The Lean Startup: The Million Copy Bestseller Driving Entrepreneurs to Success

by Eric Ries

Even after ditching the IM add-on strategy, it still took months to understand why it hadn’t worked. After our pivot and many failed experiments, we finally figured out this insight: customers wanted to use IMVU to make new friends online. Our customers intuitively grasped something that we were slow to realize. All the existing social products online were centered on customers’ real-life identity. IMVU’s avatar technology, however, was uniquely well suited to help people get to know each other online without compromising safety or opening themselves up to identity theft. Once we formed this hypothesis, our experiments became much more likely to produce ...

This is true startup productivity: systematically figuring out the r...

These were just a few experiments among hundreds that we ran week in and week out as we started to learn which customers would use the product and why. Each bit of knowledge we gathered suggested new experiments to r...

Every time I teach the IMVU story, students have an overwhelming temptation to focus on the tactics it illustrates: launching a low-quality early prototype, charging customers from day one, and using low-volume revenue targets as a way to drive accountability. These are useful techniques, but they are not the moral of the story. There are too many exceptions. Not every kind of customer will accept a low-quality prototype, for example.

The tactics from the IMVU story may or may not make sense in your particular business.

Instead, the way forward is to learn to see every startup in any industry as a grand experiment. 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.

In other words, we need the scientific method. In the Lean Startup model, every product, every feature, every marketing campaign—everything a startup does—is understood to be an experiment designed to achieve validated learning. This experimental appro...

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. A true experiment follows the scientific method. It begins with a clear hypothesis that makes predictions about what is supposed to happen. It then tests those predictions empirically. Just as scientific experimentation is informed by theory, startup experimentation is guided by the startup’s vision. The goal of every startup experiment is to discover how to build a sustainable business around that vision.

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. Founder Nick Swinmurn was frustrated because there was no central online site with a great selection of shoes. He envisioned a new and superior retail experience. Swinmurn could have waited a long time, insisting on testing his complete vision complete with warehouses, distribution partners, and the promise of significant sales. Many early e-commerce pioneers did just that, including infamous dot-com failures such as Webvan and Pets.com.

Instead, 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. Zappos began with a tiny, simple product. It was designed to ans...

Zappos’ initial experiment provided a clear, quantifiable outcome: either a sufficient number of customers would buy the shoes or they would not. It also put the company in a position to observe, interact with, and learn from real customers and partners. This qualitative learning is a necessary companion to quantitative testing. Although the early efforts were decidedly small-scale, that did not prevent the huge Zappos vision from being realized. In fact, in 2009 Zappos was acquired by the e-commerce giant Amazon.com for a reported $1.2 billion.2

By starting small, Caroline could prevent a tremendous amount of waste down the road without compromising her overall vision. 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 value hypothesis and the growth hypothesis. 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 employees find donating their time valuable? We could survey them to get their opinion, but that would not be very accurate because most people have a hard time assessing their feelings objectively. 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? We could find opportunities for a small number of employees to volunteer and then look at the retention rate of those employees. How many of them sign up to volunteer again? 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? A likely way this program could expand is through viral growth. If that is true, the most important thing to measure is behavior: would the early participants actively spread the word to other employees? In this case, a simple experiment would involve t...

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.

Next, using a technique I call the concierge minimum viable product (described in detail in Chapter 6), 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. Unlike in a focus group, her goal would be to measure what the customers actually did. For example, how many of the first volunteers actually complete their volunteer assignments? How many volunteer a second time? How many are willing to recruit a colleague to participate in a subsequent volunteer activity?

If ten people complete the first experiment, how many do we expect to volunteer again? If they are asked to recruit a colleague, how many do we expect will do so? 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.

This entire experiment could be conducted in a matter of weeks, less than one-tenth the time of the traditional strategic planning process. Also, it can happen in parallel with strategic planning while the plan is still being formulated. Even when experiments produce a negative result, those failures prove instructive and can influence the strategy.

