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

by Eric Ries

After all, it was my work over the prior months that needed...

When it came time to pivot and abandon that original strategy, almost all of my work—thousands of lines of code—was thrown out. I felt betrayed.

I had committed the biggest waste of all: building a product that our customers refused to use. That was really depressing.

I consoled myself that if we hadn’t built this first product—mistakes and all—we never would have learned these important insights about customers.

For a time, this “learning” consolation made me feel better, but my relief was short-lived. Here’s the question that bothered me most of all: if the goal of those months was to learn these important insights about customers, why did it take so long? How much of our effort contributed to the essential lessons we needed to learn? Could we have learned those lessons earlier if I hadn’t been so focused on making the product “better”

VALUE VS. WASTE

In other words, which of our efforts are value-creating and ...

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

in a startup, who the customer is and what the customer might find valuable are unknown,

realized that as a startup, we needed a new definition of value.

Anything we had done during those months that did not contribute to our learning was a form of waste. Would it have been possible to learn the same things with less effort? Clearly, the answer is yes.

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.

WHERE DO YOU FIND VALIDATION?

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.

Positive changes in metrics became the quantitative validation that our learning was real. This was critically important because we could show our stakeholders—employees, investors, and ourselves—that we were making genuine progress, not deluding ourselves.

THE AUDACITY OF ZERO

The irony is that it is often easier to raise money or acquire other resources when you have zero revenue, zero customers, and zero traction than when you have a small amount. Zero invites imagination, but small numbers invite questions about whether large numbers will ever materialize.

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.

This phenomenon creates a brutal incentive: postpone getting any data until you are certain of success. Of course, as we’ll see, such delays have the unfortunate effect of increasing the amount of wasted work, decreasing essential feedback, and dramatically increasing the risk that a startup will build something nobody wants.

we can mitigate the waste that happens because of the audacity of zero with validated learning.

LESSONS BEYOND IMVU

The Lean Startup is not a collection of individual tactics. It is a principled approach to new product development.

The question is not “Can this product 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?”

4 EXPERIMENT

struggling to answer the following questions: Which customer opinions should we listen to, if any? How should we prioritize across the many features we could build? Which features are essential to the product’s success and which are ancillary? What can be changed safely, and what might anger customers? What might please today’s customers at the expense of tomorrow’s? What should we work on next?

if the plan is to see what happens, a team is guaranteed to succeed—at seeing what happens—but won’t necessarily gain validated learning.

FROM ALCHEMY TO SCIENCE

The Lean Startup methodology reconceives a startup’s efforts as experiments that test its strategy to see which parts are brilliant and which are crazy.

Think Big, Start Small

Zappos is the world’s largest online shoe store, with annual gross sales in excess of $1 billion. It is known as one of the most successful, customer-friendly e-commerce businesses in the world, but it did not start that way.

he started by running an experiment. His hypothesis was that customers were ready and willing to buy shoes online. To test it, he began by asking local shoe stores if he could take pictures of their inventory. In exchange for permission to take the pictures, he would post the pictures online and come back to buy the shoes at full price if a customer bought them online.

In the course of testing this first assumption, many other assumptions were tested as well. To sell the shoes, Zappos had to interact with customers: taking payment, handling returns, and dealing with customer support. This is decidedly different from market research.

Zappos’ initial experiment provided a clear, quantifiable outcome: either a sufficient number of customers would buy the shoes or they would not.

This qualitative learning is a necessary companion to quantitative testing.

For Long-Term Change, Experiment Immediately

Here’s what it might look like if Caroline were to treat her project as an experiment.

Break It Down

The first step would be to break down the grand vision into its component parts. The two most important assumptions entrepreneurs make are what I call the ...

The value hypothesis tests whether a product or service really delivers value to customers once they are using it. What’s a good indicator that emp...

Experiments provide a more accurate gauge. What could we see in real time that would serve as a proxy for the value participants were gaining from volunteering?

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

For the growth hypothesis, which tests how new customers will discover a product or service, we can do a similar analysis. Once the program is up and running, how will it spread among the employees, from initial early adopters to mass adoption throughout the company?

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.

Next, using a technique I call the concierge minimum viable product

Caroline could make sure the first few participants had an experience that was as good as she could make it, completely aligned with her vision.

her goal would be to measure what the custome...

For example, if the growth model requires that a certain percentage of participants share their experiences with colleagues and encourage their participation, the degree to which that takes place can be tested even with a very small sample of people.

Remember that these are supposed to be the kinds of early adopters with the most to gain from the program. Put another way, what if all ten early adopters decline to volunteer again? That would be a highly significant—and very negative—result. If the numbers from such early experiments don’t look promising, there is clearly a problem with the strategy. That doesn’t mean it’s time to give up; on the contrary, it means it’s time to get some immediate qualitative feedback about how to improve the program.