The book that changed the entrepreneurial world. It teaches you not only how to start a tech company, but most importantly it teaches you how to think. A must read for any entrepreneur, tech or not.
Author: Eric Ries
Finished on: 28/06/2012
Here’s a link to the Amazon page.
A startup is a human institution designed to create a new product or service under conditions of extreme uncertainty.
Validated learning is not after-the-fact rationalization or a good story designed to hide failure. It is a rigorous method for demonstrating progress when one is embedded in the soil of extreme uncertainty in which startups grow. Validated learning is the process of demonstrating empirically that a team has discovered valuable truths about a startup’s present and future business prospects. It is more concrete, more accurate, and faster than market forecasting or classical business planning. It is the principal antidote to the lethal problem of achieving failure: successfully executing a plan that leads nowhere.
Lean thinking defines value as providing benefit to the customer; any thing else is waste. In a manufacturing business, customers don’t care how the product is assembled, only that it works correctly. 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 Audacity of Zero
The irony is that it is often easier to raise money or acquire other resources when you have zero revenue, zero customers, and zero traction than when you have a small amount. Zero in vites imagination, but small numbers invite questions about whether large numbers will ever materialize. Everyone knows (or thinks he or she knows) stories of products that achieved breakthrough success overnight. As long as nothing has been released and no data have been collected, it is still possible to imagine overnight success in the future. Small numbers pour cold water on that hope.
This phenomenon creates a brutal incentive: postpone getting any data until you are certain of success. Of course, as we’ll see, such delays have the unfortunate effect of increasing the amount of wasted work, decreasing essential feedback, and dra matically increasing the risk that a startup will build something nobody wants. However, releasing a product and hoping for the best is not a good plan either, because this incentive is real.
We can mitigate the waste that happens because of the audacity of zero with validated learning. What we needed to demonstrate was that our product development efforts were leading us toward massive success without giving in to the temptation to fall back on vanity metrics and “success theater”—the work we do to make ourselves look successful. 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.
The two most important assumptions entrepreneurs make are what I call the value hypothesis and the growth hypothesis.
The value hypothesis tests whether a product or service really delivers value to customers once they are using it. What’s a good indicator that employees find donating their time valuable? We could survey them to get their opinion, but that would not be very accurate because most people have a hard time assessing their feelings objectively.
Experiments provide a more accurate gauge. What could we see in real time that would serve as a proxy for the value participants were gaining from volunteering? We could find opportunities for a small number of employees to volunteer and then look at the retention rate of those employees. How many of them sign up to volunteer again? When an employee voluntarily invests their time and attention in this program, that is a strong indicator that they find it valuable.
For the growth hypothesis, which tests how new customers will discover a product or service, we can do a similar analysis. Once the program is up and running, how will it spread among the employees, from initial early adopters to mass adoption throughout the company? A likely way this program could expand is through viral growth. If that is true, the most important thing to measure is behavior: would the early participants actively spread the word to other employees?
In this case, a simple experiment would involve 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: the customers who feel the need for the product most acutely. Those customers tend to be more forgiving of mistakes and are especially eager to give feedback. Even when experiments produce a negative result, those failures prove instructive and can influence the strategy.
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).
We can visualize this three-step process with the Build-Measure-Learn feedback loop, which is at the core of the Lean Startup model.
To apply the scientific method to a startup, we need to identify which hypotheses to test. I call the riskiest elements of a startups 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 startups engine of growth. Each iteration of a startup is an attempt to rev this engine to see if it will turn. Once it is running, the process repeats, shifting into higher and higher gears.
Once clear on these leap-of-faith assumptions, the first step is to enter the Build phase as quickly as possible with a minimum viable product (MVP). The MVP is that version of the product that enables a full turn of the Build-Measure-Learn loop with a minimum amount of effort and the least amount of development time. The minimum viable product lacks many features that may prove essential later on. However, in someways, 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 were 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.
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.
The Lean Startup method builds capital-efficient companies because it allows startups to recognize that it’s time to pivot sooner, creating less waste of time and money. 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: we figure out what we need to learn, use innovation accounting to figure out what we need to measure to know if we are gaining validated learning, and then figure out what product we need to build to run that experiment and get that measurement.
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.
Many assumptions in a typical business plan are unexceptional. These are well-established facts drawn from past industry experience or straightforward deductions. In Facebook’s case, it was clear that advertisers would pay for customers’ attention. Hidden among these mundane details are a handful of assumptions that require more courage to state—in the present tense—with a straight face: we assume that customers have a significant desire to use a product like ours, or we assume that supermarkets will carry our product. Acting as if these assumptions are true is a classic entrepreneur superpower. They are called leaps of faith precisely because the success of the entire venture rests on them. If they are true, tremendous opportunity awaits. If they are false, the startup risks total failure.
