The Lean Startup: How Today's Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses. Read more. “The Lean Startup isn't just about how to create a more successful entrepreneurial Eric Ries's revolutionary Lean Startup method will help bring your new business idea positioning so that customers at least would download it. We were. You can download The Lean Startup full ebook in pdf format for Free from the link given below. Download link: > The Lean tetraedge.info If that doesn't work.
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Eric Ries's revolutionary Lean Startup method will help bring your new They'd customize the avatar and download the product like before. up for a free trial once they have a certain amount of information about the service. His blog is called Running Lean, and he also has released an eBook of the same name. The Lean Startup: How Constant Innovation Creates Radically Successful Businesses by Eric Ries. Read online, or download in secure EPUB format. Read "The Lean Startup How Today's Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses" by Eric Ries available from.
The Lean Startup is a new approach to business that's being adopted around the world. It is changing the way companies are built and new products are launched. The Lean Startup is about learning what your customers really want. It's about testing your vision continuously, adapting and adjusting before it's too late. Eric Ries is an entrepreneur and the author of the international bestseller The Lean Startup , which has sold over one million copies and has been translated into over thirty languages. He has founded a number of startups including IMVU, where he served as CTO, and he has advised on business and product strategy for startups, venture capital firms, and large companies, including General Electric, where he partnered to create the FastWorks program. Toggle navigation.
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Start a book on your smartphone, pick up where you left off on your laptop and even fall asleep while listening on your tablet - you'll never lose your place. We soon ran out of friends and family; our frustration escalated. Our failure to move the numbers prodded us to accelerate our efforts to bring customers into our Office for in-person interviews and usability tests.
I wish I could say that I was the one to realize our mistake and suggest the solution, but in truth, I was the last to admit the problem. In short, our entire strategic analysis of the market was utterly wrong. We figured this out empirically, through experimentation, rather than through focus groups or market research.
Customers could not tell us what they wanted; most, after all, had never heard of 3D avatars. Instead, they revealed the truth through their action or inaction as we struggled to make the product better. Talking to Customers Out of desperation, we decided to talk to some potential customers.
So exactly what would you like me to do? Imagine a seventeen-year-old giri sitting down with us to look at this product.
But since she was in the room with us, we were able to talk her into doing it. You want me to risk inviting one of my friends?
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What are they going to think of me? So we built a single-nlaver version. What is the point of a single-player experience for a social product?
Out of further desperation, we introduced a feature called ChatNow that allows you to push a button and be randomly matched with somebody else anywhere in the world. The only thing you have in common is that you both pushed the button at the same time. A stranger on my AIM buddy list? One or two, maybe? To which the teenager would sav. I run eight. Our customers revealed that this was nonsense. We had a mental model for how people used software that was years out of date, and so eventually, painfully, after dozens of meetings like that, it started to dawn on us that the IM add-on concept was fundamentally flawed.
They did not consider having to learn how to use a new IM program a barrier; on the contrary, our early adopters used many different IM programs simultaneously. Our customers were not intimidated by the idea of having to take their friends with them to a new IM network; it tumed out that they enjoyed that challenge.
Even more surprising, our assumption that customers would want to use avatar-based IM primarily with their existing friends was also wrong. They wanted to make new friends, an activity that 3D avatars are particularly well suited to facilitating. Bit by bit, customers tore apart our seemingly brilliant initial strategy. Throwing My Work Away Perhaps you can sympathize with our situation and forgive my obstinacy.
After all, it was my work over the prior months that needed to be thrown away.
I had slaved over the software that was required to make our IM program interoperate with other networks, which was at the heart of our original strategy. When it came time to pivot and abandon that original strategy. I felt betrayed.
I was a devotee of the latest in software development methods known collectively as agile development , which promised to help drive waste out of product development.
However, despite that, I had committed the biggest waste of all: That was really depressing. I wondered: Had I really been needed? Would it have been better if I had not done any work at all? There is, as I mentioned at the beginning of this chapter, always one last refuge for people aching to justify their own failure.
We never would have learned that our strategy was flawed. There is truth in this excuse: How mueh of our effort contributed to the essential lessons we needed to learn? This question is at the heart of the lean manufacturing revolution; it is the first question any lean manufacturing adherent is trained to ask.
Leamine to see waste and then svstematicallv eliminate it has allowed lean companies such as Toyota to dominate entire industries. In the world of software, the agile development methodologies I had practiced until that time had their origins in lean thinking.
