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The Lean Startup by Eric Ries.
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The goal of a startup is to learn what their customers want and will pay for, as quickly as possible. Because startups face so much uncertainty, you have to make continuous adjustments to your startup plan, based on the information you get back. This is Build-Measure-Learn.

Think about it like driving a car. When you get on the road, you make constant adjustments to the steering wheel, based on where you see yourself on the road. Veer a little right, and you turn to the left before you go offroad.

A startup’s the same way. You collect information about your customer, just like seeing where you are on the road. Based on this new information, you adjust your strategy, like turning the steering wheel. You keep repeating this and making progress.

But you need to approach learning with discipline, a process called “validated learning.”

Bad learning is executing a plan without much prior thought, seeing it work or fail, and giving a post-hoc rationalization. (“Well of course X didn’t work, that means we should do Y!”)

Validated learning is having testable hypotheses about the world, designing experiments to test those hypotheses, and analyzing the data to evaluate your hypotheses. You have real, quantitative data to show what you have learned.

How can you learn what you users want, as quickly as possible, and with as few resources as possible? The strategies of Lean Startup answer this question.

Build-Measure Learn

First, you set your hypothesis. What do you believe about your customers that is vital to your business? How are you going to measure this?

Next, you build the MINIMAL product necessary to test the hypothesis.

Next, you run the experiment. Often this means exposing users to the product and collecting data on their behavior.

Finally, you analyze the data and reflect – how far off was your hypothesis? What do you need to change about your strategy? Should you actually change your entire direction?

Then you update the hypothesis or set a new one. Then you build the minimal product to test that hypothesis, and so on.

The faster you move through this loop, the faster you’ll learn, and the more progress you’ll make. Imagine how much you’d learn from 10 steadily improving prototypes vs 1 giant, fully-featured prototype.

Startup Hypotheses

What are hypotheses in a startup? Your hypotheses should revolve around the most important problem of a startup – how to build a sustainable business around your vision.

Commonly, there are two critical hypotheses:

Value hypothesis: does the customer have the problem you’re trying to solve? Does the product actually deliver value to the customer?

Growth hypothesis: how will the company grow once people start using the product?

Principles of Lean Experimentation

The best way to understand human preferences is to track behavior of real people, not ask for opinions. People often can’t verbalize what they actually want – cue the classic Henry Ford quote, “If you asked people what they wanted, they’d have said faster horses.” The better way to measure what people want is by their real behavior - what they click on, what they spend time looking at, what they choose to pay for.

Think about the cheapest, fastest experiment you can run to validate the hypothesis. Simplify the product to the core essentials needed to run the experiment. Resist the urge to build more than is absolutely necessary – as you’ll learn, sometimes you can fake it ‘til you make it without a real product.

Launching early gives you customer information earlier. The earlier you learn if customers actually want what you’re building, the more time you have to change your plan and run more experiments. You also discover customer concerns you couldn’t have predicted in a vacuum.

The Minimum Viable Product

Your goal is to move through the Build-Measure-Learn loop as quickly as possible. Even though the loop has 3 steps, Build is often the step where you will waste the most time.

The critical question you need to answer is: what is the MINIMUM product you can build to get reliable data on your hypothesis?

This product is termed the Minimum Viable Product (MVP) and is one of the most important concepts in Lean Startup. This is the product that is the bare minimum to test your hypothesis. Unlike normal product development, you are NOT aiming for product perfection – you’re merely trying to start learning as soon as possible.

Depending on your business, there are a variety of ways to test your hypothesis in the cheapest, fastest way possible. In the more extreme, you don’t need a product at all - you can simply pretend you have a product and collect user behavior.

Here are examples of MVP types:

  • Landing Page MVP: create a web page describing the features of the product, before a product actually exists. Track clicks and attempted downloads to gauge how much users want it.
  • Video MVP: make a video simulating what the product does. Even without using the actual product, watching the video will give enough info for viewers to decide whether they want the product shown.
  • Concierge MVP: instead of building processes that scale, run very manual processes dependent on special white-glove treatment. Often the founders themselves deliver the service. This accelerates learning and allows quick iteration on the product.
  • Wizard of Oz MVP: if you plan to build fancy automated technology, try to test it with a human behind the scenes. The human performs the service that the technology does, and the user is none the wiser.

