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Multiple Factor Optimization [Lean Startup
Posted on June 29, 2017 @ 11:04:00 AM by Paul Meagher

Lean Startup Theory advocates the use of ongoing experimentation to find out if customers value your product or not. The use of A/B testing is often used determine if some feature is having a significant positive influence on customers or not. The popularity of A/B testing derives from the fact that it is easy to change some minor feature of a website to see if some success measure is improved or not relative to the control/existing version of the website. The technique is easy and the math is easy (but check this out).

A/B testing is only one experimental technique that might be used and it has some limitations. One limitation is that it is often used to test only one factor or version at a time to see if the factor/version improves success metrics or not. Optimal performance is often a function of the interaction of two or more factors that can not determined by testing one factor at a time. Chemical reactions can occur optimally at a combination of temperature and pressure that is not predicted by studying each factor separately.

Today I want to mention a methodology that is not that well known but which is used in industry to find the factors and the levels of each factor that produces optimal outcomes. That methodology is called Response Surface Methodology (RSM) and is commonly used in chemical industries to find the optimal operating conditions (temp, pressure, catalytic agents, reactants, pH, etc..) for producing a chemical reaction.

Response Surface Methodology begins by listing all the factors that might contribute to the response. It also examine the levels that each of these factors might take on. When you do this you quickly run into a combinatorial explosion of factor levels to test. Where Response Surface Methodology comes in is to help guide you towards a reduced set of factors/levels to interatively test to arrive at an estimate of the optimal factor level settings.

All I can hope to do in this blog is mention a couple of ideas from response surface methods that I found interesting and am still exploring.

If you have, say, 3 factors (temperature, pressure, pH) with 5 levels then to run a full factorial design requires that you measure responses under each of the possible 125 conditions. This is generally not economically feasible so the question becomes whether you can find the optimal condition by studying some subset of conditions, often called a fractional factorial design. One such design that is popular in RSM is called Central Composite Rotable Design. That is an intimidating phrase for a neat idea. Basically you reduce the number of levels by only testing the extreme levels of each factor along with the median level of each factor and interpolating all the values that would fall in between.

So if you wanted to test the growth of a plant as a function of nitrogen and moisture instead of studying all the levels of each factor you would only test the response for the median levels of each factor and the extreme levels of each factor and try to interpolate what the response would be between these levels. Other fractional designs are possible.

The second idea from RSM that is worth thinking about is to plot the levels of your factors on each axis to generate a response surface that depicts our how the variables interact. The combination of moisture and temperature levels will generate a growth response in the plant that can be plotted as a response surface that an educated eye can read to better grasp what the optimal factor levels might be, or what tradeoff might might be best to make. We are all familiar with drawing and interpreting graphs consisting of curves, but not so familiar with drawing and interpreting surfaces and contours so as to understand the interaction of 2 variables. Being able to visualize the interaction of 2 variables as response surfaces can allow our visual system to process the information more thoroughly than a set of numbers would.

I have found that a good starting point for understanding response surfaces is Khan Academy's tutorials on Multivariate Calculus. You don't have to know calculus to learn some useful multivariate skills from the first few tutorials.

I hope this blog has helped to convince you that there are experimental methodologies besides A/B testing that might be applied to discovering the right combination of factors for your product or service. One at a time testing does not provide any insight into the potential interactions of that factor with other factors. For that you need a factorial design and to administer it efficiently you may want to consider response surface methodologies.

The definitive text on RSM is by George Box and Norman Draper with the title Response Surfaces, Mixtures, and Ridge Analyses (2nd, 2007). Not an easy read but worth scanning for ideas and probably very useful if you want to use RSM to optimize your product or service.

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The Lean Startup: Measure [Lean Startup
Posted on February 27, 2017 @ 07:22:00 AM by Paul Meagher

A lean startup uses innovation accounting to properly measure the effect of design changes on customers. A startup can fail if it is measuring the wrong things. The chapter "Measure" is about strategies we can use to make sure we are measuring the right things.

We discussed the concept of a Minimum Viable Product (MVP) in the last blog ("Test") of this blog series on Eric Reis seminal book The Lean Startup (2011). One property of an MVP that I didn't discuss was the use of an MVP to gather initial baseline measurements of the Key Performance Indicators (KPI). When designing your MVP, keep in mind that one important role that it can serve is to kick off the process of measuring baselines for key performance indicators like the number of registrations, number of downloads, number of customer logins, number of payments, and so on (sales funnel behaviors). Once you gather this baseline data for your key performance indicators, then you can verify whether any future design changes you make actually have a significant effect on the levels of these key performance indicators.

