The Lean Startup: How Today's Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses

by Eric Ries

Startup success is not a consequence of good genes or being in the right place at the right time. Startup success can be engineered by following the right process, which means it can be learned, which means it can be taught.

Building a startup is an exercise in institution building; thus, it necessarily involves management. This often comes as a surprise to aspiring entrepreneurs, because their associations with these two words are so diametrically opposed. Entrepreneurs are rightly wary of implementing traditional management practices early on in a startup, afraid that they will invite bureaucracy or stifle creativity.

The goal of a startup is to figure out the right thing to build—the thing customers want and will pay for—as quickly as possible. In other words, the Lean Startup is a new way of looking at the development of innovative new products that emphasizes fast iteration and customer insight, a huge vision, and great ambition, all at the same time.

Unfortunately, too many startup business plans look more like they are planning to launch a rocket ship than drive a car. They prescribe the steps to take and the results to expect in excruciating detail, and as in planning to launch a rocket, they are set up in such a way that even tiny errors in assumptions can lead to catastrophic outcomes.

The Lean Startup method, in contrast, is designed to teach you how to drive a startup. Instead of making complex plans that are based on a lot of assumptions, you can make constant adjustments with a steering wheel called the Build-Measure-Learn feedback loop.

Products change constantly through the process of optimization, what I call tuning the engine. Less frequently, the strategy may have to change (called a pivot). However, the overarching vision rarely changes.

A startup is a human institution designed to create a new product or service under conditions of extreme uncertainty.

We often lose sight of the fact that a startup is not just about a product, a technological breakthrough, or even a brilliant idea. A startup is greater than the sum of its parts; it is an acutely human enterprise.

Startups are designed to confront situations of extreme uncertainty. To open up a new business that is an exact clone of an existing business all the way down to the business model, pricing, target customer, and product may be an attractive economic investment, but it is not a startup because its success depends only on execution—so much so that this success can be modeled with high accuracy. (This is why so many small businesses can be financed with simple bank loans; the level of risk and uncertainty is understood well enough that a loan officer can assess its prospects.)

In fact, I believe a company’s only sustainable path to long-term economic growth is to build an “innovation factory” that uses Lean Startup techniques to create disruptive innovations on a continuous basis.

Yet if the fundamental goal of entrepreneurship is to engage in organization building under conditions of extreme uncertainty, its most vital function is learning. We must learn the truth about which elements of our strategy are working to realize our vision and which are just crazy. We must learn what customers really want, not what they say they want or what we think they should want. We must discover whether we are on a path that will lead to growing a sustainable business.

Validated learning is the process of demonstrating empirically that a team has discovered valuable truths about a startup’s present and future business prospects.

I wish I could say that I was the one to realize our mistake and suggest the solution, but in truth, I was the last to admit the problem. In short, our entire strategic analysis of the market was utterly wrong. We figured this out empirically, through experimentation, rather than through focus groups or market research. Customers could not tell us what they wanted; most, after all, had never heard of 3D avatars. Instead, they revealed the truth through their action or inaction as we struggled to make the product better.

Empirical demonstration > market research

I was a devotee of the latest in software development methods (known collectively as agile development), which promised to help drive waste out of product development. However, despite that, I had committed the biggest waste of all: building a product that our customers refused to use. That was really depressing.

In other words, which of our efforts are value-creating and which are wasteful? This question is at the heart of the lean manufacturing revolution; it is the first question any lean manufacturing adherent is trained to ask.

Lean thinking defines value as providing benefit to the customer; anything else is waste.

In a manufacturing business, customers don’t care how the product is assembled, only that it works correctly. But in a startup, who the customer is and what the customer might find valuable are unknown, part of the very uncertainty that is an essential part of the definition of a startup.

Here’s the thought that kept me up nights: did we have to support any networks at all? Is it possible that we could have discovered how flawed our assumptions were without building anything? For example, what if we simply had offered customers the opportunity to download the product from us solely on the basis of its proposed features before building anything?

I’ve come to believe that learning is the essential unit of progress for startups. The effort that is not absolutely necessary for learning what customers want can be eliminated. I call this validated learning because it is always demonstrated by positive improvements in the startup’s core metrics. As we’ve seen, it’s easy to kid yourself about what you think customers want. It’s also easy to learn things that are completely irrelevant. Thus, validated learning is backed up by empirical data collected from real customers.

We adopted the view that our job was to find a synthesis between our vision and what customers would accept; it wasn’t to capitulate to what customers thought they wanted or to tell customers what they ought to want.

It is also the right way to think about productivity in a startup: not in terms of how much stuff we are building but in terms of how much validated learning we’re getting for our efforts.

Whenever we would change the product to make it easier for people to find and keep new friends, we discovered that customers were more likely to engage. This is true startup productivity: systematically figuring out the right things to build.

Very important

As long as nothing has been released and no data have been collected, it is still possible to imagine overnight success in the future. Small numbers pour cold water on that hope.