Choosing the Right Metrics

Defining the right metrics that actually matter to your business is critical.

The most insidious kind of metrics, vanity metrics, give you false optimism – it seems like you’re making great progress, but in reality you’re actually stuck. Often, vanity metrics are metrics that have no choice but to keep increasing over time. One common example is total user count. Let’s say your app adds 1,000 users every week. At the end of 10 weeks of hard work, you have 10,000 users. This is a big number!

Except you aren’t growing any faster – you’re still adding 1,000 users every week. Your hard work actually didn’t change your growth rate.

Instead of looking at cumulative vanity metrics over time, the more accurate analysis is to separate users into groups based on the time they joined, then measure your metric for each group independently. Each group of users is called a cohort. For instance, you can measure the signup rate for each week separately to see if that’s increasing over time.

A/B Testing

Let’s say you develop a new feature to your product and you release it to all your users. Suddenly your metrics improve. But how do you know seasonal effects aren’t at play – that the users who joined later aren’t just naturally more engaged? Or that you got a burst of users from an unexpected news article?

An A/B test avoids bias by splitting users into seeing two different versions of your product. By analyzing the metric resulting from both groups, you get strong quantitative evidence about which version users like more.

For example, let’s say you have a landing page MVP listing your potential features and a signup form. You’re not sure which of two features your users will like more. So you set up an A/B test – half of visitors see feature A on your landing page; the other half sees feature B. You measure the difference in signup rate. If feature A gets a 5% signup rate, but feature B gets 2%, this is evidence that your users may prefer feature A!

Another benefit of A/B testing is that it lowers politics. You don’t have to squabble with your team over which features are better – you can put it to the test with an MVP. A/B testing also lets you assign credit where it’s due.

Pivoting Your Startup

Now when you’re facing the...

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The Lean Startup Summary Introduction

The media portrays startup success incorrectly as fatalistic – if you have the right stuff (a good idea, determination, timing, and luck), you will inevitably succeed. If you don’t succeed, it’s because you didn’t have the right stuff. You either have it or you don’t.

This idea is seductive because it both promises easy success and justifies failure. To succeed, all you need is the right stuff – easy! And yet if you fail, you can simply justify failure as not having the right stuff, rather than making poor decisions. This is softer on the ego.

This is the wrong way to think about entrepreneurship. Startup success is NOT fatalistic. There is a rigorous, repeatable method to achieve startup success – the Lean Startup.

Origins of the Lean Startup

The ideas in the book came about when Eric Ries got frustrated working hard on products that failed to get traction. As an engineer, he initially thought they failed due to technical problems, but this was never the right answer. In reality, they just wasted a lot of time building things nobody wanted.

So when he started his new company, IMVU, he wanted to try something different. One inspiration was Steve Blank’s idea of Customer Development: a rigorous methodology for the business and marketing side of a startup. Another inspiration was Japan’s lean manufacturing systems, made famous by Toyota.

He applied these concepts to IMVU, which became a roaring success, with millions of users and $50 million in annual revenues in 2011. To help others succeed in innovation, Eric started the Lean Startup movement by publicizing the framework you’ll learn about here.

The 5 Principles of Lean Startup

There are 5 main concepts in the Lean Startup. We’ll be exploring each one in much more detail.

1) Entrepreneurs are everywhere. Eric defines a startup as “a human institution designed to create new products and services under conditions of extreme uncertainty.” This is purposely inclusive and covers a range of people, from employees in a large company or the government to the stereotypical college dropouts...

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The Lean Startup Summary Part 1: Vision | Chapter 1: Startups Need Managing

‘Management’ is often a dirty word in startups because it conjures images of grey suits, bureaucracy, and TPS reports. Startups are worried management will squash energy and creativity.

The problem is, startups go too far in the other direction into chaos. They often take a shoot-from-the-hip, hail-Mary, undisciplined approach to company development. This unfortunately often leads to failure, spending years of your life building something no one cares about or remembers.

Startups really do need management, but a new kind of management catering to high-risk innovation - a kind of Entrepreneurial Management. By using this management method, you will be confident you’re moving in the right direction and learning more about your company. Surprise – that method is the Lean Startup.

Startups Need to Learn as Quickly as Possible

The goal of a startup is to learn what their customers want and will pay for, as quickly as possible. Thus the real metric you should care about is the amount you learn – not the number of hours you work, the lines of code you’ve written, or the number of times you’ve banged your head against the wall.

