this page).

When an employee voluntarily invests their time and attention in this program, that is a strong indicator that they find it valuable

If that is true, the most important thing to measure is behavior: would the early participants actively spread the word to other employees

The point is not to find the average customer but to find early adopters: the customers who feel the need for the product most acutely. Those customers tend to be more forgiving of mistakes and are especially eager to give feedback

Until we could figure out how to sell and make the product, it wasn’t worth spending any engineering time on.”

To apply the scientific method to a startup, we need to identify which hypotheses to test. I call the riskiest elements of a startup’s plan, the parts on which everything depends, leap-of-faith assumptions. The two most important assumptions are the value hypothesis and the growth hypothesis. These give rise to tuning variables that control a startup’s engine of growth. Each iteration of a startup is an attempt to rev this engine to see if it will turn. Once it is running, the process repeats, shifting into higher and higher gear

There are several things to notice in this revised statement. First, it’s important to identify the facts clearly. Is it really true that progressive image loading caused the adoption of the World Wide Web, or was this just one factor among many? More important, is it really true that there are large numbers of potential customers out there who want our solution right now? The earlier analogy was designed to convince stakeholders that a reasonable first step is to build the new startup’s technology and see if customers will use it. The restated approach should make clear that what is needed is to do some empirical testing first: let’s make sure that there really are hungry customers out there eager to embrace our new technology.

If too much analysis is dangerous but none can lead to failure, how do entrepreneurs know when to stop analyzing and start building? The answer is a concept called the minimum viable product, the subject of Chapter 6

Minimum viable products range in complexity from extremely simple smoke tests (little more than an advertisement) to actual early prototypes complete with problems and missing features. Deciding exactly how complex an MVP needs to be cannot be done formulaically. It requires judgment. Luckily, this judgment is not difficult to develop: most entrepreneurs and product development people dramatically overestimate how many features are needed in an MVP. When in doubt, simplify