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Quotes

Introduction

The 5 principles of the Lean Startup, which inform all three parts of this book, are as follows:

1. Entrepreneurs are everywhere. You don't have to work garage to be in a startup. The concept of entrepreneurship includes anyone who works within my definition of a startups 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 site 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 toe 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 food back loop

5. Innovation accounting. To improve entrepreneurial out comes and hold innovators accountable, we need to focus on the boring stuff: how to measure progress, how to set up mile stones, and how to prioritize work. This requires a new kind of accounting designed for startups-and the people who hold them accountable.

Part One: Vision

1. Start

If you have a daily commute, you probably know the route so well that your hands seem to steer you there on their own accord. We can practically drive the route in our sleep. Yet if I asked you to close your eyes and write down exactly how to get to your office-not the street directions but every action you need to take, every push of hand on wheel and foot on pedals-you'd find it impossible.

By contrast, a rocket ship requires just this kind of in-advance calibration. It must be launched with the most precise instructions on what to do: every thrust, every firing of a booster, and every change in direction. The tiniest error at the point of launch could yield catastrophic results thousands of miles later.

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 of 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.

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 cus tomer will be. The product is the end result of this strategy

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. Entrepreneurs are committed to seeing the startup through to that destination. Every setback is an opportunity for learning how to get where they want to go.

In real life, a startup is a portfolio of activities. A lot is happening simultaneously: the engine is running, acquiring new customers and serving existing ones, we are running, trying to improve our product, marketing, and operations, and we steering, deciding if and when to pivot. The challenge of entrepreneurship is to balance all these activities. Even the small est 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.

2. Define

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

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

3. Learn

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.

Aligned with a superior strategy, our product development efforts became magically more productive - not because we were working harder but because we were working smarter, aligned with our customers' real neds. 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. Is 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.

4. Experiment

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 4 questions:

1. Do customers 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?

The common tendency of product development is to skip straight to the 4th question and build a solution before confirming that customers have the problem.

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 use it, which was extremely valuable information. Where customers complained about missing features, this suggested that the team was on the right track. The team now has 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.

"Success is not delivering a feature; success is learning how to solve the customer's problem"

As a result of those early experiments, VLS created an end product that was a 3-foot x 4-foot mobile kiosk that included an energy-efficient, consumer-grade washing machine, a dryer, and an extra-long extension cord. The kiosk used Western detergent and was supplied with fresh clean water delivered by VLS. Since then, the Village Laundry Service has grown substantially, with 14 locations operational in Bangalore, Mysore, and Mumbai. As CEO Akshay Mehra shared with me, "We have serviced 116,000 kgs in 20210 (vs 36,000 kgs in 2009). And almost 60% of the business is coming from repeat customers. We have serviced more than 10,000 customers in the past year alone across all the outlets."

Part One: Steer

How Vision Leads to Steering

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.

Many people have professional training that emphasizes one element of this feedback loop. For engineers, it's learning to build things as efficiently as possible. 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 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.

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-bolts usefulness of actionable metrics, which help to analyze customer behavior in ways that support innovation accounting

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

Part Two: Steer

5. Leap

Every business plan begins with a set of assumptions. It lays out a strategy that takes those assumptions as a given and proceeds to show how to achieve the company's vision. Because the assumptions haven't been proved to be true (they are assumptions, after all) and in fact are often erroneous, the goal of a startup's early efforts should be to test them as quickly as possible. 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.

In a startup's earliest days, there is not enough data to make an informed guess about what this model might look like. A startup's earliest strategic plans are likely to be hunch - or intuition-guided, and that is a good thing. To translate those instincts into data, entrepreneurs must, in Steve Blank's famous phrase, "get out of the building" and start learning.

While a company working on a sustaining innovation knows enough about who and where their customers are to use genchi gembutsu to discover what customers want, startups' early contact with potential customers merely reveals what assumptions require the most urgent testing.

The goal of such early contact with customers is not to gain definitive answers. Instead, it is to clarify at a basic, coarse level that we understand our potential customer and what problems they have. 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.