Because startups face so much uncertainty, you have to make continuous adjustments to your startup plan, based on the information you get back. This is Build-Measure-Learn.

Think about it like driving a car. When you get on the road, you make constant adjustments to the steering wheel, based on where you see yourself on the road. Veer a little right, and you turn to the left before you go offroad.

A startup’s the same way. You collect information about your customer, just like seeing where you...

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The Lean Startup Summary Chapter 2: Entrepreneurs are Everywhere

Who, exactly, is an entrepreneur?

Entrepreneurs aren’t just startup founders working out of apartments. They can also be general managers in large companies charged with creating new ventures or new product lines.

The author defines a startup as “a human institution designed to create a new product or service under conditions of extreme uncertainty.”

The definition is purposefully broad – it can apply to a non-profit, a government agency, a big company or a small company. The key piece of this definition that makes a startup unique is the condition of “extreme uncertainty.”

In situations of uncertainty, traditional management tools – like forecasts, business plans, and milestones – break down. There’s too much that’s unknown about the world to predict with high accuracy what’s going to work.

The Lean Startup is a set of methods for building a successful startup, wherever it is and whoever is working on it.

Startup Example: Snaptax

In 2009, a startup wanted to automate W-2 form processing to streamline individual taxes. Their early users had problems had difficulty scanning in their tax forms, so instead the startup decided to switch to cell phone cameras as a way to capture W-2 forms. But the customers asked for something even more ambitious – could they complete their entire tax return on the phone?

This was a tall order. Tax forms can get super complex and annoying to deal with.

Instead of building a complete product and shipping a giant package, the startup decided to release a barebones version. It only processed the simple 1040EZ tax return, and it only worked for California. It launched as SnapTax in 2011 to great success.

So who was behind SnapTax? Surprise! It was an internal project at Intuit, a giant public company that makes finance tools like Quicken and Turbotax.

Intuit: a Giant Startup

Intuit was founded by Scott Cook in 1983, and they dominated the finance and tax prep software industries. But by 2002, its product initiatives were...

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The Lean Startup Summary Chapter 3: Learn What Your Users Want

Remember that a startup is trying to build a new product or service in conditions of extreme uncertainty. There are a lot of things you know that you don’t know, and a lot of things you don’t know that you don’t know.

Therefore, a startup’s most important function is learning – in particular, learning what the customers really want and what will lead to a sustainable business.

But you need to approach learning with discipline, a process called “validated learning.”

Bad learning is executing a plan without much prior thought, seeing it work or fail, and giving a post-hoc rationalization. (“Well of course X didn’t work, that means we should do Y!”)

Validated learning is having testable hypotheses about the world, designing experiments to test those hypotheses, and analyzing the data to evaluate your hypotheses. You have real, quantitative data to show what you have learned.

Startup Example: IMVU

In 2004, author Eric Ries and his co-founders at IMVU wanted to build a social network around instant messaging (IM), which seemed attractive for its network effects – the more people who join, the more valuable the network is, which makes even more people join.

Because of network effects, the top IM products owned the vast majority of the industry. It was commonly accepted, almost obvious, that it’d be extremely difficult to make a new IM network succeed. If you’re already on a network with all your friends, you don’t want to switch to a new network without all your friends joining too. (Shortform note: think about how hard it’d be to build a rival to Facebook today – how would you get Facebook users to join your network when all their friends are on Facebook? Would they really want to join yet another social network?)

To avoid competing with incumbents head-on, IMVU decided not to start their own new IM network. Instead, they built a new 3D video game layer on top of IM networks. Users would first join IMVU, a 3D virtual world where they could create avatars and buy virtual goods. Then they would connect their IM accounts and message people on...

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The Lean Startup Summary Chapter 4: Experiment like a Scientist

Lean Startup treats building a startup as science:

  1. Create a hypothesis. What do you believe that’s important to your business?
  2. Design an experiment to directly test that hypothesis. Run the experiment and gather data.
  3. Reflect on the data – can you validate or reject your hypothesis?

Once again, this stands in stark contrast to the typical “launch and we’ll see” approach. You’re forced to question what your assumptions about your business are, and you’re encouraged to test those assumptions as quickly and cheaply as possible.