They were designed to eliminate waste too. The answer came to me slowly over the subsequent years. Lean thinking defines value as providing benefit to the customer; anything else is waste.
But in a startup, who the customer is and what the customer might find valuable are unknown, part of the very uncertainty that is an essential part of the definition of a startup. I realized that as a startup, we needed a new definition of value. The real progress we had made at IMVU was what we had leamed over those first months about what creates value for customers. Anything we had done during those months that did not contribute to our learning was a form of waste.
Would it have been possible to learn the same things with less effort? Clearly, the answer is yes. For one thing, think of all the debate and prioritization of effort that went into features that customers would never discover.
If we had shipped sooner, we could have avoided that waste. Also consider all the waste caused by our incorrect strategic assumptions.
I had built interoperability for more than a dozen different IM clients and networks. Was this really necessary to test our assumptions? Could we have gotten the same feedback from our customers with half as many networks? With only three? With only one? Since the customers of all IM networks found our product equally unattractive, the level of learning would have been the same, but our effort would have been dramatically less.
Is it possible that we could have discovered how flawed our assumptions were without building anything? For examnle. Note that this is different from asking customers what they want. We could have conducted an experiment, offering customers the chance to try something and then measuring their behavior. Such thought experiments were extremely disturbing to me because they undermined my job description. As the head of product development, I thought my job was to ensure the timely delivery of high-quality products and features.
But if many of those features were a waste of time, what should I be doing instead? How could we avoid this waste? The effort that is not absolutely necessary for learning what customers want can be eliminated.
Thus, validated learning is backed up by empirical data collected from real customers. As I can attest, anybody who fails in a startup can claim that he or she has leamed a lot from the experience. They can tell a compelling story. In faet, in the story of IMVU so far, you might have no ticed something missing.
What evidence did we have? Certainly our stories of failure were entertaining, and we had fascinating theories about what we had done wrong and what we needed to do to create a more successful product.
However, the proof did not come until we put those theories into practice and built subsequent versions of the product that showed superior results with actual customers. The next few months are where the true story of IMVU begins, not with our brilliant assumptions and strategies and whiteboard gamesmanship but with the hard work of discovering what customers really wanted and adjusting our product and strategy to meet those desires.
As we came to understand our customers better, we were able to improve our products. As we did that, the fundamental metrics of our business changed. In the early days, despite our efforts to improve the product, our metrics were stubbomly flat. Each day, roughly the same number of customers would buy the product, and that number was pretty close to zero despite the many improvements.
However, once we pivoted away from the original strategy, things started to change. 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.
It is also the right wav to think about Droductivitv in a startuD: We were able to measure the difference in behavior between the two groups. Not only were the people in the experimental group more likely to sign up for the product, they were more likely to become long-term paying customers. We had plenty of failed experiments too.
U nfortunately, customers who got that VIP treatment were no more likely to become active or paying customers. After our pivot and many failed experiments, we finally figured out this insight: Our customers intuitively grasped something that we were slow to realize.
Once we formed this hypothesis, our experiments became much more likely to produce positive results. Whenever we would change the product to make it easier for people to find and keep new friends, we discovered that customers were more likely to engage. This is true startup productivity: These were just a few experiments among hundreds that we ran week in and week out as we started to learn which customers would use the oroduct and whv.
Each bit of knowledee we gathered suggested new experiments to run, which moved our metrics doser and doser to our goal. Unfortunately, because of the traditional way businesses are evaluated, this is a dangerous situation. The irony is that it is often easier to raise money or acquire other resources when you have zero revenue, zero customers, and zero traction than when you have a small amount.
Zero invites imagination, but small numbers invite questions about whether large numbers will ever materialize. Everyone knows or thinks he or she knows stories of produds that achieved breakthrough success ovemight. As long as nothing has been released and no data have been collected, it is still possible to imagine overnight success in the future.
Small numbers pour cold water on that hope. This phenomenon creates a brutal incentive: However, releasing a product and hoping for the best is not a good plan either, because this incentive is real. When we launched IMVU, we were ignorant of this problem.
In faet, at one point, some investors were seriously recommending that we pull the product out of the market and return to stealth mode. Fortunately, as we pivoted and experimented, incorporating what we learned into our product development and marketing efforts, our numbers started to imDrove. But not by much! These early graphs, although promising, were not by themselves sufficient to combat the loss of faith caused by our early failure, and we lacked the language of validated learning to provide an alternative concept to rally around.
