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

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

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Highlights & Notes

I have seen firsthand how often a promising start leads to failure. The grim reality is that most startups fail. Most new products are not successful. Most new ventures do not live up to their potential.

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.

  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 accountable.

Startups do not yet know who their customer is or what their product should be. As the world becomes more uncertain, it gets harder and harder to predict the future. The old management methods are not up to the task. Planning and forecasting are only accurate when based on a long, stable operating history and a relatively static environment. Startups have neither.

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.

Every new version of a product, every new feature, and every new marketing program is an attempt to improve this engine of growth. Like Henry Ford’s tinkering in his garage, not all of these changes turn out to be improvements. New product development happens in fits and starts. Much of the time in a startup’s life is spent tuning the engine by making improvements in product, marketing, or operations.

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.

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. Through this process of steering, we can learn when and if it’s time to make a sharp turn called a pivot or whether we should persevere along our current path. Once we have an engine that’s revved up, the Lean Startup offers methods to scale and grow the business with maximum acceleration.

The challenge of entrepreneurship is to balance all these activities. Even the smallest startup faces the challenge of supporting existing customers while trying to innovate. Even the most established company faces the imperative to invest in innovation lest it become obsolete. As companies grow, what changes is the mix of these activities in the company’s portfolio of work.

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

Usually, companies like Intuit fall into the trap described in Clayton Christensten’s The Innovator’s Dilemma: they are very good at creating incremental improvements to existing products and serving existing customers, which Christensen called sustaining innovation, but struggle to create breakthrough new products—disruptive innovation—that can create new sustainable sources of growth.

Innovation is a bottoms-up, decentralized, and unpredictable thing, but that doesn’t mean it cannot be managed. It can, but to do so requires a new management discipline, one that needs to be mastered not just by practicing entrepreneurs seeking to build the next big thing but also by the people who support them, nurture them, and hold them accountable.

The amount of time a company can count on holding on to market leadership to exploit its earlier innovations is shrinking, and this creates an imperative for even the most entrenched companies to invest in innovation.

Brad explained to me how they hold themselves accountable for their new innovation efforts by measuring two things: the number of customers using products that didn’t exist three years ago and the percentage of revenue coming from offerings that did not exist three years ago.

Leadership requires creating conditions that enable employees to do the kinds of experimentation that entrepreneurship requires.

Unfortunately, “learning” is the oldest excuse in the book for a failure of execution. It’s what managers fall back on when they fail to achieve the results we promised. Entrepreneurs, under pressure to succeed, are wildly creative when it comes to demonstrating what we have learned. We can all tell a good story when our job, career, or reputation depends on it.

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. It is more concrete, more accurate, and faster than market forecasting or classical business planning. It is the principal antidote to the lethal problem of achieving failure: successfully executing a plan that leads nowhere.

The reason for that wisdom is simple. Because of the power of network effects, IM products have high switching costs. To switch from one network to another, customers would have to convince their friends and colleagues to switch with them. This extra work for customers creates a barrier to entry in the IM market: with all consumers locked in to an incumbent’s product, there are no customers left with whom to establish a beachhead.

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

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.

This is true startup productivity: systematically figuring out the right things to build.

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?”

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.

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

the growth hypothesis, which tests how new customers will discover a product or service,

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.

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.

  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?”

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

“Success is not delivering a feature; success is learning how to solve the customer’s problem.”4

  • @sergeiw @mooddha @gabrielhdm

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.

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.

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. The minimum viable product lacks many features that may prove essential later on. However, in some ways, creating a MVP requires extra work: we must be able to measure its impact. For example, it is inadequate to build a prototype that is evaluated solely for internal quality by engineers and designers. 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.

startups need to conduct experiments that help determine what techniques will work in their unique circumstances. For startups, the role of strategy is to help figure out the right questions to ask.

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.

Numbers tell a compelling story, but I always remind entrepreneurs that metrics are people, too. No matter how many intermediaries lie between a company and its customers, at the end of the day, customers are breathing, thinking, buying individuals. Their behavior is measurable and changeable.