AN EXPERIMENT IS A PRODUCT In the Lean Startup model, an experiment is more than just a theoretical inquiry; it is also a first product. If this or any other experiment is successful, it allows the manager to get started with his or her campaign: enlisting early adopters, adding employees to each further experiment or iteration, and eventually starting to build a product. By the time that product is ready to be distributed widely, it will already have established customers. It will have solved real problems and offer detailed specifications for what needs to be built. Unlike a traditional strategic planning or market research process, this specification will be rooted in feedback on what is working today rather than in anticipation of what might work tomorrow.

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: Do consumers recognize that they have the problem you are trying to solve? If there was a solution, would they buy it? Would they buy it from us? Can we build a solution for that problem?”

The common tendency of product development is to skip straight to the fourth question and build a solution before confirming that customers have the problem.

Those negative results demoralized the team. The usability problems frustrated them, as did customer complaints about missing features, many of which matched the original road map. Cook explained that even though the product was missing features, the project was not a failure. The initial product—flaws and all—confirmed that users did have the desire to create event albums, which was extremely valuable information. Where customers complained about missing features, this suggested that the team was on the right track. The team now had early evidence that those features were in fact important. What about features that were on the road map but that customers didn’t complain about? Maybe those features weren’t as important as they initially seemed.

Through a beta launch the team continued to learn and iterate. While the early users were enthusiastic and the numbers were promising, the team made a major discovery. Through the use of online surveying tool KISSinsights, the team learned that many customers wanted to be able to arrange the order of pictures before they would invite others to contribute. 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. In a world where marketing launch dates were often set months in advance, waiting until the team had really solved the problem was a break from the past.

This process represented a dramatic change for Kodak Gallery; employees were used to being measured on their progress at completing tasks. As Cook says, “Success is not delivering a feature; succe...

THE VILLAGE LAUNDRY SERVICE In India, due to the cost of a washing machine, less than seven percent of the population have one in their homes. Most people either hand wash their clothing at home or pay a Dhobi to do it for them. Dhobis take the clothes to the nearest river, wash them in the river water, bang them against rocks to get them clean, and hang them to dry, which takes two to seven days. The result? Clothes are returned in about ten days and are probably not that clean. Akshay Mehra had been working at Procter & Gamble Singapore for eight years when he sensed an opportunity. As the brand manager of the Tide and Pantene brands for India and ASEAN countries, he thought he could make laundry services available to people who previously could not afford them. Returning to India, Akshay joined the Village Laundry Services (VLS), created by Innosight Ventures. VLS began a series of experiments to test its business assumptions. For their first experiment, VLS mounted a consumer-grade laundry machine on the back of a pickup truck parked on a street corner in Bangalore. The experiment cost less than $8,000 and had th...

VLS also experimented with parking the carts in front of a local minimarket chain. Further iterations helped VLS figure out which services people were most interested in and what price they were willing to pay. They discovered that customers often wanted their clothes ironed and were willing to pay double the price to get their laundry back in four hours rather than twenty-four hours.

As a result of those early experiments, VLS created an end product that was a three-foot by four-foot mobile kiosk that included an energy-efficient, consumer-grade washing machine, a dryer, and an extra-long extension cord. The kiosk used Western detergents and was supplied daily with fresh clean water delivered by VLS.

Since then, the Village Laundry Service has grown substantially, with fourteen locations operational in Bangalore, Mysore, and Mumbai. As CEO Akshay Mehra shared with me, “We have serviced 116,000 kgs. in 2010 (vs. 30,600 kg. in 2009). And almost 60 percent of the business is coming from repeat customers. We have s...

My suggestion was drawn straight from the principles of this chapter: treat the CFPB as an experiment, identify the elements of the plan that are assumptions rather than facts, and figure out ways to test them. Using these insights, we could build a minimum viable product and have the agency up and running—on a micro scale—long before the official plan was set in motion.