We were quite fortunate that some of our early investors understood its importance and were willing to look beyond our small gross numbers to see the real progress we were making. Thus, we can mitigate the waste that happens because of the audacity of zero with validated learning. We could have tried marketing gimmicks, bought a Super Bowl ad, or tried flamboyant public relations PR as a way of juicing our gross numbers.
That would have given investors the illusion of traction, but only for a short time. Eventually, the fundamentals of the business would win out and the PR bump would pass. Because we would have squandered precious resources on theatrics instead of progress, we would have been in real trouble. Sixty million avatars later, IMVU is still going strong.
Its legacy is not just a great product, an amazing team, and promising financial results but a whole new way of measuring the progress of startups.
Every time I teach the IMVU story, students have an overwhelming temptation to focus on the tactics it illustrates: These are useful techniques, but they are not the moral of the story. There are too many exceptions.
Not every kind of customer will accept a low-quality prototype, for example. If the students are more skeptical, they may argue that the techniques do not apply to their industry or situation, but work only because IMVU is a software company, a consumer Internet business, or a non-mission-critical application. None of these takeaways is especially useful. The Lean Startup is not a collection of individual tactics. It is a principled approach to new product development. The only way to make sense of its recommendations is to understand the underlying principles that make them work.
The tactics from the IMVU story may or may not make sense in your particular business. Instead, the way forward is to leam to see every startup in any industry as a grand experiment. In other words, we need the scientific method. In the Lean Startup model, every product, every feature, every marketing campaign — everything a startup does — is understood to be an experiment designed to achieve validated leaming.
This exDerimental approach works across industries and sectors. Which customer opinions should we listen to, if any? How should we prioritize across the many features we could build? What can be changed safely, and what might anger customers?
What should we work on next? This is one of the most important lessons of the scientific method: A true experiment follows the scientific method. It begins with a clear hypothesis that makes predictions about what is supposed to happen. It then tests those predictions empirically. The goal of everv startup experiment is to discover how to build a sustainable business around that vision.
It is known as one of the most successful, customer-friendly e-commerce businesses in the world, but it did not start that way. Founder Nick Swinmurn was frustrated because there was no central Online site with a great selection of shoes.
He envisioned a new and superior retail experience. Swinmurn could have waited a long time, insisting on testing his complete vision complete with warehouses, distribution partners, and the promise of significant sales.
Many early e-commerce pioneers did just that, including infamous dot-com failures such as Webvan and Pets. Instead, he started by running an experiment. His hypothesis was that customers were ready and willing to buy shoes Online.
To test it, he began by asking local shoe stores if he could take pictures of their inventory. In exchange for permission to take the pictures, he would post the pictures Online and come back to buy the shoes at full price if a customer bought them Online. Zappos began with a tiny, simple product. It was designed to answer one question above all: However, a well-designed startup experiment like the one Zappos began with does more than test a single aspect of a business plan.
In the course of testing this first assumption, many other assumptions were tested as well. To seil the shoes, Zappos had to interact with customers: This is decidedly different from market research.
If Zappos had relied on existing market research or conducted a survey, it could have asked what customers thought they wanted.
By building a product instead, albeit a simple one, the company learned much more: It had more accurate data about customer demand because it was observing real customer behavior, not asking hypothetical questions. It put itself in a position to interact with real customers and learn about their needs. For example, the business plan might call for discounted pricing, but how are customer perceptions of the product affected by the discounting strategy?
The Lean Startup
It allowed itself to be surprised when customers behaved in unexpected ways, revealing information Zappos might not have known to ask about. For example, what if customers returned the shoes? It also put the company in a position to observe, interact with, and leam from real customers and partners. This qualitative leaming is a necessary comp anion to quantitative testing. Although the early efforts were decidedly small-scale, that did not prevent the huge Zappos vision from being realized.
In faet, in Zappos was acquired by the e-commerce giant Amazon. Corporate guidelines encourage every employee to spend up to four hours a month of company time volunteering in his or her community; that volunteer work could take the form of any DhilanthroDic effort: Daintine fences.
A designer could help a nonprofit with a new website design. A team of engineers could wire a school for Internet access. Most of the volunteering has been of the low- impact variety, involving manual labor, even when the volunteers were highly trained experts. This is the kind of corp orate initiative undertaken every day at companies around the world. On the surface it seems to be suited to traditional management and planning.