Getting Out of the Building
As Steve Blank has been teaching entrepreneurs for years, the facts that we need to gather about customers, markets, suppliers, and channels exist only “outside the building.” Startups need extensive contact with potential customers to understand them, so get out of your chair and get to know them.
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.
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. 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.
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. 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.
In a concierge MVP, this personalized service is not the product but a learning activity designed to test the leap-of-faith assumptions in the company’s growth model. In fact, a common outcome of a concierge MVP is to invalidate the company’s proposed growth model, making it clear that a different approach is needed. This can happen even if the initial MVP is profitable for the company. Without a formal growth model, many companies get caught in the trap of being satisfied with a small profitable business when a pivot (change in course or strategy) might lead to more significant growth. The only way to know is to have tested the growth model systematically with real customers.
Most modern business and engineering philosophies focus on producing high-quality experiences for customers as a primary principle; it is the foundation of Six Sigma, lean manufacturing, design thinking, extreme programming, and the software craftsmanship movement.
These discussions of quality presuppose that the company already knows what attributes of the product the customer will perceive as worthwhile. In a startup, this is a risky assumption to make. Often we are not even sure who the customer is. Thus, for startups, I believe in the following quality principle: “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 care about. This is infinitely better than mere speculation or whiteboard strategizing, because it provides a solid empirical foundation on which to build future products.
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. But once again, this does not mean operating in a sloppy or undisciplined way.
Building an MVP
As you consider building your own minimum viable product, let this simple rule suffice: remove any feature, process, or effort that does not contribute directly to the learning you seek.
The MVP is just the first step on a journey of learning. Down that road—after many iterations—you may learn that some element of your product or strategy is flawed and decide it is time to make a change, which I call a pivot, to a different method for achieving your vision.
A Startup’s Job
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 out comes for a startup is to bumble along in the land of the living dead.
Innovation Accounting (definition)
Innovation accounting enables startups to prove objectively that they are learning how to grow a sustainable business. Innovation accounting begins by turning the leap-of-faith assumptions into a quantitative financial model. Every business plan has some kind of model associated with it, even if it’s written on the back of a napkin. That model provides assumptions about what the business will look like at a successful point in the future.
Innovation Accounting (steps)
Innovation accounting works in three steps: first, use a minimum viable product to establish real data on where the company is right now. Without a clear-eyed picture of your current status—no matter how far from the goal you may be—you can not begin to track your progress.
Second, startups must attempt to tune the engine from the baseline toward the ideal. This may take many attempts. After the startup has made all the micro changes and product optimizations it can to move its baseline toward the ideal, the company reaches a decision point. That is the third step: pivot or persevere.
When a company pivots, it starts the process all over again, reestablishing 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.
These MVPs provide the first example of a learning milestone. 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.
When one is choosing among the many assumptions in a business plan, it makes sense to test the riskiest assumptions first. If you can’t find a way to mitigate these risks toward the ideal that is required for a sustainable business, there is no point in testing the others.
Once the baseline has been established, the startup can work toward the second learning milestone: 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.
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.
This is one of the most important tools of startup analytics. Although it sounds complex, it is based on a simple premise. Instead of looking at cumulative totals or gross numbers such as total revenue and total number of customers, one looks at the performance of each group of customers that comes into contact with the product independently. Each group is called a cohort.
This technique is useful in many types of business, because every company depends for its survival on sequences of customer behavior called flows. Customer flows govern the interaction of customers with a company’s products. They allow us to understand a business quantitatively and have much more predictive power than do traditional gross metrics.
The Value of the Three A’s
For a report to be considered actionable, it must demonstrate clear cause and effect. Otherwise, it is a vanity metric. When cause and effect is clearly understood, people are better able to learn from their actions.
First, make the reports as simple as possible so that everyone understands them. Accessibility also refers to widespread access to the reports.
We must ensure that the data is credible to employees. 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.
The real work that determines the success of startups happens during the photo montage. It doesn’t make the cut in terms of the big story because it is too boring. Only 5 percent of entrepreneurship is the big idea, the business model, the whiteboard strategizing, and the splitting up of the spoils. The other 95 percent is the gritty work that is measured by innovation accounting: product prioritization decisions, deciding which customers to target or listen to, and having the courage to subject a grand vision to constant testing and feedback.