The number one assumption underlying the current plan is that once Americans know they can call the CFPB for help with financial fraud and abuse, there will be a significant volume of citizens who do that. This sounds reasonable, as it is based on market research about the amount of fraud that affects Americans each year. However, despite all that research, it is still an assumption. If the actual call volume differs markedly from that in the plan, it will require significant revision. What if Americans who are subjected to financial abuse don’t view themselves as victims and therefore don’t seek help? What if they have very different notions of what problems are important? What if they call the agency seeking help for problems that are outside its purview? Once the agency is up and running with a $500 million budget and a correspondingly large staff, altering the plan will be expensive and time-consuming, but why wait to get feedback? To start experimenting immediately, the agency could start with the creation of a simple hotline number, using one of the new breed of low-cost and fast setup platforms such as Twilio. With a few hours’ work, they could add simple voice prompts, offering callers a menu of financial problems to choose from. In the first version, the prompts could be drawn straight from the existing research. Instead of a caseworker on the line, each prompt could offer the caller useful information about how to solve her or his problem. Instead of marketing th...

As a comprehensive solution to the problem of financial abuse, this minimum viable product is not very good compared with what a $500 million agency could accomplish. But it is also not very expensive. This product could be built in a matter of days or weeks, and...

What we would learn from this experiment would be invaluable. On the basis of the selections of those first callers, the agency could immediately start to get a sense of what kinds of problems Americans believe they have, not just what they “should” have. The agency could begin to test marketing messages: What motivates people to call? It could start to extrapolate real-world trends: What percentage of people in the target area actually call? The extrapolatio...

At its heart, a startup is a catalyst that transforms ideas into products. As customers interact with those products, they generate feedback and data. The feedback is both qualitative (such as what they like and don’t like) and quantitative (such as how many people use it and find it valuable). 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.

Minimize TOTAL time through the loop This Build-Measure-Learn feedback loop is at the core of the Lean Startup model.

Some managers are experts at strategizing and learning at the whiteboard. Plenty of entrepreneurs focus their energies on the individual nouns: having the best product idea or the best-designed initial product or obsessing over data and metrics.

The truth is that none of these activities by itself is of paramount importance. Instead, we need to focus our energies on minimizing the total time through this feedback loop.

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 gears.

Once clear on these leap-of-faith assumptions, the first step is to enter the Build phase as quickly as possible with a minimum viable product (MVP). The MVP is that version of the product that enables a full turn of the Build-Measure-Learn loop with a minimum amount of effort and the least amount of development time.

We also need to get it in front of potential customers to gauge their reactions. We may even need to try selling them the prototype, as we’ll soon see.

When we enter the Measure phase, the biggest challenge will be determining whether the product development efforts are leading to real progress. Remember, if we’re building something that nobody wants, it doesn’t much matter if we’re doing it on time and on budget. The method I recommend is called innovation accounting, a quantitative approach that allows us to see whether our engine-tuning efforts are bearing fruit.

It also allows us to create learning milestones, which are an alternative to traditional business and product milestones. Learning milestones are useful for entrepreneurs as a way of assessing their progress accurately and objectively; they are also invaluable to managers and investors who must hold entrepreneurs accountable. However, not all metrics are created equal, and in Chapter 7 I’ll clarify the danger of vanity metrics in contrast to the nuts-and-bol...

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...

If we’ve discovered that one of our hypotheses is false, it is time to make a major change to a new strategic hypothesis. The Lean Startup method builds capital-efficient companies because it allows startups to recognize that it’...

Many things about it are remarkable, but I’d like to focus on only one: how Facebook was able to raise so much money when its actual usage was so small.1 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. The second impressive thing about Facebook’s early traction was the rate at which it had taken over its first few college campuses. The rate of growth was staggering:

Facebook launched on February 4, 2004, and by the end of that month almost three-quarters of Harvard’s undergraduates were using it, without a dollar of marketing or advertising having been spent.

Facebook was different, because it employed a different engine of growth. It paid nothing for customer acquisition, and its high engagement meant that it was accumulating massive amounts of customer attention every day.

What traditional business strategy excels at is helping managers identify clearly what assumptions are being made in a particular business. The first challenge for an entrepreneur is to build an organization that can test these assumptions systematically. The second challenge, as in all entrepreneurial situations, is to perform that rigorous testing without losing sight of the company’s overall vision.

we assume that customers have a significant desire to use a product like ours, or we assume that supermarkets will carry our product. Acting as if these assumptions are true is a classic entrepreneur superpower. They are called leaps of faith precisely because the success of the entire venture rests on them. If they are true, tremendous opportunity awaits. If they are false, the startup risks total failure.

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.