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.

No amount of design can anticipate the many complexities of bringing a product to life in the real world.

A minimum viable product (MVP) helps entrepreneurs start the process of learning as quickly as possible.3 It is not necessarily the smallest product imaginable, though; it is simply the fastest way to get through the Build-Measure-Learn feedback loop with the minimum amount of effort.

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.

Before new products can be sold successfully to the mass market, they have to be sold to early adopters. These people are a special breed of customer. They accept—in fact prefer—an 80 percent solution; you don’t need a perfect solution to capture their interest.4

Early adopters use their imagination to fill in what a product is missing. They prefer that state of affairs, because what they care about above all is being the first to use or adopt a new product or technology.

The lesson of the MVP is that any additional work beyond what was required to start learning is waste, no matter how important it might have seemed at the time.

They believed—rightly, as it turned out—that file synchronization was a problem that most people didn’t know they had. Once you experience the solution, you can’t imagine how you ever lived without it.

But along the way, their product development team was always focused on scaling something that was working rather than trying to invent something that might work in the future. As a result, their development efforts involved far less waste than is typical for a venture of this kind.

Because of the short time line, none of the prototypes involved advanced technology. Instead, they were MVPs designed to test a more important question: what would be required to get customers to engage with the product and tell their friends about it?

  • @sergeiw @gabrielhdm

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.

MVPs require the courage to put one’s assumptions to the test. If customers react the way we expect, we can take that as confirmation that our assumptions are correct. If we release a poorly designed product and customers (even early adopters) cannot figure out how to use it, that will confirm our need to invest in superior design. But we must always ask: what if they don’t care about design in the same way we do?

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.

If a competitor can outexecute a startup once the idea is known, the startup is doomed anyway. The reason to build a new team to pursue an idea is that you believe you can accelerate through the Build-Measure-Learn feedback loop faster than anyone else can. If that’s true, it makes no difference what the competition knows. If it’s not true, a startup has much bigger problems, and secrecy won’t fix them.

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. The MVP is just the first step on a journey of learning. Down that road—after many iterations—you may learn that some element of your product or strategy is flawed and decide it is time to make a change, which I call a pivot, to a different method for achieving your vision.

A startup’s job is to (1) rigorously measure where it is right now, confronting the hard truths that assessment reveals, and then (2) devise experiments to learn how to move the real numbers closer to the ideal reflected in the business plan.

Innovation accounting enables startups to prove objectively that they are learning how to grow a sustainable business. Innovation accounting begins by turning the leap-of-faith assumptions discussed in Chapter 5 into a quantitative financial model. Every business plan has some kind of model associated with it, even if it’s written on the back of a napkin. That model provides assumptions about what the business will look like at a successful point in the future.

The rate of growth depends primarily on three things: the profitability of each customer, the cost of acquiring new customers, and the repeat purchase rate of existing customers. The higher these values are, the faster the company will grow and the more profitable it will be. These are the drivers of the company’s growth model.

Innovation accounting works in three steps: first, use a minimum viable product to establish real data on where the company is right now. Without a clear-eyed picture of your current status—no matter how far from the goal you may be—you cannot begin to track your progress. 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.

Every product development, marketing, or other initiative that a startup undertakes should be targeted at improving one of the drivers of its growth model. For example, a company might spend time improving the design of its product to make it easier for new customers to use. This presupposes that the activation rate of new customers is a driver of growth and that its baseline is lower than the company would like. To demonstrate validated learning, the design changes must improve the activation rate of new customers. If they do not, the new design should be judged a failure. This is an important rule: a good design is one that changes customer behavior for the better.

Customer flows govern the interaction of customers with a company’s products. They allow us to understand a business quantitatively and have much more predictive power than do traditional gross metrics.

Once our efforts were aligned with what customers really wanted, our experiments were much more likely to change their behavior for the better.

A split-test experiment is one in which different versions of a product are offered to customers at the same time. By observing the changes in behavior between the two groups, one can make inferences about the impact of the different variations.