Startup Hypotheses

What are hypotheses in a startup? Your hypotheses should revolve around the most important problem of a startup – how to build a sustainable business around your vision.

Commonly, there are two critical hypotheses:

Value hypothesis: does the customer have the problem you’re trying to solve? Does the product actually deliver value to the customer?

Growth hypothesis: how will the company grow once people start using the product?

We’ll unpack each of these, and how to test them, in the following chapters.

Principles of Lean Experimentation

As you learn how to design experiments and test hypotheses, here are important principles to remember throughout.

The best way to understand human preferences is to track behavior of real people, not ask for opinions. People often can’t verbalize what they actually want – cue the classic Henry Ford quote, “If you asked people what they wanted, they’d have said faster horses.” The better way to measure what people want is by their real behavior - what they click on, what they spend time looking at, what they choose to pay for. When someone behaves this way, you know they really want it.

Think about the cheapest, fastest experiment you can run to validate the hypothesis. Simplify the product to the core essentials needed to run the experiment. Resist the urge to build more than is absolutely necessary – as you’ll learn, sometimes you can fake it ‘til you make it without a real product.

**Launching early gives you customer information...

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Shortform Exercise: Your Startup Beginning

Reflect on the principles of Lean Startup.


Do you consider yourself in an entrepreneurial situation – “creating a new product or service under conditions of extreme uncertainty?” Why or why not?

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The Lean Startup Summary Part 2: Steer | Chapter 5: Form Your Hypothesis

With your startup, your goal is to learn as quickly as you can with disciplined experiments. Central to Lean Startup is the Build-Measure-Learn feedback loop.

Let’s step through this loop once:

First, you set your hypothesis. What do you believe about your customers that is vital to your business? How are you going to measure this?

Next, you build the MINIMAL product necessary to test the hypothesis.

Next, you run the experiment. Often this means exposing users to the product and collecting data on their behavior.

Finally, you analyze the data and reflect – how far off was your hypothesis? What do you need to change about your strategy? Should you actually change your entire direction?

Then you update the hypothesis or set a new one. Then you build the minimal product to test that hypothesis, and so on.

The faster you move through this loop, the faster you’ll learn, and the more progress you’ll make. Imagine how much you’d learn from 10 steadily improving prototypes vs 1 giant, fully-featured prototype.

Startup Hypotheses

Every startup begins with a set of hypotheses about how the business will work. As stated above, there are typically two core hypotheses: the Value hypothesis (what your users want), and the Growth hypothesis (how your startup will grow).

Some hypotheses are very un-risky – they’re very likely to be true. If your business will rely on advertising for revenue, for example, chances are good that you’ll be able to procure the same advertising rates as competitors in your industry. Online advertising has matured enough that advertisers know how much they want to pay for which types of ads across the Internet.

But other hypotheses are much riskier and less likely to be true. Often, these riskiest hypotheses underlie the reason your business exists. Namely:

  • Do people actually have the problem you believe they have?
  • Do they actually want what you’re offering?
  • Are they willing to pay for it?

The Dangerous Startup by Analogy

One insidious way to frame your business is by **analogy to...

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The Lean Startup Summary Chapter 6: Test with the Minimum Viable Product

Your goal is to move through the Build-Measure-Learn loop as quickly as possible. Even though the loop has 3 steps, Build is often the step where you will waste the most time.

The critical question you need to answer is: what is the MINIMUM product you can build to get reliable data on your hypothesis?

This product is termed the Minimum Viable Product (MVP) and is one of the most important concepts in Lean Startup. This is the product that is the bare minimum to test your hypothesis. Unlike normal product development, you are NOT aiming for product perfection – you’re merely trying to start learning as soon as possible.

The value hypothesis is key to most startups – “does anyone want what we’re building?” The typical wrong route many entrepreneurs make is to simply build their full product, then wait for results. Here’s the problem with that approach – every extra feature you add before testing means wasted time. You might be pointed entirely in the wrong direction. Building features nobody wants means more wasted time before you test and find out you’re in the wrong direction.

Depending on your business, there are a variety of ways to test your hypothesis in the cheapest, fastest way possible. In the more extreme, you don’t need a product at all - you can simply pretend you have a product and collect user behavior.

We’ll cover a range of such possibilities, from landing pages and videos to manually-serviced technology.