There are 2 ever-present dangers when entrepreneurs conduct market research and talk to customer. Followers of the just-do-it school of entrepreneurship are impatient to get started and don't want to spend time analyzing their strategy. They'd rather start building immediately, often after just a few cursory customer conversations. Unfortunately, because customers don't really know what they want, it's easy for these entrepreneurs to delude themselves that they are on the right path.

Other entrepreneurs can fall victim of analysis paralysis, endlessly refining their plans. In this case, talking to customers, reading research reports, and whiteboard strategizing are all equally unhelpful. The problem with most entrepreneurs' plan 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.

6. Test

A minimum viable product (MVP) helps entrepreneurs start the process of learning as quickly as possible. 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. Contrary to traditional product development, which usually involves a long, thoughtful incubation period and strives for product perfection, the goal of the MVP is to begin the process of learning, not end it.

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.

In particular, Dropbox needed to test its leap-of-faith question: if we can provide a superior customer experience, will people give our product a try? 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.

The challenge was that it was impossible to demonstrate the working software in a prototype form. The product required that they overcome significant technical hurdles; it also had an online service component that required high reliability and availability. 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. The video is banal, a simple three-minute demonstration of the technology as it is meant to work, but it was targeted at a community of technology early adopters. Drew narrates the video personally, and as he's narrating, the viewer is watching his As he describes the kinds of files he'd like to synchronize, the viewer can watch his mouse manipulate his computer. Of course, if you're paying attention, you start to notice that the files he's moving around are full of in-jokes and humorous references that were appreciated by this community of early adopters. Drew recounted, "It drove hundreds of thousands of people to the website. Our beta waiting list went from 5,000 people to 75,000 people literally overnight. It totally blew us away." Today, Dropbox is one of Silicon Valley's hottest companies, rumored to be worth more than $1 billion.

In this case, the video was the minimum viable product. The MVP validated Drew's leap-of-faith assumption that customers wanted the product he was developing not because they said so in a focus group or because of a hopeful analogy to another business, but because they actually signed up.

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

Even a "low-quality" MVP can act in service of building a great high-quality product. Yes, MVPs sometimes are perceived as low-quality by customers. If so, we should use this as an opportunity to learn what attributes customers are about. This is infinitely better than mere speculation or whiteboard strategizing, because it provides a solid empirical foundation on which to build future products.

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? Thus, the Lean Startup method is not opposed to building high-quality products, but only in service of the goal of winning over customers. We must be willing to set aside our traditional professional standards to start the process of validated learning as soon as possible.

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. Sooner or later, a succesful startup will face competition from last followers. A head start is rarely large enough to matter, and time spent in stealth mode - away from customers - is unlikely to provide a head start. The only way to win is to learn faster than anyone else.

Many startups plan to invest in building a great brand, and an MVP can seem like a dangerous branding risk. Similarly, entrepreneurs in existing organizations often are constrained by the fear of damaging the parent company's established brand. In either of these cases, there is an easy solution: launch the MVP under a different brand name. In addition, a long-term reputation is only at risk when companies engage in vocal launch activities such as PR and building hype. When a product fails to live up to those pronouncements, real long-term damage can happen to a corporate brand. But startups have the advantage of being obscure, having a pathetically small number of customer and not having much exposure. Rather than lamenting them use these advantages to experiment under the radar and then do a public marketing launch once the product has proved in with real customers.

Finally, it helps to prepare for the fact that MVPs often result in bad news. Unlike traditional concept tests or prototypes, they are designed to speak to the full range of business questions, not just design or technical ones, and they often provide a needed dose of reality. In fact, piercing the reality distortion field is quite uncomfortable. Visionaries are especially afraid of a false negative: that customers will reject a flawed MVP that is too small or too limited. It is precisely this attitude that one sees when companies launch fully formed products without prior testing. They simply couldn't bear to test them in anything less than their full splendor. Yet there is wisdom in the visionary's fear. Teams steeped in traditional product development methods are trained to make go/kill decisions on a regular basis. That is the essence of the waterfall or stage-gate development model. If an MVP fails, teams are liable to give up hope and abandon the project altogether.