Actionable metrics are the antidote to this problem. When cause and effect is clearly understood, people are better able to learn from their actions. Human beings are innately talented learners when given a clear and objective assessment.

First, make the reports as simple as possible so that everyone understands them. Remember the saying “Metrics are people, too.” The easiest way to make reports comprehensible is to use tangible, concrete units.

Each cohort analysis says: among the people who used our product in this period, here’s how many of them exhibited each of the behaviors we care about.

Instead of housing the analytics or data in a separate system, our reporting data and its infrastructure were considered part of the product itself and were owned by the product development team. The reports were available on our website, accessible to anyone with an employee account.

Second, those building reports must make sure the mechanisms that generate the reports are not too complex. Whenever possible, reports should be drawn directly from the master data, rather than from an intermediate system, which reduces opportunities for error. I have noticed that every time a team has one of its judgments or assumptions overturned as a result of a technical problem with the data, its confidence, morale, and discipline are undermined.

Only 5 percent of entrepreneurship is the big idea, the business model, the whiteboard strategizing, and the splitting up of the spoils. The other 95 percent is the gritty work that is measured by innovation accounting: product prioritization decisions, deciding which customers to target or listen to, and having the courage to subject a grand vision to constant testing and feedback.

are we making sufficient progress to believe that our original strategic hypothesis is correct, or do we need to make a major change? That change is called a pivot: a structured course correction designed to test a new fundamental hypothesis about the product, strategy, and engine of growth.

Startup productivity is not about cranking out more widgets or features. It is about aligning our efforts with a business and product that are working to create value and drive growth. In other words, successful pivots put us on a path toward growing a sustainable business.

The goal of creating learning milestones is not to make the decision easy; it is to make sure that there is relevant data in the room when it comes time to decide.

The more money, time, and creative energy that has been sunk into an idea, the harder it is to pivot.

A pivot requires that we keep one foot rooted in what we’ve learned so far, while making a fundamental change in strategy in order to seek even greater validated learning.

a zoom-in pivot, refocusing the product on what previously had been considered just one feature of a larger whole.

a customer segment pivot, keeping the functionality of the product the same but changing the audience focus.

a platform pivot. Instead of selling an application to one customer at a time, David envisioned a new growth model inspired by Google’s AdWords platform. He built a self-serve sales platform where anyone could become a customer with just a credit card. Thus, no matter what cause you were passionate about, you could go to @2gov’s website and @2gov would help you find new people to get involved.

Seasoned entrepreneurs often speak of the runway that their startup has left: the amount of time remaining in which a startup must either achieve lift-off or fail. This usually is defined as the remaining cash in the bank divided by the monthly burn rate, or net drain on that account balance.

The true measure of runway is how many pivots a startup has left: the number of opportunities it has to make a fundamental change to its business strategy.

This is also common with pivots; it is not necessary to throw out everything that came before and start over. Instead, it’s about repurposing what has been built and what has been learned to find a more positive direction.

Once you have found success with early adopters, you want to sell to mainstream customers. Mainstream customers have different requirements and are much more demanding.

a customer segment pivot. In this pivot, the company realizes that the product it’s building solves a real problem for real customers but that they are not the customers it originally planned to serve. In other words, the product hypothesis is confirmed only partially.

We had gotten really good at optimizing, tuning, and iterating, but in the process we had lost sight of the purpose of those activities: testing a clear hypothesis in the service of the company’s vision. Instead, we were chasing growth, revenue, and profits wherever we could find them.

A pivot is a special kind of change designed to test a new fundamental hypothesis about the product, business model, and engine of growth.

Implicit in the idea of monetization is that it is a separate “feature” of a product that can be added or removed at will. In reality, capturing value is an intrinsic part of the product hypothesis. Often, changes to the way a company captures value can have far-reaching consequences for the rest of the business, product, and marketing strategies.

A pivot is better understood as a new strategic hypothesis that will require a new minimum viable product to test.

