The Lean Startup Summary

In his book The Lean Startup, Eric Ries introduced the build-measure-learn feedback loop as a guide for modern startups. Some of the key takeaways of his approach are as follows.

Introduction


Lean startup method:

1. Entrepreneurs are everywhere
2. Entrepreneurship is management specifically geared to its context of extreme uncertainty
3. Validated learning
4. Build-measure-learn
5. Innovation accounting

Why startups fail?

1. Allure of a good plan, a solid strategy and a thorough market research. They don’t apply in startups due to too much uncertainty. Planning and forecasting are only accurate when based on a long, stable operating history and a relatively static environment. Startups have neither
2. Adoption of ‘just do it’ approach. If management is the problem, chaos is the answer. And that doesn’t work either

Vision


1. Start

– We like to code all day to call it productive. The Lean Startup asks people to start measuring their productivity differently. Because startups often accidentally build something nobody wants, it doesn’t matter much if they do it on time and on budget. The goal of a startup is to figure out the right thing to build — the thing customers want and will pay for — as quickly as possible. In other words, the Lean Startup is a new way of looking at the development of innovative new products that emphasizes fast iteration and customer insight, a huge vision, and a great ambition, all at the same time
– We know the route very well while driving but we can’t describe every push of hand on wheel and foot on pedal. By contrast, a rocket ship requires just this kind of 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
– Entrepreneurship is like driving: Vision (rarely changes), strategy (may have to change) and product (continuously changing)

2. Define

– A startup is a human institution designed to create a new product or service under conditions of extreme uncertainty
– To open up a new business that is an exact clone of an existing business all the way down to the business model, customers, pricing, and product maybe an attractive economic investment but it is not a startup because its success depends only on execution — so much so that this success can be modeled with high accuracy (this is why so many small businesses can be financed with simple bank loans; the level of risk and uncertainty is understood well enough that a loan officer can assess its prospects)

3. Learn

– Learning is the oldest excuse in the book for a failure of execution. It’s what managers fall back on when they fail to achieve the promised results. Yet if the fundamental goal of entrepreneurship is to engage in organization building under conditions of extreme uncertainty, its most vital function is learning. We must learn the truth which elements of our strategy are working to realize our vision and which are just crazy. We must learn what customers really want, not what they say they want or what we think that they should want. We must discover whether we are on a path that will lead to growing a sustainable business. That’s where validated learning comes in
– Validated learning is a rigorous method for demonstrating progress empirically that a team has discovered valuable truths about a startup’s present and future business prospects. The effort that is not absolutely necessary for learning what customers want can be eliminated. It is the lethal antidote to the lethal problem of achieving failure: successfully executing a plan that leads nowhere
– Customers can’t tell what they want. They however reveal the truth through their actions or inactions
– Value vs waste: Lean thinking defines value as providing benefit to the customer; anything else is waste
– Productivity in a startup: not in terms of how much stuff is being built but in terms of how much validated learning is coming for the efforts

4. Experiment

– This is one of the most important lessons of the scientific method: if you cannot fail, you cannot learn
– 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
– An experiment is more than just a theoretical inquiry; it is also a first product
– Instead of ‘I am going to build this’, answer the following questions:
– Do consumers recognize that they have the problem you are trying to solve
– If there was a solution, would they buy it
– Would they buy it from us
– Can we build a solution for that problem

Steer


Although the feedback loop is written as build-measure-learn because 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

5. Leap

– Leap-of-faith assumptions: value hypothesis and growth hypothesis
– Genchi gembutsu: go and see for yourself (Japanese Toyota way)
– The facts 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

6. Test

– Minimum viable product (MVP): when in doubt, simplify
– Dropbox CEO made a video explaining the features, and thousands of people signed for beta waiting list, validating their leap-of-faith assumption that customers wanted the product they were developing
– Product development should always focused on scaling something that is working rather than trying to invent something that might work in the future
– MVP – if we do not know who the customer is, we do not what the quality is
– Customers don’t care how much time something takes to build. They care only if it serves their needs
– About big companies stealing your ideas, try letting their relevant managers steal your idea. The truth is that most managers in most companies are already overwhelmed with good ideas. Their challenge lies in prioritization and execution, and it is those challenges that give a startup hope of surviving
– 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 successful startup will face competition from fast followers. A head start is rarely large enough to matter and time spent in stealth mode — away from competitors — is unlikely to provide a head start. The only way to win is to learn faster than anyone else