However, I hope the discussion in Chapter 2 has prompted you to be a little suspicious. Most important, how much does she really know about how to change the behavior of hundreds of thousand people in more than countries? Looked at that way, her plan seems full of untested assumptions — and a lot of vision. In accordance with traditional management practices, Barlerin is spending time planning, getting buy-in from various departments and other managers, and preparing a road map of initiatives for the first eighteen months of her project.
She also has a strong accountability framework with metrics for the impact her project should have on the company over the next four years.
Like many entrep reneurs, she has a business plan that lays out her intentions nicelv. Yet desDite all that work. Perhaps longtime employees would feel a desire to reaffirm their values of giving back to the community by volunteering. A second assumption could be that they would find it more satisfying and therefore more sustainable to use their actual workplace skiils in a volunteer capacity, which would have a greater impact on behalf of the organizations to which they donated their time.
The Lean Startup model offers a way to test these hypotheses rigorously, immediately, and thoroughly. Strategic planning takes months to complete; these experiments could begin immediately. By starting small, Caroline could prevent a tremendous amount of waste down the road without compromising her overall vision.
Break It Down The first step would be to break down the grand vision into its component parts. The two most important assumptions entrepreneurs make are what I call the value hypothesis and the growth hypothesis.
The value hypothesis tests whether a product or service really delivers value to customers once they are using it. We could survey them to get their opinion, but that would not be very accurate because most people have a hard time assessing their feelines obiectivelv.
Experiments provide a more accurate gauge. What could we see in real time that would serve as a proxy for the value participants were gaining from volunteering? We could find opportunities for a small number of employees to volunteer and then look at the retention rate of those employees.
How many of them sign up to volunteer again? When an employee voluntarily invests their time and attention in this program, that is a strong indicator that they find it valuable. For the growth hypothesis, which tests how new customers will discover a product or service, we can do a similar analysis. Once the program is up and running, how will it spread among the employees, from initial early adopters to mass adoption throughout the company? A likely way this program could expand is through viral growth.
If that is true, the most important thing to measure is behavior: In this case, a simple experiment would involve taking a very small number — a dozen, perhaps — of existing long-term employees and providing an exceptional volunteer opportunity for them. The point is not to find the average customer but to find early adopters: Those customers tend to be more forgiving of mistakes and are especially eager to give feedback.
Next, using a technique I call the concierge minimum viable product described in detail in Chapter 6 , Caroline could make sure the first few participants had an experience that was as good as she could make it, completely aligned with her vision.
Unlike in a focus group, her goal would be to measure what the customers actually did. For example, how many of the first volunteers actually complete their volunteer assignments?
How many volunteer a second time? How manv are willing to recruit a colleaeue to participate in a subsequent volunteer activity? Additional experiments can expand on this early feedback and learning.
For example, if the growth model requires that a certain percentage of participants share their experiences with colleagues and encourage their participation, the degree to which that takes place can be tested even with a very small sample of people. If ten people complete the first experiment, how many do we expect to volunteer again? If they are asked to recruit a colleague, how many do we expect will do so?
Remember that these are supposed to be the kinds of early adopters with the most to gain from the program. Put another way, what if all ten early adopters decline to volunteer again? That would be a highly significant — and very negative — result. We already have a cohort of people to talk to as well as knowledge about their actual behavior: This entire experiment could be conducted in a matter of weeks, less than one-tenth the time of the traditional strategic planning process. Also, it can happen in parallel with strategic planning while the plan is still being formulated.
Even when experiments produce a negative result, those failures prove instructive and can influence the strategy. For example, what if no volunteers can be found who are experiencing the conflict of values within the organization that was such an important assumption in the business plan? If so, congratulations: If this or any other experiment is successful, it allows the manager to get started with his or her campaign: 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. To see this in action, consider an example from Kodak.
Do consumers recognize that they have the problem you are trying to solve? If there was a solution, would they buy it? Would they buy it from us? Can we build a solution for that problem? For example, Kodak Gallery offered wedding cards with gilded text and graphics on its site. Those designs were popular with customers who were getting married, and so the team redesigned the cards to be used at other special occasions, such as for holidays.