The Landing Page MVP

Let’s say you wanted to build a new social app where you take a picture of yourself, and the app swaps your face with celebrities. You can then share the hilarious results on social media. Call it CelebrityFaceSwap.

Based on your business model research, you have some assumptions around conversion rates to make the app sustainable – out of 100 people who visit your site, 20 people might sign up, 10 people will stick around for a week, and so on.

Let’s focus on the hypothesis of the 20% sign up rate (20 signups out of 100 visitors). What is the MVP in this situation? Think about it for a second.

A...

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Shortform Exercise: Design Your MVP

Try to design the minimum product that will test your hypothesis.


What is the key hypothesis you want to test? Often, it’s centered around the question of whether your users will want what you’re building, or whether they have the problem you’re solving.

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The Lean Startup Summary Chapter 7: Measure

The job of a startup is to:

  1. Build an MVP to measure where it is now
  2. Experiment to improve the metrics
  3. Decide whether to continue in the same direction or pivot in a new direction.

Defining the right metrics that actually matter to your business is critical. Before giving examples of good metrics, we’ll define common bad metrics startups choose.

Avoiding Vanity Metrics

The most insidious kind of metrics, vanity metrics, give you false optimism – it seems like you’re making great progress, but in reality you’re actually stuck.

Often, vanity metrics are metrics that have no choice but to keep increasing over time. One common example is total user count. Let’s say your app adds 1,000 users every week. At the end of 10 weeks of hard work, you have 10,000 users. This is a big number!

Except you aren’t growing any faster – you’re still adding 1,000 users every week. Your hard work actually didn’t change your growth rate.

These vanity metrics are misleading because they keep increasing over time, making you feel good. To give an obviously silly example, think about a metric like number of hours worked. Every day, you and your team each add 10 hours to the total count. At the end of the month, you proudly announce to the team, “this month, we added 1,250 hours to our Total Hours Worked metric. This is great progress. Good job, team!”

This may sound silly, but think about how many founders you’ve heard brag about how many hours they work. What really matters are the results they achieve.

Another type of vanity metric tracks results that aren’t actually critical to business function. For instance, early startups worry about racking up press mentions or making new hires. While these may help the business move, they’re not the actual core of the business – your company doesn’t exist to hire employees, it exists to create value for customers and create profits for shareholders.

These common vanity metrics are all problematic:

  • Total number of anything – users, sales, actions in product
  • Money raised from investors
  • Press...

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Shortform Exercise: Choose the Right Metrics

Don’t be fooled by vanity metrics - focus on the most valuable metrics for your startup.


Have you ever been asked to focus on a metric that you thought was misguided or silly? What was the metric, and why was it bad?

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The Lean Startup Summary Chapter 8: Pivot or Persevere

Now when you’re facing the data, you need to decide what to do. Is there still promise in your direction, and should you keep trying to iterate through the Build-Measure-Learn loop? Or have the metrics come back so disappointing so often that it’s time to change your strategy entirely – to pivot?

The answer is often unclear because you will seldom encounter complete, abject failure. The more likely state is when you’re barely limping along, not plummeting to the earth but also feeling like you’re not really making progress. The decision gets hard here.

There are two signs of the need to pivot:

  • Your metrics are not good enough to meet your goals for your startup.
  • Your experiments are leading to less and less progress (which is a sign that you’re out of good ideas)

Startup Example: Votizen

Lean Startup gives the example of Votizen, a company founded to boost participation in politics. Their first product was a social network where voters could unite around causes and mobilize action. They formed hypotheses around 4 metrics: signing up (registration), verifying voters (activation), sticking around and using Votizen (retention), and recruiting friends to join (referral).

Votizen built an MVP in 3 months and got their baseline metrics. Then they spent 2 more months and $5,000 to get a second set of metrics. Then they spent another 8 months and $20,000 to get a third set of metrics. Let’s take a look:

MVP

(3 months, $1,200)

Optimization 1

(2 months, $5,000)

Optimization 2

(8 months, $20,000)

Registration 5% 17% 17%
Activation 17% 90% 90%
Retention Too low to measure 5% 8%
Referral Too low to measure 4% 6%

Starting with...