The term Innovation Accounting refers to the repetitive 3 step process of gathering baseline measurements, making a design change intended to improve KPIs, and then using these measurements to help you decide whether to pivot or persevere in your present course. The more times you can successfully complete this cycle, the more actual value-adding innovation is happening.

Lots of startups measure the performance of their business but you can still fail if you are measuring vanity metrics rather than actionable metrics. Vanity metrics are numbers that portray the startup in the best possible light but which don't actually give us much insight into what is working or not. These graphs often look like increasing sales graphs measuring gross numbers of users registering or performing some other desirable action on a website. While those numbers look good, it may be masking problems with other more critical metrics like conversions and sales. Ultimately the problem with a vanity metric is that it is not fine grained enough to inform us about what is working and what is not working. If we want to figure out what is working or not, then we need to apply scientific/statistical techniques to the design process.

If we made the effort to measure baseline performance with our MVP we are in a position to conduct A/B testing on some feature to see if it affects our baseline numbers or not. A/B testing involves presenting the potential customer with two versions of the product with one major factor made to differ across the two versions. If we find that version A delivers more sales than version B, and that A delivers more sales than our previous baseline sales, then we can start to develop a causal understanding of what factors are important to the success of our startup and which ones are not.

Eric unashamedly uses the term "cause-effect inferences" (p. 135) to describe the goal of measurement in the lean startup. He believes that A/B Testing and Cohort Analysis are both readily available techniques startups can use to achieve such understanding. He provides a detailed case study of how the educational startup Grockit applied A/B testing to figure out what was working and what was not working on their learning platform. They believed that peer learning was an underutilized aspect of learning and developed lots of platform features to support it but eventually realized the new features weren't producing improvements in their KPIs. This lead to the realization that learners also want a solo mode for learning which resulted in pivot in their design approach to more fully support both peer-based AND solo modes of learning.

I've discussed the book Getting to Plan B as an important influence on Eric's thinking. Chapter 2 of Plan B, Guiding Your Flight Progress: The Power of Dashboards, offers more useful ideas and techniques around measuring what matters. Plan B advises using Dashboards what list out what leaps of faith you are testing, how they are translated into hypothesis, what metrics you'll use to decide if the leap of faith is true or not, what your actual measurements are, and what insights and responses are appropriate given the results. Here is a simple dashboard for a lemonade stand which illustrates the basic ideas and format/layout they advocate.

What Eric did was add many useful details about the need for baselines, MVPs, innovation accounting, split testing and cohort analysis to this framework. These techniques help the lean startup more reliably find a value proposition and business model that works.

I'll conclude this blog by asking you to think about whether these ideas can be applied to developing new songs? Should a musician begin by develop a Minimal Viable Song that they expose to audiences to get baseline feedback? What key performance indicators might they measure? What variations might they experiment with to see if a change makes the song better (e.g., same lyric but different melodic delivery)? Could they achieve a cause-effect understanding of what elements of the song are contributing to the success of the song? What vanity metrics might mislead them about the success of their song?

I was listening to an interview with a musician recently that suggested she was using a sort of lean startup methodology to figure out how to develop new songs and thought it was an interesting domain of application for lean methods.

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The Lean Startup: Test [Lean Startup
Posted on February 22, 2017 @ 06:02:00 AM by Paul Meagher

In Lean Thinking the notion of quality is often used to define the type of product an established organization is trying to deliver to the customer. It doesn't work so well for an innovative startup that doesn't yet know what quality is or exactly who their customers are. In such cases, you have to be less concerned about quality and more concerned about learning. Startup products and services are designed to maximize learning, not quality.

That is the main idea in the chapter Test in Eric Reis' seminal book The Lean Startup (2011). Here he advocates developing low cost facsimiles of the product and/or service you envision that the customer will want or agree to use. A mental roadblock we might encounter is that the facsimile is too cheap in appearance and quality or too much of a kludge to want to expose a customer to it. We have to resist this urge so that we can test the assumptions, or leaps of faith, that your startup vision implies.

It is in this chapter that Eric discusses the idea of developing a Minimum Viable Product (MVP) as a means of testing your core value proposition and to acquire useful customer feedback. Think up the simplest version of your product or service that might work to test your idea and gather user/customer feedback. Don't let the notion of "quality" hinder you in this effort because you goal is not to deliver a quality product, it is to try to test your main leaps of faith as early as you can with users/customer so you can maximize learning.