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 differnet method for achieving your vision.

7. Measure

At I the beginning, a startup is little more than a model on a piece of paper. The financials in the business plan include projection of how many customers the company expects to attract, how much it will spend, and how much revenue and profit that will lead to. It's an ideal that's usually far from where the startup is in its early days.

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.

Most products-even the ones that fail-do not have zero traction. Most products have some customers, some growth. and some positive results. One of the most dangerous our comes for a startup is to bumble along in the land of the living dead. Employees and entrepreneurs tend to be optimistic by nature. We want to keep believing in our ideas even when the writing is on the wall. This is why the myth of perseverance so dangerous. We all know stories of epic entrepreneurs who managed to pull out a victory when things seemed incredibly bleak. Unfortunately, we don't hear stories about the countless nameless others who persevered too long, leading their companies to failure.

For example, the business plan for an established manufacturing company would show it growing in proportion to its sales volume. As the profits from the sales of goods are reinvested in marketing and promotions, the company gains new customers. The rate of growth depends primarily on 3 things:

1. the profitability of each customer
2. the cost of acquiring new customers, and
3. 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.

By contrast, a marketplace company that matches buyers and sellers such as eBay will have a different growth model. Its success depends primarily on the network effects that make it the premier destination for both buyers and sellers to transact business. Sellers want the marketplace with the highest number of potential customers. Buyers want the marketplace with the most competition among sellers, which leads to the greatest availability of products and the lowest prices. (In economics, this sometimes is called supply-side increasing returns and demand-side increasing returns.) For this kind of startup, the important thing to measure is that the network effects are working, as evidenced by the high retention rate of new buyers and sellers. If people stick with the product with very little attrition, the marketplace will grow no matter how the company acquires new customers. The growth curve will look like a compounding interest table, with the rate of growth depending on the "interest rate" of new customers coming to the product.

Though these two businesses have very different drivers of growth, we can still use a common framework to hold their leaders accountable. This framework supports accountability even when the model changes. 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 fre Baseline toward the ideal. This may take many attempts. Aber 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.

If the company is making good progress toward the ideal, that means it's learning appropriately and using that leaning effectively, in which case it makes sense to continue If not, the management team eventually must conclude that its current product strategy is flawed and needs a serious change When company pivots, it starts the process all over again, reestablish ing a new baseline and then tuning the engine from there. The sign of a successful pivot is that these engine-tuning activities are more productive after the pivot than before.

Establish the Baseline

A startup might create a complete prototype of its product and offer to sell it to real customers through its main marketing channel. This single MVP would rest most of the startup's assumptions and establish baseline metrics for each sumption simultaneously. Alternatively, a startup might prefer to build separate MVPs that are aimed at getting feedback on one assumption at a time. Before building the prototype, the company might perform a smoke test with its marketing materials. This is an old direct marketing technique in which customers are given the opportunity to preorder a product that has not yet been built. A smoke test measures only one thing whether customers are interested in trying a product. By itself, this is insufficient to validate an entire growth model. Nonetheless, it can be very useful to get feedback on this assumption before committing more money and other resources to the product.

These MVPs provide the first example of a learning milestones. An MVP allows a startup to fill in real baseline data in its growth model-conversion rates, sign-up and trial rates, customer life time value, and so on and this is valuable as the foundation for learning about customers and their reactions to a product even if that foundation begins with extremely bad news.

Tuning the Engine

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.

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 hypothesi 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?

Pivot or Persevere

Over time, a team that is learning its way toward a sustainable business will see the numbers in its model rise from the horrible baseline established by the MVP and converge to something like the ideal one established in the business plan. A startup that fails to do so will see that ideal recede ever farther into the distance. When this is done right, even the most powerful reality distortion field won't be able to cover up this simple fact: if we're not moving the drivers of our business model, we're not making progress. That becomes a sure sign that it's time to pivot.