Don't be optimistic. Be realistic. Use empirical data and no vanity metrics (e.g. total users)

The question is not “Can this product be built?” In the modern economy, almost any product that can be imagined can be built. The more pertinent questions are “Should this product be built?” and “Can we build a sustainable business around this set of products and services?” To answer those questions, we need a method for systematically breaking down a business plan into its component parts and testing each part empirically.

The value hypothesis tests whether a product or service really delivers value to customers once they are using it.

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.

Mark explained, “Traditionally, the product manager says, ‘I just want this.’ In response, the engineer says, ‘I’m going to build it.’ Instead, I try to push my team to first answer four questions: 1. Do consumers recognize that they have the problem you are trying to solve? 2. If there was a solution, would they buy it? 3. Would they buy it from us? 4. Can we build a solution for that problem?”

Knowing they weren’t ready to launch, Cook held off his division’s general manager by explaining how iterating and experimenting before beginning the marketing campaign would yield far better results.

First figure out what they want before spending all your money on marketing.

“Success is not delivering a feature; success is learning how to solve the customer’s problem.”

As we saw in Part One, the products a startup builds are really experiments; the learning about how to build a sustainable business is the outcome of those experiments. For startups, that information is much more important than dollars, awards, or mentions in the press, because it can influence and reshape the next set of ideas.

Finally, and most important, there’s the pivot. Upon completing the Build-Measure-Learn loop, we confront the most difficult question any entrepreneur faces: whether to pivot the original strategy or persevere. If we’ve discovered that one of our hypotheses is false, it is time to make a major change to a new strategic hypothesis.

By all accounts, what impressed investors the most were two facts about Facebook’s early growth. The first fact was the raw amount of time Facebook’s active users spent on the site. More than half of the users came back to the site every single day.2 This is an example of how a company can validate its value hypothesis—that customers find the product valuable.

Example of actual tracking of customer behaviour

What differentiates the success stories from the failures is that the successful entrepreneurs had the foresight, the ability, and the tools to discover which parts of their plans were working brilliantly and which were misguided, and adapt their strategies accordingly.

The importance of basing strategic decisions on firsthand understanding of customers is one of the core principles that underlies the Toyota Production System. At Toyota, this goes by the Japanese term genchi gembutsu, which is one of the most important phrases in the lean manufacturing vocabulary. In English, it is usually translated as a directive to “go and see for yourself” so that business decisions can be based on deep firsthand knowledge.

Startups need extensive contact with potential customers to understand them, so get out of your chair and get to know them.

With that understanding, we can craft a customer archetype, a brief document that seeks to humanize the proposed target customer. This archetype is an essential guide for product development and ensures that the daily prioritization decisions that every product team must make are aligned with the customer to whom the company aims to appeal.

The problem with most entrepreneurs’ plans is generally not that they don’t follow sound strategic principles but that the facts upon which they are based are wrong. Unfortunately, most of these errors cannot be detected at the whiteboard because they depend on the subtle interactions between products and customers.

Unlike a prototype or concept test, an MVP is designed not just to answer product design or technical questions. Its goal is to test fundamental business hypotheses.

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.

Most entrepreneurs approach a question like this by building the product and then checking to see how customers react to it. I consider this to be exactly backward because it can lead to a lot of waste. First, if it turns out that we’re building something nobody wants, the whole exercise will be an avoidable expense of time and money. If customers won’t sign up for the free trial, they’ll never get to experience the amazing features that await them.

To avoid the risk of waking up after years of development with a product nobody wanted, Drew did something unexpectedly easy: he made a video.

Dropbox

In a concierge MVP, this personalized service is not the product but a learning activity designed to test the leap-of-faith assumptions in the company’s growth model. In fact, a common outcome of a concierge MVP is to invalidate the company’s proposed growth model, making it clear that a different approach is needed.

If we do not know who the customer is, we do not know what quality is.

Customers don’t care how much time something takes to build. They care only if it serves their needs.

As you consider building your own minimum viable product, let this simple rule suffice: remove any feature, process, or effort that does not contribute directly to the learning you seek.

You have to commit to a locked-in agreement—ahead of time—that no matter what comes of testing the MVP, you will not give up hope. Successful entrepreneurs do not give up at the first sign of trouble, nor do they persevere the plane right into the ground. Instead, they possess a unique combination of perseverance and flexibility.

We all need a disciplined, systematic approach to figuring out if we’re making progress and discovering if we’re actually achieving validated learning.

Second, startups must attempt to tune the engine from the baseline toward the ideal. This may take many attempts. After the startup has made all the micro changes and product optimizations it can to move its baseline toward the ideal, the company reaches a decision point. That is the third step: pivot or persevere.

Alternatively, a startup might prefer to build separate MVPs that are aimed at getting feedback on one assumption at a time.

Compare two startups. The first company sets out with a clear baseline metric, a hypothesis about what will improve that metric, and a set of experiments designed to test that hypothesis. The second team sits around debating what would improve the product, implements several of those changes at once, and celebrates if there is any positive increase in any of the numbers. Which startup is more likely to be doing effective work and achieving lasting results?