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The Lean Startup Summary Part 3: Accelerate | Chapter 9: Work in Smaller Batches

Part 2 is really the core of Lean Startup. You’ve learned about the entire Build-Measure-Learn loop. You know that you need to form hypotheses about your business and test them with MVPs. You know to avoid vanity metrics and focus on the metrics that really tell you the health of your business. You know that you will need to face the question of whether to pivot.

In the concluding section of the book, we’ll learn how to accelerate through the Build-Measure-Learn loop and stay agile as you grow.

Decreasing Batch Sizes

The aim of the Build-Measure-Learn loop is to step through it - or iterate - as quickly as you can. This increases your rate of learning.

To support this, try to decrease your batch size. Don’t overinvest in huge feature releases. Release less and more often, and you’ll learn faster.

The Envelope Analogy

Imagine you have 100 letters to mail. Each letter needs to be signed, folded, put in an envelope, sealed, and stamped. How would you approach this?

Your intuition is likely to batch each separate step and do all 100 at once. You’ll sign all 100 letters. Then you’ll fold all 100 letters. Then you’ll put 100 letters in 100 envelopes. And so on.

Surprisingly, Eric Ries claims this is slower than fully processing each letter at once – a small-batch method. You’d think that you get better at each individual process when you do it repetitively. But this doesn’t make up for the additional logistics needed for big batches - to stack 100 folded papers, stack 100 filled but unsealed envelopes, and so on.

There’s another benefit to small batches – you figure out problems much earlier. What if the envelopes are old and the glue has worn out? What if the paper’s printed in the wrong size? In a large batch, you might figure this out after you’ve already done 300 previous steps and have to redo your work. With small batches, you figure this out almost immediately.

This concept maps directly onto building a startup. Instead of releasing a fully-featured product once a year, you could release small batches of features regularly. With...

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The Lean Startup Summary Chapter 10: How Your Startup Grows

A startup is a new company designed to grow. If you’re ambitious about your startup, you want more customers and more revenue sooner. When growth stagnates, it indicates a problem with your growth strategy.

Ideally, you strive for sustainable growth – wherein new customers come from the actions of past customers. This contrasts with unsustainable one-time activities, like a publicity stunt or Super Bowl ad.

How exactly your business grows depends on your industry and product. Eric defines three major engines of growth: Sticky, Viral, and Paid. These aren’t mutually exclusive, and often businesses will use all three at once. But it’s likely one of these will dominate the others.

The Sticky Engine of Growth

The Sticky engine relies on retention of customers to grow. When you acquire the user, you want the user to stick around as much as possible.

As a metaphor, think of a leaky bucket with holes. When you pour water in, water flows out the holes. If you fill the bucket with water at the same rate it’s exiting the holes, the water level stays steady – no growth. This is like a startup that loses companies as fast as it’s gaining them - the total number of active users isn’t growing.

But if you plug up the holes, the funnel will start filling up. Analogously, if users stick around each week, you’ll continue to grow the number of users.

Sticky growth is especially important in a few types of businesses:

  • In a network or marketplace of connected users. Here, the value grows exponentially with the number of users on it.
    • Social networks like Facebook: if Facebook keep new users on and get existing users to post more, their friends will find Facebook overall more valuable.
    • Marketplaces like Uber: more riders means more rides for drivers and more earnings. This kicks off a virtuous cycle where more earnings recruit more drivers, which improves the rider experience and recruits more riders
  • When your customer keeps paying you
    • Merchants like Amazon: retaining users means they continue to buy from the...

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The Lean Startup Summary Chapter 11: Slow Down Intelligently

A lean startup faces natural tension between opposites: fast and scrappy vs slow and methodical, hacky and agile vs robust and overdesigned.

Initially, building a product quickly and poorly gets you data faster. But you incur technical debt that will slow development in the future.

How do you decide where on the spectrum to lie? You need a natural feedback loop to tell you when you’re moving too slowly or too quickly by identifying the root cause of problems.

When you face a new problem, root cause analysis tells you precisely why the problem happened, and suggests how to fix it. This prevents overdesign and prevents the problem from happening again.

The Five Whys

Find the root cause with the Five Whys method. When you see a problem, ask yourself five Whys in succession.