Once I had data in hand, my interactions with customers changed. Suddenly I had urgent questions that needed answering: Why aren't customers responding to our product "improvements"? Why isn't our hard work paying off? For example, we kept making it easier and easier for customers to use IMVU with their existing friends. Unfortunately, customers didn't want to engage in that behavior. Making it easier to use was totally beside the point. Once we knew what to look for, genuine understanding came much faster. As was described in Chapter 3, this eventually led to a critically important pivot: away from an IM add-on used with existing friends and toward a standalone network one can use to make new friends. Suddenly, our worries about productivity vanished. Once our efforts were aligned with what customers really wanted, our experiments were much more likely to change their behavior for the better.

This pattern would repeat time and again, from the days when we were making less than a thousand dollars in revenue per month all the way up to the time we were making millions. In fact, this is the sign of a successful pivot: the new experiments you run are overall more productive than the experiments you were running before.

This is the pattern: poor quantitative results force us to declare failure and create the motivation, context, and space for more qualitative research. These investigations produce new ideas-new hypotheses to be tested, leading to a possible pivot. Each pivot unlocks new opportunities for further experimentation, and the cycle repeats. Each time we repeat this simple rhythm: establish the baseline, tune the engine, and make a decision to pivot or persevere.

Cohort Analysis

In the example above, early in the company's life, the product development team was incredibly productive because the company's founders had identified a large unmet need in the target market. The initial product, while flawed, was popular with early adopters. Adding the major features that customers asked for seemed to work wonders, as the early adopters spread the word about the innovation far and wide. But unasked and unanswered were other lurking questions: Did the company have a working engine of growth? Was this early success related to the daily work of the product development team? In most cases, the answer was no; success was driven by decisions the team had made in the past. None of its current initiatives were having any impact. But this was obscured because the company's gross metrics were all "up and to the right."

As we'll see in a moment, this is a common danger. Companies of any size that have a working engine of growth can come to rely on the wrong kind of metrics to guide their actions. This is what tempts managers to resort to the usual bag of success theater tricks: last-minute ad buys, channel stuffing, and whiz-bang demos, in a desperate attempt to make the gross numbers look better. Energy invested in success theater is energy that could have been used to help build a sustainable business. I call the traditional numbers used to judge startups "vanity metrics," and innovation accounting requires us to avoid the temptation to use them.

Cohorts and Split-tests

Split testing often uncovers surprising things. For example, many features that make the product better in the eyes of engineers and designers have no impact on customer behavior. This was the case at Grockit, as it has been in every company I have seen adopt this technique. Although working with split tests seems to be more difficult because it requires extra accounting and metrics to keep track of each variation, it almost always saves tremendous amounts of time in the long run by eliminating work that doesn't matter to customers. Split testing also helps teams refine their understanding of what customers want and don't want. Grockit's team constantly added new ways for their customers to interact with each other in the hope that those social communication tools would increase the product's value. Inherent in those efforts was the belief that customers desired more communication during their studying. When split testing revealed that the extra features did not change customer behavior, it called that belief into question.

The questioning inspired the team to seek a deeper understanding of what customers really wanted. They brainstormed new ideas for product experiments that might have more impact. In fact, many of these ideas were not new. They had simply been overlooked because the company was focused on building social tools. Without the discipline of spilt testing, the company might not have had this realization

First, remember that "Metrics are people, too." We need to be able to test the data by hand, in the messy real world, by talking to customers. This is the only way to be able to check if the reports contain true facts. Managers need the ability to spot check the data with real customers. It also has a second benefit: systems that provide this level of auditability give managers and entrepreneurs the opportunity to gain insights into why customers are behaving the way the data indicate.

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.

One decision stands out above all others as the most difficult, the most time-consuming, and the biggest source of waste for most startups. We all must face this fundamental test: deciding when to pivot and when to persevere.

8. Pivot (or Persevere)

Every entrepreneur eventually faces an overriding challenge in developing a successful product: deciding when to pivot and when to persevere. Everything that has been discussed so far is a prelude to a seemingly simple question: 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.

