More on this book
Community
Kindle Notes & Highlights
by
Eric Ries
Read between
September 7 - October 8, 2020
The first problem is the allure of a good plan, a solid strategy, and thorough market research. In earlier eras, these things were indicators of likely success. The overwhelming temptation is to apply them to startups too, but this doesn’t work, because startups operate with too much uncertainty. Startups do not yet know who their customer is or what their product should be. As the world becomes more uncertain, it gets harder and harder to predict the future. The old management methods are not up to the task. Planning and forecasting are only accurate when based on a long, stable operating
...more
This book is divided into three parts: “Vision,” “Steer,” and “Accelerate.”
“Vision” makes the case for a new discipline of entrepreneurial management.
identify who is an entrepreneur, define a startup, and articulate a new way for startups to gauge if they are making prog...
This highlight has been truncated due to consecutive passage length restrictions.
“Steer” dives into the Lean Startup method in detail, showing one major turn through the core Build-Measure-Learn feedback loop.
In “Accelerate,” we’ll explore techniques that enable Lean Startups to speed through the Build-Measure-Learn feedback loop as quickly as possible, even as they scale.
the Lean Startup uses a different unit of progress, called validated learning. With scientific learning as our yardstick, we can discover and eliminate the sources of waste that are plaguing entrepreneurship.
The goal of a startup is to figure out the right thing to build—the thing customers want and will pay for—as quickly as possible.
In other words, the Lean Startup is a new way of looking at the development of innovative new products that emphasizes fast iteration and customer insight, a huge vision, and great ambition, all at the same time.
startup is a human institution designed to create a new product or service under conditions of extreme uncertainty.
learning is cold comfort to employees who are following an entrepreneur into the unknown. It is cold comfort to the investors who allocate precious money, time, and energy to entrepreneurial teams. It is cold comfort to the organizations—large and small—that depend on entrepreneurial innovation to survive. You can’t take learning to the bank; you can’t spend it or invest it. You cannot give it to customers and cannot return it to limited partners. Is it any wonder that learning has a bad name in entrepreneurial and managerial circles?
In the Lean Startup model, we are rehabilitating learning with a concept I call validated learning. 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
...more
Validated learning!!
Metcalfe’s law: the value of a network as a whole is proportional to the square of the number of participants. In other words, the more people in the network, the more valuable the network. This makes intuitive sense: the value to each participant is driven primarily by how many other people he or she can communicate with.
Metcalfe’s law
Lean thinking defines value as providing benefit to the customer; anything else is waste.
Lean thinking
The effort that is not absolutely necessary for learning what customers want can be eliminated. I call this validated learning because it is always demonstrated by positive improvements in the startup’s core metrics. As we’ve seen, it’s easy to kid yourself about what you think customers want. It’s also easy to learn things that are completely irrelevant. Thus, validated learning is backed up by empirical data collected from real customers.
We adopted the view that our job was to find a synthesis between our vision and what customers would accept; it wasn’t to capitulate to what customers thought they wanted or to tell customers what they ought to want.
This is true startup productivity: systematically figuring out the right things to build.
Experiment I come across many startups that are struggling to answer the following questions: Which customer opinions should we listen to, if any? How should we prioritize across the many features we could build? Which features are essential to the product’s success and which are ancillary? What can be changed safely, and what might anger customers? What might please today’s customers at the expense of tomorrow’s? What should we work on next?
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. Just as scientific experimentation is informed by theory, startup experimentation is guided by the startup’s vision. The goal of every startup experiment is to discover how to build a sustainable business around that vision.
The 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.
For the growth hypothesis, which tests how new customers will discover a product or service, we can do a similar analysis.
In the Lean Startup model, an experiment is more than just a theoretical inquiry; it is also a first product.
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.
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?”
“Success is not delivering a feature; success is learning how to solve the customer’s problem.”
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.
What differentiates the success stories from the failures is that the successful entrepreneurs had the foresight, the ability, and the tools to discover which parts of their plans were working brilliantly and which were misguided, and adapt their strategies accordingly.
The first step in understanding a new product or service is to figure out if it is fundamentally value-creating or value-destroying.
A startup’s earliest strategic plans are likely to be hunch- or intuition-guided, and that is a good thing. To translate those instincts into data, entrepreneurs must, in Steve Blank’s famous phrase, “get out of the building” and start learning.
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.
If we do not know who the customer is, we do not know what quality is.
Customers don’t care how much time something takes to build. They care only if it serves their needs.
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.
Innovation accounting works in three steps: first, use a minimum viable product to establish real data on where the company is right now. Without a clear-eyed picture of your current status—no matter how far from the goal you may be—you cannot begin to track your progress.
Second, startups must attempt to tune the engine from the baseline toward the ideal. This may take many attempts. After the startup has made all the micro changes and product optimizations it can to move its baseline toward the ideal, the company reaches a decision point. That is the third step: pivot or persevere.
Before building the prototype, the company might perform a smoke test with its marketing materials. This is an old direct marketing technique in which customers are given the opportunity to preorder a product that has not yet been built. A smoke test measures only one thing: whether customers are interested in trying a product. By itself, this is insufficient to validate an entire growth model. Nonetheless, it can be very useful to get feedback on this assumption before committing more money and other resources to the product.
Pivot or Persevere Over time, a team that is learning its way toward a sustainable business will see the numbers in its model rise from the horrible baseline established by the MVP and converge to something like the ideal one established in the business plan. A startup that fails to do so will see that ideal recede ever farther into the distance. When this is done right, even the most powerful reality distortion field won’t be able to cover up this simple fact: if we’re not moving the drivers of our business model, we’re not making progress. That becomes a sure sign that it’s time to pivot.
Every time, we told ourselves we were making the product better, but that subjective confidence was put to the acid test of real numbers.
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.
Cohort analysis
This is the pattern: poor quantitative results force us to declare failure and create the motivation, context, and space for more qualitative research. These investigations produce new ideas—new hypotheses—to be tested, leading to a possible pivot. Each pivot unlocks new opportunities for further experimentation, and the cycle repeats.
Each time we repeat this simple rhythm: establish the baseline, tune the engine, and make a decision to pivot or persevere.
Learning milestones prevent this negative spiral by emphasizing a more likely possibility: the company is executing—with discipline!—a plan that does not make sense. The innovation accounting framework makes it clear when the company is stuck and needs to change direction.
Compared to a lot of startups, the Grockit team had a huge advantage: they were tremendously disciplined. A disciplined team may apply the wrong methodology but can shift gears quickly once it discovers its error. Most important, a disciplined team can experiment with its own working style and draw meaningful conclusions.
split-test experiment is one in which different versions of a product are offered to customers at the same time. By observing the changes in behavior between the two groups, one can make inferences about the impact of the different variations.
Although split testing often is thought of as a marketing-specific (or even a direct marketing–specific) practice, Lean Startups incorporate it directly into product development.
Actionable metrics are the antidote to this problem. When cause and effect is clearly understood, people are better able to learn from their actions. Human beings are innately talented learners when given a clear and objective assessment.
Each cohort analysis says: among the people who used our product in this period, here’s how many of them exhibited each of the behaviors we care about.
The heart of the scientific method is the realization that although human judgment may be faulty, we can improve our judgment by subjecting our theories to repeated testing.