7. Measure

– Myth of perseverance is 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
– Innovation accounting: (a) establish the baseline, (b) tuning the engine, and (c) pivot or persevere
– 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 hypothesis. 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
– Vanity metrics: gross number of customers, etc. Actionable metrics: clear cause and effect relationship between added features and customer response/feedback
– That which optimizes one part of the system necessarily undermines the system as a whole
– Superficial aspects of Lean Startup learning milestones: shipping an early product and establishing baseline metrics, relatively short iterations, each of which is judged by its ability to improve customer metrics. Examples of wrong metrics: types of metrics changing in every cycle like gross usage numbers in one month, and registration numbers in another. Then, metrics will seem to go up and down on their own and drawing clear cause and effect inferences will not be possible. Right questions to ask: when we shipped feature X, did it affect customer behavior (that requires tremendous time and effort)? When exactly did feature X ship? Which customers were exposed to it? Was anything launched around that same time? Were there seasonal factors that might be skewing the data?
– Cohorts and split-tests: instead of looking at gross metrics, switch to cohort-based metrics, and instead of looking for cause-and-effect relationships after the fact, launch each new feature as a true split-test experiment
– Metrics are people, too: actionable (clear cause-and-effect), accessible (in easy to understand form) and auditable (talking to customers)

8. Pivot (or Persevere)

– Pivot is a structured course correction designed to test a new fundamental hypothesis about the product, strategy and engine of growth
– The faster you execute the strategy, the earlier you come to the crossroad of pivot or persevere and the better the chances of success
– A pivot requires that we keep one foot rooted in what we’ve learned so far, while making a fundamental change in strategy in order to seek even greater validated learning
– Runway: the amount of time in which a startup must either achieve lift-off or fail
– 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
– The rationale for building low-quality MVPs is that developing any features beyond what early adopters require is a form of waste. Once you have found success with early adopters, you want to sell to mainstream customers. Mainstream customers have different requirements and are much more demanding
– Once successful early, company has to keep changing to adapt to mainstream customers

Accelerate


Value in a startup is not the creation of stuff, but rather validated learning about how to build a sustainable business. What products do customers really want? How will our business grow? Who is our customer? Which customers should we listen to and who should we ignore?

9. Batch

– The envelopes example
– Process is only the foundation upon which a great company culture can develop

10. Grow

– Sustainable growth is characterized by one simple rule: New customers come from the actions of past customers
– The 3 engines of growth: sticky, viral and paid
– It is important to use actionable metrics to evaluate the progress. However, this leaves a large amount of variety in terms of which numbers one should measure. I fact, one of the most expensive forms of potential waste for a startup is spending time arguing about how to prioritize new development once it has a product on the market. At any time, the company could invest its energy in finding new customers, servicing existing customers better, improving over all quality, or driving down costs
– Engines of growth are designed to give startups a relatively small set of metrics on which to focus their energies. Startups don’t starve; they drown. There are always a zillion new ideas about how to make the product better floating around, but the hard truth is that most of those ideas make a difference only at margins. They are mere optimizations. Startups have to focus on big experiments that lead to validated learning. Engines of growth framework helps them stay focused on the metrics that matter

11. Adapt

– The key to andon cord is that it brings work to a stop as soon as an uncorrectable quality problem surfaces — which forces it to be investigated. This is one of the most important discoveries of the lean manufacturing movement: you cannot trade quality for time. If you are causing (or missing) quality problems now, the resulting defects will slow you down later
– On the one hand, the logic of validated learning and the minimum viable product says that we should get a product into customers’ hands as soon as possible and that any extra work we do beyond what is required to learn from customers is waste. On the other hand, the Build-Measure-Learn feedback loop is a continuous process. We don’t stop after one MVP but use what we have learned to get to work immediately on the next iteration. Therefore, shortcuts taken in product quality, design, or infrastructure today my wind up slowing a company down tomorrow
– When confronted with a problem, utilize the principle of 5 Whys
– 2 simple rules: (I) be tolerant of all mistakes the first time, and (II) never allow the same mistake to be made twice

12. Innovate

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

13. Epilogue: Waste Not

– We have the capacity to build almost anything we can imagine. The big question of our time is not ‘Can it be built?’ but ‘Should it be built?’ Our future prosperity depends on the quality of our collective imagniations
– There is surely nothing quite so useless as doing with great efficiency what should not be done at all

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