A pivot is not just an exhortation to change. Remember, it is a special kind of structured change designed to test a new fundamental hypothesis about the product, business model, and engine of growth. It is the heart of the Lean Startup method. It is what makes the companies that follow Lean Startup resilient in the face of mistakes: if we take a wrong turn, we have the tools we need to realize it and the agility to find another path.

Product releases incur overhead, and so from an efficiency point of view, releasing often leaves less time to devote to building the product. However, waiting too long to release can lead to the ultimate waste: making something that nobody wants.

Recall from Chapter 3 that value in a startup is not the creation of stuff, but rather validated learning about how to build a sustainable business. What products do customers really want? How will our business grow? Who is our customer? Which customers should we listen to and which should we ignore? These are the questions that need answering as quickly as possible to maximize a startup’s chances of success. That is what creates value for a startup.

The biggest advantage of working in small batches is that quality problems can be identified much sooner.

Working in small batches ensures that a startup can minimize the expenditure of time, money, and effort that ultimately turns out to have been wasted.

When our immune system detects a problem, a number of things happen immediately: 1. The defective change is removed immediately and automatically. 2. Everyone on the relevant team is notified of the problem. 3. The team is blocked from introducing any further changes, preventing the problem from being compounded by future mistakes … 4. … until the root cause of the problem is found and fixed. (This root cause analysis is discussed in greater detail in Chapter 11.)

The essential lesson is not that everyone should be shipping fifty times per day but that by reducing batch size, we can get through the Build-Measure-Learn feedback loop more quickly than our competitors can. The ability to learn faster from customers is the essential competitive advantage that startups must possess.

When I work with product managers and designers in companies that use large batches, I often discover that they have to redo their work five or six times for every release.

Large batches tend to grow over time. Because moving the batch forward often results in additional work, rework, delays, and interruptions, everyone has an incentive to do work in ever-larger batches, trying to minimize this overhead. This is called the large-batch death spiral because, unlike in manufacturing, there are no physical limits on the maximum size of a batch.6 It is possible for batch size to keep growing and growing.

The ideal goal is to achieve small batches all the way down to single-piece flow along the entire supply chain. Each step in the line pulls the parts it needs from the previous step. This is the famous Toyota just-in-time production method.8

As soon as we formulate a hypothesis that we want to test, the product development team should be engineered to design and run this experiment as quickly as possible, using the smallest batch size that will get the job done.

disillusioned directors of HR can attest. The Lean Startup works only if we are able to build an organization as adaptable and fast as the challenges it faces. This requires tackling the human challenges inherent in this new way of working;

Sustainable growth is characterized by one simple rule: New customers come from the actions of past customers.

In fact, one of the most expensive forms of potential waste for a startup is spending time arguing about how to prioritize new development once it has a product on the market. At any time, the company could invest its energy in finding new customers, servicing existing customers better, improving overall quality, or driving down costs.

Engines of growth are designed to give startups a relatively small set of metrics on which to focus their energies.

“Startups don’t starve; they drown.” There are always a zillion new ideas about how to make the product better floating around, but the hard truth is that most of those ideas make a difference only at the margins. They are mere optimizations. Startups have to focus on the big experiments that lead to validated learning. The engines of growth framework helps them stay focused on the metrics that matter.

Therefore, companies using the sticky engine of growth track their attrition rate or churn rate very carefully. The churn rate is defined as the fraction of customers in any period who fail to remain engaged with the company’s product. The rules that govern the sticky engine of growth are pretty simple: if the rate of new customer acquisition exceeds the churn rate, the product will grow. The speed of growth is determined by what I call the rate of compounding, which is simply the natural growth rate minus the churn rate. Like a bank account that earns compounding interest, having a high rate of compounding will lead to extremely rapid growth—without advertising, viral growth, or publicity stunts.

Online social networks and Tupperware are examples of products for which customers do the lion’s share of the marketing. Awareness of the product spreads rapidly from person to person similarly to the way a virus becomes an epidemic. This is distinct from the simple word-of-mouth growth discussed above. Instead, products that exhibit viral growth depend on person-to-person transmission as a necessary consequence of normal product use. Customers are not intentionally acting as evangelists; they are not necessarily trying to spread the word about the product. Growth happens automatically as a side effect of customers using the product. Viruses are not optional.