The market research and design Drocess indicated that customers would like the new cards. They were also hard to produce. Cook realized that they had done the work backward. In a break with the past, Cook led the group through a process of identifying risks and assumptions before building anything and then testing those assumptions experimentally.
There were two main hypotheses underlying the proposed event album: The team assumed that customers would want to create the albums in the first place. It assumed that event participants would upload photos to event albums created by friends or colleagues. The Kodak Gallery team built a simple prototype of the event album. It lacked many features — so many, in faet, that the team was reluctant to show it to customers. However, even at that early stage, allowing customers to use the prototype helped the team refute their hypotheses.
First, creating an album was not as easy as the team had predicted; none of the early customers were able to create one. Further, customers complained that the early product version lacked essential features. Those negative results demoralized the team. The usabilitv problems frustrated them, as did customer complains about missing features, many of which matched the original road map.
Cook explained that even though the product was missing features, the project was not a failure. The initial product — flaws and all — confirmed that users did have the desire to create event albums, which was extremely valuable information.
Where customers complained about missing features, this suggested that the team was on the right track. The team now had early evidence that those features were in faet important. Through a beta launch the team continued to leam and iterate. While the early users were enthusiastic and the numbers were promising, the team made a major discovery.
Through the use of Online surveying tool KISSinsights, the team leamed that many customers wanted to be able to arrange the order of pictures before they would invite others to contribute.
In a world where marketing launch dates were often set months in advance, waiting until the team had really solved the problem was a break from the past. This process represented a dramatic change for Kodak Gallery; employees were used to being measured on their progress at completing tasks.
Dhobis take the clothes to the nearest river, wash them in the river water. The result? Clothes are returned in about ten days and are probably not that clean. As the brand manager of the Tide and Pantene brands for India and ASEAN countries, he thought he could make laundry services available to people who previously could not afford them. VLS began a series of experiments to test its business assumptions. For their first experiment, VLS mounted a consumer-grade laundry machine on the back of a pickup truck parked on a Street corner in Bangalore.
The entrepreneurs did not clean the laundry on the truck, which was more for marketing and show, but took it off-site to be cleaned and brought it back to their customers by the end of the day. The VLS team continued the experiment for a week, parking the truck on different Street comers, digging deeper to discover all they could about their potential customers.
They wanted to know how they could encourage people to come to the truck. Did cleaning speed matter? Was cleanliness a concern? What were people asking for when they left their laundry with them? They discovered that customers were happy to give them their laundry to clean. However, those customers were suspicious of the washing machine mounted on the back of the truck, concerned that VLS would take their laundry and run. To address that concern, VLS created a slightly more substantial mobile cart that looked more like a kiosk.
VLS also experimented with parking the carts in front of a local minimarket chain. Further iterations helped VLS figure out which services people were most interested in and what price they were willing to pay. They discovered that customers often wanted their clothes ironed and were willing to pay double the price to get their laundry back in four hours rather than twenty-four hours. As a result of those earlv experiments. VLS created an end product that was a three-foot by four-foot mobile kiosk that included an energy-efficient, consumer-grade washing machine, a dryer, and an extra-long extension cord.
The kiosk used Western detergents and was sup plied daily with fresh clean water delivered by VLS. Since then, the Village Laundry Service has grown substantially, with fourteen locations operational in Bangalore, Mysore, and Mumbai.
And almost 60 percent of the business is coming from repeat customers. We have serviced more than 10, customers in the past year alone across all the outlets. This agency is tasked with protecting American citizens from predatory lending by financial services companies such as credit card companies, student lenders, and payday loan offices.
The plan calls for it to accomplish this by setting up a call center where trained case workers will held calls direcdy from the public. Left to its own devices, a new govemment agency would probably hire a large staff with a large budget to develop a plan that is expensive and time-consuming. However, the CFPB is considering doing things differendy. President Obama tasked his chief technology officer, Aneesh Chopra, with collecting ideas for how to set up the new startup agency, and that is how I came to be involved.
In Darticular. My suggestion was drawn straight from the principles of this chapter: Using these insights, we could build a minimum viable product and have the agency up and running — on a micro scale — long before the official plan was set in motion. The number one assumption underlying the current plan is that once Americans know they can call the CFPB for help with financial fraud and abuse, there will be a significant volume of citizens who do that.
This sounds reasonable, as it is based on market research about the amount of fraud that affects Americans each year. However, despite all that research, it is still an assumption. If the actual call volume differs markedly from that in the plan, it will require significant revision. What if they have very different notions of what problems are important?