In 2003 I started a company in roughly the same space as I'm in today. I had roughly the same domain expertise and industry credibility, fresh off the USA.gov success. But back then my company was a total failure (despite consuming significantly greater investment), while now I have a business making money and closing deals. Back then I did the traditional linear product development model, releasing an amazing product (it really was) after 12 months of development, only to find that no one would buy it. This time I produced four versions in twelve weeks and generated my first sale relatively soon after that. And it isn't just market timing-two other companies that launched in a similar space in 2003 subsequently sold for tens of millions of dollars, and others in 2010 followed a linear model straight to the dead pool.

When startups start to run low on cash, they can extend the runway two ways: by cutting costs or by raising additional funds. But when entrepreneurs cut costs indiscriminately, they are as liable to cut the costs that are allowing the company to get through its Build-Measure-Learn feedback loop as they are to cut waste. If the cuts result in a slowdown to this feedback loop, all they have accomplished is to help the startup go out of business more slowly. 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. Measuring runway through the lens of pivots rather than that of time suggests another way to extend that runway: get to each pivot faster. In other words, the startup has to find ways to achieve the same amount of validated learning at lower cost or in a shorter time. All the techniques in the Lean Startup model that have been discussed so far have this as their overarching goal.

Ask most entrepreneurs who have decided to pivot and they will tell you that they wish they had made the decision sooner:

• First, vanity metrics can allow entrepreneurs to form false conclusions and live in their own private reality. This is particularly damaging to the decision to pivot because it robs teams of the belief that it is necessary to change. When people are forced to change against their better judgment, the process is harder, takes longer, and leads to a less decisive outcome.
• Second, when an entrepreneur has an unclear hypothesis, it's almost impossible to experience complete failure, and without failure there is usually no impetus to embark on the radical change a pivot requires. As I mentioned earlier, the failure of the "launch it and see what happens" approach should now be evident: you will always succeed-in seeing what happens. Except in rare cases, the early results will be ambiguous, and you won't know whether to pivot or persevere, whether to change direction or stay the course.
• Third, many entrepreneurs are afraid. Acknowledging failure can lead to dangerously low morale. Most entrepreneurs' biggest fear is not that their vision will prove to be wrong. More terrifying is the thought that the vision might be deemed wrong without having been given a real chance to prove itself.

The reality of our team and our backgrounds built up a massive wall of expectations. I don't think it would have mattered what we would have released; we would have been met with expectations that are hard to live up to. But to us it just meant we needed to get our product and our vision out into the market broadly in order to get feedback and to begin iteration. We humbly test our theories and our approach to see what the market thinks. Listen to feedback honestly. And continue to innovate in the directions we think will create meaning in the world

Catalog of Pivots:

• Zoom-in Pivot: In this case, what previously was considered a single feature in a product becomes the whole product. This is the type of pivot Votizen made when it pivoted away from a full social network and toward a simple voter contact product.
• Zoom-out Pivot: In the reverse situation, sometimes a single feature is insufficient to support a whole product. In this type of pivot, what was considered the whole product becomes a single feature of a much larger product.
• Customer Segment Pivot: In this pivot, the company realizes that the product it is building solves a real problem for real customers but that they are not the type of customers it originally planned to serve. In other words, the product hypothesis is partially confirmed, solving the right problem, but for a different customer than originally anticipated.
• Customer Need Pivot: As a result of getting to know customers extremely well, it sometimes becomes clear that the problem we're trying to solve for them is not very important. However, because of this customer intimacy, we often discover other related problems that are important and can be solved by our team. In many cases, these related problems may require little more than repositioning the existing product. In other cases, it may require a completely new product. Again, this a case where the product hypothesis is partially confirmed; the target customer has a problem worth solving, just not the one that was originally anticipated.
• Platform Pivot: A platform pivot refers to a change from an application to a platform or vice versa. Most commonly, startups that aspire to create a new platform begin life by selling a single application, the so-called killer app, for their platform. Only later does the platform emerge as a vehicle for third parties to leverage as a way to create their own related products. However, this order is not always set in stone, and some companies have to execute this pivot multiple times.
• Business Architecture Pivot: This pivot borrows a concept from Geoffrey Moore, who observed that companies generally follow one of two major business architectures: high margin, low volume (complex systems model) or low margin, high volume (volume operations model). The former commonly is associated with business to business (B2B) or enterprise sales cycles, and the latter with consumer products (there are notable exceptions). In a business architecture pivot, a startup switches architectures. Some companies change from high margin, low volume by going mass market (e.g., Google's search "appliance"); others, originally designed for the mass market, turned out to require long and expensive sales cycles.
• Value Capture Pivot: There are many ways to capture the value a company creates. These methods are referred to commonly as monetization or revenue models. These terms are much too limiting. 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.
• Engine of Growth Pivot: As we'll see in Chapter 10, there are three primary engines of growth that power startups: the viral, sticky, and paid growth models. In this type of pivot, a company changes its growth strategy to seek faster or more profitable growth. Commonly but not always, the engine of growth also requires a change in the way value is captured.
• Channel Pivot: In traditional sales terminology, the mechanism by which a company delivers its product to customers is called the sales channel or distribution channel. For example, consumer are sold in a grocery store, cars are sold in dealerships, and much packaged goods enterprise software is sold (with extensive customization) by consulting and professional services firms. Often, the requirements of the channel determine the price, features, and competitive landscape of a product. A channel pivot is a recognition that the same basic solution could be delivered through a different channel with greater effectiveness. Whenever a company abandons a previously complex sales process to "sell direct" to its end users, a channel pivot is in progress. It is precisely because of its destructive effect on sales channels that the Internet has had such a disruptive influence in industries that previously required complex sales and distribution channels, such as newspaper, magazine, and book publishing.
• Technology Pivot: Occasionally, a company discovers a way to achieve the same solution by using a completely different technology. Technology pivots are much more common in established businesses. In other words, they are a sustaining innovation, an incremental improvement designed to appeal to and retain an existing customer base. Established companies excel at this kind of pivot because so much is not changing. The customer segment is the same, the customer's problem is the same, the value-capture model is the same, and the channel partners are the same. The only question is whether the new technology can provide superior price and/or performance compared with the existing technology.

Part Two: Accelerate

9. Batch

The essential lesson is not that everyone should be shipping 50 times per day but that by reducing the 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 starts must process.

The Lean Startup works only if we are able to build an organization as adaptable and fast as the challenges it faces.

Read the book for examples in hardware industry.

10. Grow

There are 4 primary ways past customers drive sustainable growth:

1. Word of mouth: Embedded in most products is a natural level of growth that is caused by satisfied customers' enthusiasm for the product. For example, when I bought my first Tivo DVR, I couldn't stop telling my friends and family about it. Pretty soon, my entire family was using one.
2. As a side effect of product usage: Fashion or status, such as luxury goods products, drive awareness of themselves whenever they are used. When you see someone dressed in the latest clothes or driving a certain car, you may be influenced to buy that product. This is also true of so-called viral products such as Facebook and PayPal. When a customer sends money to a friend using PayPal, the friend is exposed automatically to the PayPal product.
3. Through funded advertising: Most businesses employ advertising to entice new customers to use their products. For this to be a source of sustainable growth, the advertising must be paid for out of revenue, not one-time sources such as investment capital. As long as the cost of acquiring a new customer (the so-called marginal cost) is less than the revenue that cus tomer generates (the marginal revenue), the excess (the marginal profit) can be used to acquire more customers. The more marginal profit, the faster the growth.
4. Through repeat purchase or use: Some products are signed to be purchased repeatedly either through a subscription plan (a cable company) or through voluntary repurchases (groceries de or lightbulbs). By contrast, many products and services are intentionally designed as one-time events, such as wedding planning.

Suppose an advertisement cost $100 and causes 50 new customers to sign up for the service. This ad has a Cost Per Acquisition (CPA) of $2. In this example, if the product has an LifeTime Value (LTV) t hat is larger than $2, the product will grow. The margin between the LTV and the CPA determines how fast the paid engine of growth will turn (this is called the marginal profit).