Here’s an example from Toyota where a machine broke down:

  1. Why did the machine stop? There was a power overload and the fuse blew.
    1. If we stopped here, we would just replace the fuse. The fuse would blow again, and we’d replace it again, and so on. We would treat the symptom, but not the cause.
  2. Why? The bearing was not lubricated.
  3. Why? The lubrication pump wasn’t pumping.
  4. Why? The shaft of the pump was worn.
  5. Why? The mechanic forgot to put a filter on, and metal scrap got in.

The final root cause is dramatically different from what you started out with. Because it’s a root cause, addressing it will solve all the problems up the line. If you had stopped asking why at each stage, you’d have addressed only a symptom that will recur, instead of treating the underlying disease.

In fact, if you kept asking why a few more times, you’d probably find other root causes to tackle. The mechanic may have gotten insufficient training on this equipment. Repair of this machine might not have a checklist, which is standard practice. Or the mechanic might have just been a bad hire – in which case bad hiring practices are to blame.

This example can be applied to any recurring problem in your startup, whether it’s a problem in customer service, engineering,...

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Shortform Exercise: Try the Five Whys

Try to figure out the root cause of a problem.


What problem have you faced recently? Why did it happen?

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The Lean Startup Summary Chapter 12: Startups in Big Organizations

The final substantive chapter of Lean Startup discusses innovation in a large company and internal startups.

If growth is your goal and you achieve it, you’ll keep growing your startup to 10, 100, 1,000 people. How do you keep innovating to grow new lines of business while keeping existing products competitive? How do you prevent yourself from being bogged down by process?

The solution is to manage lines of innovation in parallel.

Each product has a life cycle:

  • new product
  • growth and scaling
  • optimization, operational excellence, and fighting newcomers
  • legacy support and cutting costs

Once a company grows past its early stages, it needs to think about what comes next. This means managing multiple products at different stages of the cycle at once. Earlier projects will be sunsetting and losing revenue just as the next major innovation breaks new ground.

Match People with the Product Stage

Who is the best person to run a product through its lifecycle? A typical answer is to tie the original product team through the entire life cycle of the product. After all, they built it, so they seem like natural candidates to scale it up and to sunset the product.

This may be sub-optimal. People tend to be specialists at one of the phases in the life cycle. Some people are natural early-stage innovators and don’t have the energy or conscientiousness for optimization. Others like the rigor of operational excellence, but can’t stomach the variability in the earliest stages.

Instead of keeping the original team with the product, keep people in the stage they’re good at. Then transfer products between teams - once a product matures past the growth phase team, move it to the operational maintenance team. This means some people’s roles may focus on spinning up new early-stage startups, and others inherit those startups and grow it to scale.

The 3 Attributes of Successful Startup Teams

The author suggests that successful innovation requires three structural attributes:

  • Limited but secure resources. By their nature,...

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The Lean Startup Summary Chapter 13: Epilogue

Eric Ries believes a tremendous amount of effort today is wasteful. Over the past century, our bandwidth for production is much larger than our ability to know the right things to build. This problem is compounded by the high rate of change of many industries.

Luckily, this is preventable. The Lean Startup is a framework for figuring out the right things to build. It answers the innovation question: how can we build a sustainable organization around a new product or service?

Lean Startup accomplishes this through the scientific method: forming testable hypotheses, testing them, and gathering data for important metrics. This is validated learning.

Lean Startup is NOT meant to be a rigid, codified set of practices. If you blindly follow the steps outlined here without care, you may find yourself obsessing about stepping through the motions, rather than isolating the core of what really matters to your startup. Eric...

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Table of Contents

  • 1-Page Summary
  • Introduction
  • Part 1: Vision | Chapter 1: Startups Need Managing
  • Chapter 2: Entrepreneurs are Everywhere
  • Chapter 3: Learn What Your Users Want
  • Chapter 4: Experiment like a Scientist
  • Exercise: Your Startup Beginning
  • Part 2: Steer | Chapter 5: Form Your Hypothesis
  • Chapter 6: Test with the Minimum Viable Product
  • Exercise: Design Your MVP
  • Chapter 7: Measure
  • Exercise: Choose the Right Metrics
  • Chapter 8: Pivot or Persevere
  • Part 3: Accelerate | Chapter 9: Work in Smaller Batches
  • Chapter 10: How Your Startup Grows
  • Chapter 11: Slow Down Intelligently
  • Exercise: Try the Five Whys
  • Chapter 12: Startups in Big Organizations
  • Chapter 13: Epilogue