A pivot: a structured course correction designed to test a new fundamental hypothesis about the product, strategy, and engine of growth.
Different Kinds of Pivots
In this case, what previously was considered a single feature in a product becomes the whole product.
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 some times 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.
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
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.
In traditional sales terminology, the mechanism by which a company delivers its product to customers is called the sales channel. 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.
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.
Working on Small Batches
The biggest advantage of working in small batches is that quality problems can be identified much sooner. This is the origin of Toyota’s famous andon cord, which allows any worker to ask for help as soon as they notice any problem, such as a defect in a physical part, stopping the entire production line if it cannot be corrected immediately. This is another very counterintuitive practice. An assembly lineworks best when it is functioning smoothly, rolling car after car off the end of the line. The andon cord can interrupt this careful flow as the line is halted repeatedly. However, the benefits of finding and fixing problems faster outweigh this cost. This process of continuously driving out defects has been a win-win for Toyota and its customers. It is the root cause of Toyota’s historichigh quality ratings and low costs.
Building Products with Lean Methodology
Our goal in building products is to be able to run experiments that will help us learn how to build a sustainable business. Thus, the right way to think about the product development process in a Lean Startup is that it is responding to pull requests in the form of experiments that need to be run. As soon as we formulate a hypothesis that we want to test, the product development team should be engineered to design and run this experiment as quickly as possible, using the smallest batch size that will get the job done. Remember that 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: we figure out what we need to learn and then work backwards to see what product will work as an experiment to get that learning. Thus, it is not the customer, but rather our hypothesis about the customer, that pulls work from product development and other functions. Any other work is waste.
Sustainable growth is characterized by one simple rule: New customers come from the actions of past customers.
There are four 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.
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, youmay be influenced to buy that 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 customer 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 designed to be purchased repeatedly either through a subscription plan (acable company) or through voluntary repurchases (groceries or lightbulbs). By contrast, many products and services are intentionally designed as oneime events, such as wedding planning.
Sticky Engine of Growth
The rules that govern the sticky engine of growth are pretty simple: if the rate of new customer acquisition exceeds the churn rate, the product will grow. The speed of growth is determined by what I call the rate of compounding, which is simply the natural growth rate minus the churn rate. Like a bank account that earns compounding interest, having a high rate of compounding will lead to extremely rapid growth—without advertising, viral growth, or publicity stunts.
Viral Engine of Growth
Like the other engines of growth, the viral engine is powered by a feedback loop that can be quantified. It is called the viral loop, and its speed is determined by a single mathematical term called the viral coefficient. The higher this coefficient is, the faster the product will spread. The viral coefficient measures how many new customers will use a product as a consequence of each new customer who signs up. Put another way, how many friends will each customer bring with him or her? Since each friend is also a new customer, he or she has an opportunity to recruit yet more friends.
For a product with a viral coefficient of 0.1, one in every ten customers will recruit one of his or her friends. This is not a sustainable loop. Imagine that one hundred customers sign up. They will cause ten friends to sign up. Those ten friends will cause one additional person to sign up, but there the loop will fizzle out. By contrast, a viral loop with a coefficient that is greater than 1.0 will grow exponentially, because each person who signs up will bring, on average, more than one other person with him or her.
Companies that rely on the viral engine of growth must focus on increasing the viral coefficient more than anything else, because even tiny changes in this number will cause dramatic changes in their future prospects. A consequence of this is that many viral products do not charge customers directly but rely on indirect sources of revenue such as advertising. This is the case because viral products can not afford to have any friction impede the process of signing customers up and recruiting their friends. This can make testing the value hypothesis for viral products especially challenging.
The true test of the value hypothesis is always a voluntary exchange of value between customers and the startup that serves them. A lot of confusion stems from the fact that this exchange can be monetary, as in the case of Tupperware, or non-monetary, as in the case of Facebook. In the viral engine of growth, monetary exchange does not drive new growth; it is useful only as an indicator that customers value the product enough to pay for it.
Paid Engine of Growth
Imagine another pair of businesses. The first makes $1 on each customer it signs up; the second makes $100,000 from each customer it signs up.To predict which company will grow faster, you need to know only one additional thing: how much it costs to sign up a new customer.
Imagine that the first company uses Google AdWords to find new customers online and pays an average of 80 cents each time a new customer joins.The second company sells heavy goods to large companies. Each sale requires a significant time investment from a salesperson and on-site sales engineering to help install the product; these hard costs total up to $80,000 per new customer. Both companies will grow at the exact same rate. Each has the same proportion of revenue (20 percent) available to reinvest in new customer acquisition. If either company wants to increase its rate of growth, it can do so in one of two ways: increase the revenue from each customer or drive down the cost of acquiring a new customer.