Technically, more than one engine of growth can operate in business at a time. For example, there are products that have extremely fast viral growth as well as extremely low customer churr 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. Most entrepreneurs already have a strong leap-of-faith hypothesis about which engine is most likely to work. If they do not, time spent out of the building with customers will quickly suggest one that seems profitable. Only after pursuing one engine thoroughly should a startup consider a pivot to one of the others.

Startups occasionally ask me to help them evaluate whether they have achieved product/market fit. It's easy to answer: if you are asking, you're not there yet.

Getting a startup's engine of growth up and running is hel enough, but the truth is that every engine of growth eventually runs out of gas. Every engine is tied to a given set of customers and their related habits, preferences, advertising channels, and Interconnections. At some point, that set of customers will be exhausted. This may take a long time or a short time, depending on one's industry and timing.

Some unfortunate companies wind up following this strategy inadvertently. Because they are using vanity metrics and traditional accounting, they think they are making progress when they see their numbers growing. They falsely believe they are making their product better when in fact they are having no impact on customer behavior. The growth is all coming from an engine of growth that is working-running efficiently to bring in new customers-not from improvements driven by product development. Thus, when the growth suddenly slows, it provokes a crisis.

11. Adapt

1. Be tolerant of all mistakes the first time.
2. Never allow the same mistake to be made twice. The first rule encourages people to get used to being compassionate about mistakes, especially the mistakes of others. Remember, most mistakes are caused by flawed systems, not bad people. The second rule gets the team started making proportional investment in prevention.

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.

Read the book for examples in hardware industry.

12. Innovate

Conventional wisdom holds that when companies become larger, they inevitably lose the capacity for innovation, creativity, and growth. I believe this is wrong. 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.

Successful innovation teams must be structured correctly in order to succeed. Venture-backed and bootstrapped startups naturally have some of these structural attributes as a consequence of being small, independent companies. Internal startup teams require support from senior management to create these structures. Internal or external, in my experience startup teams require 3 structural attributes: 1. scarce but secure resources, 2. independent authority to develop their business, and 3. a personal stake in the outcome. Each of these requirements is different from those of established company divisions. Keep in mind that structure is merely a prerequisite-it does not guarantee success. But getting the structure wrong can lead to almost certain failure.

The challenge here is to create a mechanism for empowering innovation teams out in the open. This is the path toward a sustainable culture of innovation over time as companies face repeated existential threats. My suggested solution is to create a sandbox for innovation that will contain the impact of the new innovation but not constrain the methods of the startup team. It works as follows:

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 of customers can affect more than (usually expressed a specified number as a percentage company's total mainstream customer base). of the
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.

At the beginning, the sandbox has to be quite small. In the company above, the sandbox initially contained only the pricing page. Depending on the types of products the company makes, the size of the sandbox can be defined in different ways. For example, an online service might restrict it to certain pages or user flows. A retail operation might restrict it to certain stores or geographic areas. Companies trying to bring an entirely new product to market might build the restriction around customers in certain segments.

Unlike in a concept test or market test, customers in the sandbox are considered real and the innovation team is allowed to attempt to establish a long-term relationship with them. After all, they may be experimenting with those early adopters for a long time before their learning milestones are accomplished.

The sandbox also promotes rapid iteration. When people have a chance to see a project through from end to end and the work is done in small batches and delivers a clear verdict quickly, they benefit from the power of feedback. Each time they fail to move the numbers, they have a real opportunity to act on their findings immediately. Thus, these teams tend to converge on optimal solutions rapidly even if they start out with really bad ideas.

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 product of a new 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.

This tendency is one of the reasons established companies struggle to find creative managers to foster innovation in the first place. Every new innovation competes for resources with established projects, and one of the scarcest resources is talent.

The way out of this dilemma is to manage the four kinds of work differently, allowing strong cross-functional teams to develop around each area. When products move from phase to phase, they are handed off between teams. Employees can choose to move with the product as part of the handoff or stay behind and begin work on something new. Neither choice is necessarily right or wrong; it depends on the temperament and skills of the person in question.

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.