What if they call the agency seeking help for problems that are outside its purview? To start experimenting immediately, the agency could start with the creation of a simple hotline number, using one of the new breed of low-cost and fast setup platforms such as Twilio. In the first version, the prompts could be drawn straight from the existing research. Instead of a caseworker on the line, each prompt could offer the caller useful information about how to solve her or his problem.
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Instead of marketing this hotline to the whole country, the agency could run the experiment in a much more limited way: Flyers on billboards, newspaper advertisements to those blocks, or specially targeted Online ads would be a good start. Since the target area is so small, they could afford to pay a premium to create a high level of awareness in the target zone.
The total cost would remain quite small. But it is also not very expensive. This product could be built in a matter of days or weeks, and the whole experiment probably would cost only a few thousand dollars.
What we would learn from this experiment would be invaluable. The agency could begin to test marketing messages: What motivates people to call? It could start to extrapolate real-world trends: What percentage of people in the target area actually call? The extrapolation would not be perfect, but it would establish a baseline behavior that would be far more accurate than market research.
Most important, this product would serve as a seed that could germinate into a much more elaborate service. With this beginning, the agency could engage in a continuous process of improvement, slowly but surely adding more and better solutions.
Eventually, it would staff the hotiine with caseworkers, perhaps at first addressing only one category of problems, to give the caseworkers the best chance of success. By the time the official plan was ready for implementation, this early service could serve as a real-world template. The CFPB is just getting started, but already they are showing signs of following an experimental approach.
For example, instead of doing a geographically limited rollout, they are segmenting their first products by use case. They have established a preliminary order of financial products to provide consumer services for, with credit cards coming first.
As their first exoeriment unfolds. This data will influence the depth, breadth, and sequence of future offerings. We have an opportunity to closely monitor what the public is telling us and react to new information.
Markets change all the time and our job is to change with them. In many cases, they are in the midst of building an organization in a way consistent with the best practices of current management thinking.
They face the same challenges in both the public and private sectors, regardless of industry.
Their challenge is to overcome the prevailing management thinking that puts its faith in well-researched plans. Remember, planning is a tool that only works in the presence of a long and stable operating history.
And yet, do any of us feel that the world around us is getting more and more stable every day? Changing such a mind-set is hard but critical to startup success. My hope is that this book will help managers and entrepreneurs make this change. As customers interact with those products, they generate feedback and data.
As we saw in Part One, the products a startup builds are really experiments; the leaming 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. We can visualize this three-step process with this simple diagram: In Part Two, we will examine it in great detail. Many people have professional training that emphasizes one element of this feedback loop.
Some managers are experts at strategizing and learning at the whiteboard. Plenty of entrepreneurs focus their energies on the individual nouns: 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.
This is the essence of steering a startup and is the subject of Part Two. We will walk through a complete turn of the Build-Measure-Learn feedback loop, discussing each of the components in detail.
The purpose of Part One was to explore the importance of learning as the measure of orogress for a startuD. As I hope is evident by now, by focusing our energies on validated learning, we can avoid much of the waste that plagues startups today. As in lean manufacturing, learning where and when to invest energy results in saving time and money. To apply the scientific method to a startup, we need to identify which hypotheses to test.
The two most important assumptions are the value hypothesis and the growth hypothesis. Each iteration of a startup is an attempt to rev this engine to see if it will tum. 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 tum of the Build-Measure-Leam 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: 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. When we enter the Measure phase, the biggest challenge will be determining whether the product development efforts are leading to real progress.
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 obiectivelv: Upon completing the Build-Measure-Learn loop, we confront the most difficult question any entrepreneur faces: Although we write the feedback loop as Build-Measure-Learn because the activities happen in that order, our planning really works in the reverse order: All of the techniques in Part Two are designed to minimize the total time through the Build-Measure-Learn feedback loop.
It was live on a handful of college campuses. It was not the market-leading social network or even the first college social network; other companies had launched sooner and with more features. Their story is now world famous. More than half of the users came back to the site every single day. The rate of growth was staggering: In other words, Facebook also had validated its growth hypothesis.
These two hypotheses represent two of the most important leap-of-faith questions any new startup faces. Facebook was different, because it employed a different engine of growth. It paid nothing for customer acquisition, and its high engagement meant that it was accumulating massive amounts of customer attention every day.