In the case of web-based startups, developing a minimum viable product is often easier to do because you can create a demo that offers the minimum number of features that solves the customer problem and release that to a potential user/customer for their feedback. You can then quickly iterate on that demo adding new features based on customer feedback and your vision. When setting up a bricks and mortar business creating a minimum viable product is more difficult but possible. In farming, before you scale up to being a market gardener, you might create a smaller version of your garden that replicates essential elements of your growing system and the types of plants you intend to grow. You might even take the produce from that garden and agree to supply a neighbor or two with a veggie box over a period of time. Each growing season offers an opportunity to test your growing capacity and value proposition and you might learn that you cannot reliably grow certain vegetables, that you need to plant more of this or less of that, that some piece of equipment might make your life alot easier, that your initial customers are asking for more of this and less of that, etc... Only after you have verified that you have a viable and potentially scalable market gardening business model should you start to invest in alot of the equipment, land, seeds, fertility and labor that would be required to be a commercial market gardener. This is the type of progression that Eric is advocating in the Test chapter - verify then scale.

It takes creativity to create the simplest and smallest version of a product or service that tests your value proposition. E.F. Shumacher observed that "any intelligent fool can make things bigger, and more complex. It takes a touch of genius - and a lot of courage to move in the opposite direction". Some of the most important work a startup will ever do will be done early on at a small scale, searching for, refining, and verifying the business model that you will scale with.

After you read the "Test" chapter, two books that you might want to browse to dig deeper on this topic are:

Value Proposition Design is worth checking out because of the authors involved (check out Alexander Osterwalder's blogging) and because it uses a unique Info Graphics presentation format to express concepts. It offers visual tools and leap testing strategies you might want to use in the early stages of validating your startup vision.

The Startup Owner's Manual is also worth checking out because of the authors involved and the useful startup ideas and techniques discussed. Entrepreneur, educator and author Steve Blank invested in Eric's successful startup company IMVU on the condition that the co-founders attend his Stanford startup class where he applied Steve's ideas about Customer Development to his own company and to the book the Lean Startup. This book is a good resource for learning about customer development and many other ideas that a startup might want to be familiar with in the early stages of their venture.

It is important to recognize that what lean thinking looks like can vary quite significantly depending on context. In the case of early stage startups, lean thinking requires that a higher priority be assigned to learning than quality during the "search" phase of the business (but not the "execution" phase where quality becomes more important). What is produced by the startup in the early stages are products and/or services that are cheap, easy to assemble, and which can be used to test some important aspect of the business model. Startups that are too focused on launching a high quality product may end up releasing a high quality dud into the market place. If you wait to long to engage in the Build-Test-Learn feedback loop, you can end up building something of high quality that no one wants. That is the fatal danger a startup might avoid by engaging in early testing of assumptions via minimal viable products and customer validation techniques. In the early stages of a startup concerns about product or service quality have to take a back seat to concerns about learning. You need to setup a build-test-learn feedback loop as soon as you can to maximize validated learning.

I'll conclude this blog with the observation that Lean Thinking is often associated with the notion of quality but that quality can be sacrificed in certain contexts such as the early stages of releasing your product or service so that you can maximize learning. Ian Flemming in his book Lean Logic (2016) toys with the notion of sacrificing efficiency under certain circumstances. He believes that a lean economy should allow for and encourage a certain amount of slack so that other values beyond minimum prices/maximum productivity are operative. Creativity often occurs when a certain amount of slack time is given to employees to play around with ideas. Just as we need to resist the urge to maximize quality early on a startup, perhaps we should also resist the urge to maximize efficiency because learning and creativity are not necessarily or prototypically efficient activities. In addition to not worrying about quality as much, perhaps we also need to slack off a bit so that we are in the proper frame of mind to begin testing our business model. I don't think Eric would necessarily agree with me that a lean startup should be slack as the lean startup seems to be about being hyper efficient at honing in on a validated business model. Can that be accomplished, however, without introducing the ping pong tables, recreational outings, flex time and other elements of slack required to ensure a certain level of creativity happens?

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The Lean Startup: Leap [Lean Startup
Posted on February 17, 2017 @ 10:03:00 AM by Paul Meagher

This is the sixth blog in my anticipated 13 blog series dedicated to each chapter of Eric Ries' seminal book The Lean Startup (2011).

The fifth chapter is titled "Leap". It is the first of four chapters in Part Two of the book (the "Steer" chapters).

To understand the importance of the "Leap" concept we need to downplay one immediate association you might have; namely, the idea that you have to screw up your courage and launch your business. The "Leap" chapter is not a motivational chapter on the courage required to be an entrepreneur. Rather "Leap" is about identifying the "Leap of Faith" assumptions that are inherent in starting any innovative business. You are betting that people are going to respond positively to your value proposition, that they will pay you for your value proposition, that the value they are willing to pay for it is more than sufficient to justify the effort to deliver it. As a startup you don't know if the answers to any of these questions are true.