That’s the paid engine of growth at work.
Like the other engines, the paid engine of growth is powered by a feedback loop. Each customer pays a certain amount of money for the product over his or her “lifetime” as a customer. Once variable costs are deducted, this usually is called the customer lifetime value (LTV). This revenue can be invested in growth by buying advertising.
Suppose an advertisement costs $100 and causes fifty new customers to sign up for the service. This ad has a cost per acquisition (CPA) of $2.00. In this example, if the product has an LTV that is greater 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). Conversely, if the CPA remains at $2.00 but the LTV falls below $2.00, the company’s growth will slow.
Marc Andreessen, the legendary entrepreneur and investor and one of the fathers of the World Wide Web, coined the term product/market fit to describe the moment when a startup finally finds a wide spread set of customers that resonate with its product: “In a great market—a market with lots of real potential customers—the market pulls product out of the startup. This is the story of search keyword advertising, Internet auctions, and TCP/IP routers. Conversely, in a terrible market, you can have the best product in the world and an absolutely killer team, and it doesn’t matter—you’re going to fail.”
I believe the concept of the engine of growth can put the idea of product/market fit on a more rigorous footing. Since each engine of growth can be defined quantitatively, each has a unique set of metrics that can be used to evaluate whether a startup is on the verge of achieving product/market fit. A startup with a viral coefficient of 0.9 or more is on the verge of success.
A startup can evaluate whether it is getting closer to product/ market fit as it tunes its engine by evaluating each trip through the Build-Measure-Learn feedback loop using innovation accounting. What really matters is not the raw numbers or vanity metrics but the direction and degree of progress.
The Five Whys
The core idea of Five Whys is to tie investments directly to the prevention of the most problematic symptoms. The system takes its name from the investigative method of asking the question “Why?” five times to understand what has happened (the root cause). If you’ve ever had to answer a precocious child who wants to know “Why is the sky blue?” and keeps asking “Why?” after each answer, you’re familiar with it.
At the root of every seemingly technical problem is a human problem. Five Whys provides an opportunity to discover what that human problem might be.
Let me demonstrate how using the Five Whys allowed us to build the employee training system that was mentioned earlier. Imagine that at IMVU we suddenly start receiving complaints from customers about a new version of the product that we have just released.
- A new release disabled a feature for customers. Why? Be cause a particular server failed.
- Why did the server fail? Because an obscure subsystem was used in the wrong way.
- Why was it used in the wrong way? The engineer who used it didn’t know how to use it properly.
- Why didn’t he know? Because he was never trained.
- Why wasn’t he trained? Because his manager doesn’t be lieve in training new engineers because he and his team are “too busy.”
What began as a purely technical fault is revealed quickly to be a very human managerial issue.
The Five Whys approach acts as a natural speed regulator. The more problems you have, the more you invest in solutions to those problems. As the investments in infrastructure or process pay off, the severity and number of crises are reduced and the team speeds up again. With startups in particular, there is a danger that teams will work too fast, trading quality for time in a way that causes sloppy mistakes. Five Whys prevents that, allowing teams to find their optimal pace.
The Five Whys ties the rate of progress to learning, not just execution. Startup teams should go through the Five Whys whenever they encounter any kind of failure, including technical faults, failures to achieve business results, or unexpected changes in customer behavior.
Five Whys is a powerful organizational technique. Some of the engineers I have trained to use it believe that you can derive all the other Lean Startup techniques from the Five Whys. Coupled with working in small batches, it provides the foundation a company needs to respond quickly to problems as they appear, without overinvesting or overengineering.
Creating an Innovation Sandbox
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:
- 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).
- One team must see the whole experiment through from end to end.
- No experiment can run longer than a specified amount of time (usually a few weeks for simple feature experiments, longer for more disruptive innovations).
- No experiment can affect more than a specified number of customers (usually expressed as a percentage of the company’s total mainstream customer base).
- Every experiment has to be evaluated on the basis of a single standard report of five to ten (no more) actionable metrics.
- Every team that works inside the sandbox and every product that is built must use the same metrics to evaluate success.
- 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.
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. Whenever possible, the innovation team should be cross-functional and have a clear team leader, like the Toyota shusa. It should be empowered to build, market, and deploy products or features in the sandbox without prior approval. It should be required to report on the success or failure of those efforts by using standard actionable metrics and innovation accounting.
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.