There was never any question that attention would be valuable to advertisers; the only question was how much they would pay. Many entrepreneurs are attempting to build the next Facebook, yet when they try to apply the lessons of Facebook and other famous startup success stories, they quickly get confused. Is the lesson of Facebook that startups should not charge customers money in the early days? Or is it that startups should never spend money on marketing?
These questions cannot be answered in the abstract; there are an almost infinite number of counterexamples for any technique. Instead, as we saw in Part One, 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 traditional business strategy excels at is helping managers identify clearly what assumptions are being made in a particular business. The first challenge for an entrep reneur is to build an organization that can test these assumptions systematically.
Many assumptions in a typical business plan are unexceptional. These are well-established facts drawn from past industry experience or straightforward deductions. Hidden among these mundane details are a handful of assumptions that require more courage to State — in the present tense — with a straight face: Acting as if these assumptions are true is a classic entrepreneur superpower. They are called leaps of faith precisely because the success of the entire venture rests on them.
If they are true, tremendous opportunity awaits. If they are false, the startup risks total failure. Most leaps of faith take the form of an argument by analogy. For example, one business plan I remember argued as follows: Previous technology X was used to win market Y because of attribute Z.
We have a new technology X2 that will enable us to win market Y2 because we too have attribute Z. The problem with analogies like this is that they obscure the true leap of faith. That is their goal: They are used to persuade investors, employees, or partners to sign on.
Most entrepreneurs would cringe to see their leap of faith written this way: Large numbers of people already wanted access to the World Wide Web. They knew what it was, they could afford it, but they could not get access to it because the time it took to load images was too long. When progressive image loading was introduced, it allowed people to get onto the World Wide Web and tell their friends about it.
Thus, company X won market Y. Similarly, there is already a large number of potential customers who want access to our product right now. They know they want it, they can afford it, but they cannot access it because the rendering is too slow.
When we debut our product with progressive rendering technology, they will flock to our software and tell their friends, and we will win market Y2. There are several things to notice in this revised statement. Is it really true that progressive image loading caused the adoption of the World Wide Web, or was this just one factor among many?
More important, is it really true that there are large numbers of potential customers out there who want our solution right now? The restated approach should make clear that what is needed is to do some empirical testing first: Analogs and Antilogs There is nothing intrinsically wrong with basing strategy on comparisons to other companies and industries. In faet, that approach can help you discover assumptions that are not really leaps of faith.
He explains the analog-antilog concept by using the iPod as an example. Will people listen to music in a public place using earphones? We think of that as a nonsense question today, but it is fundamental. When Sony asked the question, they did not have the answer. Jobs then had to face the faet that although people were willing to download music, they were not willing to pay for it.
Those are leaps of faith that I, as an entrepreneur, am taking if I go through with this business venture. They are going to make or break my business. In the iPod business, one of those leaps of faith was that people would pay for music. However, for every successful entrepreneur who was in the right place in the right time, there are many more who were there, too, in that right place at the right time but still managed to fail. Henry Ford was joined by nearly five hundred other entrepreneurs in the early twentieth century.
Imagine being an automobile entrepreneur, trained in state-of-the-art engineering, on the ground floor of one of the biggest market opportunities in history. Yet the vast majority managed to make no money at all. What differentiates the success stories from the failures is that the successful entrep reneurs 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.
Value and Growth As we saw in the Facebook story, two leaps of faith stand above all others: The first step in understanding a new product or service is to figure out if it is fundamentally value-creating or value-destroying. I use the language of economics in referring to value rather than profit, because entrepreneurs include people who start not-for-profit social ventures, those in public sector startups, and internal change agents who do not judge their success by profit alone.
Even more confusing, there are many organizations that are wildly profitable in the short term but ultimately value-destroying, such as the organizers of Ponzi schemes, and fraudulent or misguided companies e.
A similar thing is true for growth. There are many value-destroying kinds of growth that should be avoided. An example would be a business that grows through continuous fund-raising from investors and lots of paid advertising but does not develop a value-creating product.
Such businesses are engaged in what I call success theater, using the appearance of growth to make it seem that they are successful.
One of the goals of innovation accounting, which is discussed in depth in Chapter 7, is to help differentiate these false startups from true innovators. Traditional accounting judges new ventures by the same standards it uses for established comoanies. Consider companies such as Amazon. Like its traditional counterpart, innovation accounting requires that a startup have and maintain a quantitative financial model that can be used to evaluate progress rigorously.