Like the other engines of growth, the viral engine is powered by a feedback loop that can be quantified. It is called the viral loop, and its speed is determined by a single mathematical term called the viral coefficient. The higher this coefficient is, the faster the product will spread. The viral coefficient measures how many new customers will use a product as a consequence of each new customer who signs up. Put another way, how many friends will each customer bring with him or her? Since each friend is also a new customer, he or she has an opportunity to recruit yet more friends.

By contrast, a viral loop with a coefficient that is greater than 1.0 will grow exponentially, because each person who signs up will bring, on average, more than one other person with him or her.

A consequence of this is that many viral products do not charge customers directly but rely on indirect sources of revenue such as advertising. This is the case because viral products cannot afford to have any friction impede the process of signing customers up and recruiting their friends. This can make testing the value hypothesis for viral products especially challenging.

In the viral engine of growth, monetary exchange does not drive new growth; it is useful only as an indicator that customers value the product enough to pay for it. If Facebook or Hotmail had started charging customers in their early days, it would have been foolish, as it would have impeded their ability to grow. However, it is not true that customers do not give these companies something of value: by investing their time and attention in the product, they make the product valuable to advertisers. Companies that sell advertising actually serve two different groups of customers—consumers and advertisers—and exchange a different currency of value with each.2

Imagine another pair of businesses. The first makes 100,000 from each customer it signs up. To predict which company will grow faster, you need to know only one additional thing: how much it costs to sign up a new customer.

Like the other engines, the paid engine of growth is powered by a feedback loop. Each customer pays a certain amount of money for the product over his or her “lifetime” as a customer. Once variable costs are deducted, this usually is called the customer lifetime value (LTV). This revenue can be invested in growth by buying advertising.

The margin between the LTV and the CPA determines how fast the paid engine of growth will turn (this is called the marginal profit).

Similarly, advertising that is targeted to more affluent customers generally costs more than advertising that reaches the general public. What determines these prices is the average value earned in aggregate by the companies that are in competition for any given customer’s attention. Wealthy consumers cost more to reach because they tend to become more profitable customers.

Over time, any source of customer acquisition will tend to have its CPA bid up by this competition. If everyone in an industry makes the same amount of money on each sale, they all will wind up paying most of their marginal profit to the source of acquisition. Thus, the ability to grow in the long term by using the paid engine requires a differentiated ability to monetize a certain set of customers.

Technically, more than one engine of growth can operate in a business at a time. For example, there are products that have extremely fast viral growth as well as extremely low customer churn rates. Also, there is no reason why a product cannot have both high margins and high retention. However, in my experience, successful startups usually focus on just one engine of growth, specializing in everything that is required to make it work. Companies that attempt to build a dashboard that includes all three engines tend to cause a lot of confusion because the operations expertise required to model all these effects simultaneously is quite complicated. Therefore, I strongly recommend that startups focus on one engine at a time.

Chapter 6 emphasized the importance of building the minimum viable product in such a way that it contains no additional features beyond what is required by early adopters. Following that strategy successfully will unlock an engine of growth that can reach that target audience. However, making the transition to mainstream customers will require tremendous additional work.4

The key to the andon cord is that it brings work to a stop as soon as an uncorrectable quality problem surfaces—which forces it to be investigated. This is one of the most important discoveries of the lean manufacturing movement: you cannot trade quality for time.

The higher-quality the existing playbook is, the easier it will be for it to evolve over time. By contrast, a low-quality playbook will be filled with contradictory or ambiguous rules that cause confusion when anything is changed.

When you’re going too fast, you cause more problems. Adaptive processes force you to slow down and invest in preventing the kinds of problems that are currently wasting time. As those preventive efforts pay off, you naturally speed up again.