At Toyota, this goes by the Japanese term genchi gembutsu, which is one of the most important phrases in the lean manufacturing vocabulary. In my Toyota interviews, when I asked what distinguishes the Toyota Way from other management approaches, the most common first response was genchi gembutsu — whether I was in manufacturing, product development, sales, distribution, or public affairs.
You cannot be sure you really understand any part of any business problem unless you go and see for yourself firsthand. It is unacceptable to take anything for granted or to rely on the reports of others.
At Toyota, the manager responsible for the design and development of a new model is called the chief engineer, a cross-functional leader who oversees the entire process from concept to production. To figure out how to improve the minivan, he proposed an audacious entrepreneurial undertaking: States, all thirteen provinces and territories of Canada, and all parts of Mexico.
In all, he logged more than 53, miles of driving. In small towns and large cities, Yokoya would rent a current-model Sienna, driving it in addition to talking to and observing real customers.
From those firsthand observations, Yokoya was able to start testing his critical assumptions about what North American consumers wanted in a minivan. It is common to think of selling to consumers as easier than selling to enterprises, because customers lack the complexity of multiple departments and different people playing different roles in the purchasing process. Yokoya discovered this was untrue for his customers: If I learned anything in my travels, it was the new Sienna would need kid appeal.
EntreDreneurs face a different set of challenges because they operate with much higher uncertainty. 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. Even when one is selling to large institutions, as in a business-to-business model, it helps to remember that those businesses are made up of individuals.
All successful sales models depend on breaking down the monolithic view of organizations into the disparate people that make them up. The first step in this process is to confirm that your leap-of-faith questions are based in reality, that the customer has a significant problem worth solving. Instead, he picked up two phone books: Calling people at random, he inquired if he could ask them a few auestions about the wav thev manaeed their finances.
Those earlv conversations were designed to answer this leap-of-faith question: It turned out that they did, and this early validation gave Cook the confirmation he needed to get started on a solution. Those early conversations were with mainstream customers, not early adopters. Still, the conversations yielded a fundamental insight: Design and the Customer Archetype 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 many techniques for building an accurate customer archetype that have been developed over long years of practice in the design community. Traditional approaches such as interaction design or design thinking are enormously helpful. Yet because of the way design agencies traditionally have been compensated, all this work culminates in a monolithic deliverable to the Client.
All of a sudden. For startups, this is an unworkable model. No amount of design can anticipate the many complexities of bringing a product to life in the real world. In faet, a new breed of designers is developing brand-new techniques under the banner of Lean User Experience Lean UX. They recognize that the customer archetype is a hypothesis, not a faet. The customer profile should be considered provisional until the strategy has shown via validated learning that we can serve this type of customer in a sustainable way.
Other entrepreneurs can fail victim to analysis paralysis, endlessly refining their plans. In this case, talking to customers, reading research reports, and whiteboard strategizing are all equally unhelpful. Unfortunately, most of these errors cannot be detected at the whiteboard because they depend on the subtle interactions between products and customers.
If too mueh analysis is dangerous but none can lead to failure, how do entrepreneurs know when to stop analyzing and start building? The answer is a concept called the minimum viable product, the subject of Chapter 6. Although they still had grand ambitions, they were determined to keep the new product simple. They built a minimum viable product. Does this sound like a billion-dollar company to you?
Mason tells the story: We took a WordPress Blog and we skinned it to say Groupon and then every day we would do a new post. It was totally ghetto. We would seil T-shirts on the first version of Groupon. If you want a different color or size. It was just so cobbled together. It was enough to pro ve the concept and show that it was something that people really liked. The actual coupon generation that we were doing was all FileMaker. We would run a script that would e-mail the coupon PDF to people.
Really until July of the first year it was just a scrambling to grab the tiger by the tail. It was trying to catch up and reasonably piece together a product. It is revolutionizing the way local businesses find new customers, offering special deals to consumers in more than cities worldwide.
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.
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. First of all, our product was still buggy and low- quality.
The good news was that we were on a hockey-stick-shaped growth curve. Are those numbers in thousands? IMVU was providing value for customers, and we had a working engine of growth. The gross numbers were small because we were selling the product to visionary early customers called early adopters.
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. Early adopters use their imagination to fill in what a product is missing.
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