The core idea of Five Whys is to tie investments directly to the prevention of the most problematic symptoms. The system takes its name from the investigative method of asking the question “Why?” five times to understand what has happened (the root cause). If you’ve ever had to answer a precocious child who wants to know “Why is the sky blue?” and keeps asking “Why?” after each answer, you’re familiar with it. This technique was developed as a systematic problem-solving tool by Taiichi Ohno, the father of the Toyota Production System. I have adapted it for use in the Lean Startup model with a few changes designed specifically for startups.

At the root of every seemingly technical problem is a human problem. Five Whys provides an opportunity to discover what that human problem might be.

With startups in particular, there is a danger that teams will work too fast, trading quality for time in a way that causes sloppy mistakes. Five Whys prevents that, allowing teams to find their optimal pace.

Five Whys is a powerful organizational technique. Some of the engineers I have trained to use it believe that you can derive all the other Lean Startup techniques from the Five Whys. Coupled with working in small batches, it provides the foundation a company needs to respond quickly to problems as they appear, without overinvesting or overengineering.

I ask teams to adopt these simple rules: 1. Be tolerant of all mistakes the first time. 2. Never allow the same mistake to be made twice.

“Organizations have muscle memory,” and it is hard for people to unlearn old habits.

As Lean Startups grow, they can use adaptive techniques to develop more complex processes without giving up their core advantage: speed through the Build-Measure-Learn feedback loop. In fact, one of the primary benefits of using techniques that are derived from lean manufacturing is that Lean Startups, when they grow up, are well positioned to develop operational excellence based on lean principles. They already know how to operate with discipline, develop processes that are tailor-made to their situation, and use lean techniques such as the Five Whys and small batches. As a successful startup makes the transition to an established company, it will be well poised to develop the kind of culture of disciplined execution that characterizes the world’s best firms, such as Toyota.

As startups grow, entrepreneurs can build organizations that learn how to balance the needs of existing customers with the challenges of finding new customers to serve, managing existing lines of business, and exploring new business models—all at the same time. And, if they are willing to change their management philosophy, I believe even large, established companies can make this shift to what I call portfolio thinking.

structural attributes: scarce but secure resources, independent authority to develop their business, and a personal stake in the outcome.

Thus, startups are both easier and more demanding to run than traditional divisions: they require much less capital overall, but that capital must be absolutely secure from tampering.

In my experience, people defend themselves when they feel threatened, and no innovation can flourish if defensiveness is given free rein. In fact, this is why the common suggestion to hide the innovation team is misguided.

  1. Any team can create a true split-test experiment that affects only the sandboxed parts of the product or service (for a multipart product) or only certain customer segments or territories (for a new product). However: 2. One team must see the whole experiment through from end to end. 3. No experiment can run longer than a specified amount of time (usually a few weeks for simple feature experiments, longer for more disruptive innovations). 4. No experiment can affect more than a specified number of customers (usually expressed as a percentage of the company’s total mainstream customer base). 5. Every experiment has to be evaluated on the basis of a single standard report of five to ten (no more) actionable metrics. 6. Every team that works inside the sandbox and every product that is built must use the same metrics to evaluate success. 7. Any team that creates an experiment must monitor the metrics and customer reactions (support calls, social media reaction, forum threads, etc.) while the experiment is in progress and abort it if something catastrophic happens.

Functional specialists, especially those steeped in waterfall or stage-gate development, have been trained to work in extremely large batches. This causes even good ideas to get bogged down by waste. By making the batch size small, the sandbox method allows teams to make cheap mistakes quickly and start learning. As we’ll see below, these small initial experiments can demonstrate that a team has a viable new business that can be integrated back into the parent company.

the sequence of accountability is the same: build an ideal model of the desired disruption that is based on customer archetypes, launch a minimum viable product to establish a baseline, and then attempt to tune the engine to get it closer to the ideal.

Once the market for the new product is well established, procedures become more routine. To combat the inevitable commoditization of the product in its market, line extensions, incremental upgrades, and new forms of marketing are essential.

However, all companies engage in all four phases of work all the time. As soon as a product hits the marketplace, teams of people work hard to advance it to the next phase. Every successful product or feature began life in research and development (R&D), eventually became a part of the company’s strategy, was subject to optimization, and in time became old news.

The problem for startups and large companies alike is that employees often follow the products they develop as they move from phase to phase. A common practice is for the inventor of a new product or feature to manage the subsequent resources, team, or division that ultimately commercializes it. As a result, strong creative managers wind up getting stuck working on the growth and optimization of products rather than creating new ones.

Some people are natural inventors who prefer to work without the pressure and expectations of the later business phases. Others are ambitious and see innovation as a path toward senior management. Still others are particularly skilled at the management of running an established business, outsourcing, and bolstering efficiencies and wringing out cost reductions. People should be allowed to find the kinds of jobs that suit them best.

Can we use the theory to predict the results of the proposed change? Can we incubate the change in a small team and see what happens? Can we measure its impact?

Instead, we want to force teams to work cross-functionally to achieve validated learning. Many of the techniques for doing this—actionable metrics, continuous deployment, and the overall Build-Measure-Learn feedback loop—necessarily cause teams to suboptimize for their individual functions. It does not matter how fast we can build. It does not matter how fast we can measure. What matters is how fast we can get through the entire loop.

Ultimately, the Lean Startup is a framework, not a blueprint of steps to follow. It is designed to be adapted to the conditions of each specific company. Rather than copy what others have done, techniques such as the Five Whys allow you to build something that is perfectly suited to your company.

There is a reason all past management revolutions have been led by engineers: management is human systems engineering.

In the twenty-first century, we face a new set of problems that Taylor could not have imagined. Our productive capacity greatly exceeds our ability to know what to build. Although there was a tremendous amount of invention and innovation in the early twentieth century, most of it was devoted to increasing the productivity of workers and machines in order to feed, clothe, and house the world’s population. Although that project is still incomplete, as the millions who live in poverty can attest, the solution to that problem is now strictly a political one. We have the capacity to build almost anything we can imagine. The big question of our time is not Can it be built? but Should it be built? This places us in an unusual historical moment: our future prosperity depends on the quality of our collective imaginations.

In 1911, Taylor wrote: We can see our forests vanishing, our water-powers going to waste, our soil being carried by floods into the sea; and the end of our coal and our iron is in sight. But our larger wastes of human effort, which go on every day through such of our acts as are blundering, ill-directed, or inefficient … are less visible, less tangible, and are but vaguely appreciated. We can see and feel the waste of material things. Awkward, inefficient, or ill-directed movements of men, however, leave nothing visible or tangible behind them. Their appreciation calls for an act of memory, an effort of the imagination. And for this reason, even though our daily loss from this source is greater than from our waste of material things, the one has stirred us deeply, while the other has moved us but little.1

As Peter Drucker said, “There is surely nothing quite so useless as doing with great efficiency what should not be done at all.”2

It is insufficient to exhort workers to try harder. Our current problems are caused by trying too hard—at the wrong things. By focusing on functional efficiency, we lose sight of the real goal of innovation: to learn that which is currently unknown. As Deming taught, what matters is not setting quantitative goals but fixing the method by which those goals are attained. The Lean Startup movement stands for the principle that the scientific method can be brought to bear to answer the most pressing innovation question: How can we build a sustainable organization around a new set of products or services?

to achieve better results systematically by changing their beliefs about how innovation happens.

Our society needs the creativity and vision of entrepreneurs more than ever. In fact, it is precisely because these are such precious resources that we cannot afford to waste them.

Anytime a team attempts to demonstrate cause and effect by placing highlights on a graph of gross metrics, it is engaging in pseudoscience. How do we know that the proposed cause and effect is true? Anytime a team attempts to justify its failures by resorting to learning as an excuse, it is engaged in pseudoscience as well.

Only by building a model of customer behavior and then showing our ability to use our product or service to change it over time can we establish real facts about the validity of our vision.

For one thing, everyone would insist that assumptions be stated explicitly and tested rigorously not as a stalling tactic or a form of make-work but out of a genuine desire to discover the truth that underlies every project’s vision.

(you can see the results of these experiments at lean.st).

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