The concept of "Leap" is not about "just doing it" and seeing what happens. Rather, you have to strategically figure out where you should attempt to leap to first. In my blog on the excellent book Getting to Plan B: Breaking Through to a Better Business Model (2009) by John Mullins and Randy Komisar, the idea of engaging in ongoing testing of leap of faith assumptions is central to how they propose that you will find a better business model. For them, the Plan A business model is only there to help you identify the "leaps of faith" in your business model and consequently what business assumptions you need to test first. The result is generally Plan B (or C, or D, etc.). The objective is to get to the Plan B that ultimately works as quickly and efficiently as possible.

An example of testing leap-of-faith assumptions comes from my own experience in trying to start a farm mini-winery. The first major leap of faith assumption that I needed to test was whether I could grow viable wine grapes in this northern climate. There is no point creating a detailed business plan for the farm mini-winery until this foundational assumption is answered in the affirmative. I would like to say that it is a clear affirmative but after 5 years of growing I still have alot to learn about keeping grape vines alive and thriving. The vineyard has reached two acres and now the challenge is to improve the quality and density of vines on these two acres.

I harvested grapes last year and this will be my second year making wine from them. The first year, I wasn't as careful about controlling the environmental conditions of my wine and produced a wine that was not very drinkable. So this year, in an effort to test another major leap of faith assumption - that I can produce a drinkable wine from my grapes - I built a fermentation room in my garage so that I could better assess the quality of the wine I might produce. I tried to perform all the wine making steps when it was appropriate to do so, kept my wine topped up and sealed to prevent oxygen from spoiling the wine, managed sulfite levels so the wine will keep better, etc... I'll be doing some tasting in the next month to see if I have a drinkable red wine (most of my wine is red until more of my white grapes mature).

This idea of testing your most important leap of faith assumptions first is something that many entrepreneurs do already, but it is useful to have a language for talking about this process so we can formalize it a bit more in the form of dashboards used to measure and test the leaps of faith that our business model implies. If you verify a leap of faith assumption, you should double down and go in that direction; if you fail to verify a leap of faith assumption then you need to make either a smaller course correction or a major pivot. A successful company is one that has verified a series of leap of faith assumptions. Each verification becomes the vantage point from which you can search for the next leap of faith assumption you might test to grow your business further.

Even successful businesses will find the need to keep making leaps to improve their business model (or keep it from going stale). This is why I cautioned against viewing the "leap" concept as a motivational concept imploring you to start your business today. It is more of an analytical framework to think about how you might hone in on a successful business model or expand upon your current one.

Eric Reis chapter on "Leap" owes alot to the "Getting to Plan B" book which is why I have focused on that book to discuss what the significance of the "Leap" concept. I encourage you to watch this video of Randy Komisar explaining role of "Leaps" in guiding startups towards better business models.

I do want to leave Eric with the last word. For Eric, a Leap is a component of the Build-Measure-Learn feedback loop that he argues is critical to "Steering" a startup towards success. A leap in his framework is driven by what you need to learn about the most:

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. ~p 78.

If you want to maximize validated learning in your startup you need to test the most critical leap(s) of faith first by building something to test the assumption(s) and measuring the responses to it (Build-Measure-Learn).

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The Lean Startup: Experiment [Lean Startup
Posted on January 25, 2017 @ 09:28:00 AM by Paul Meagher

This is the fifth blog in my anticipated 13 blog series dedicated to each chapter of Eric Ries' seminal book The Lean Startup (2011).

The fourth chapter is titled "Experiment". The object of experimentation is the startup vision. Entrepreneurs are encouraged to test their startup vision by 1) starting small, 2) experimenting immediately, and 3) breaking it down.

Starting small

Although your vision may be big, you should start by figuring out ways to test a smaller version of your vision. In the case Zappos, a company with the vision of becoming the leading e-commerce retailer of shoes, they started by taking pictures of shoes from a local shop and agreeing to purchase them at cost from the retailer if anyone ordered them online. This provided them with valuable opportunities for validated learning on what works and what doesn't at an early stage of their companies journey. The lean startup literature offers useful ideas such as minimum viable product for how you might test a smaller version of your product or service.

Experiment immediately

If you start small, you can begin to experiment immediately on testing your vision. Some entrepreneurs may feel the need to engage in alot of planning before getting to the stage of testing their vision. This imperative reminds startups to start testing their vision as soon as possible to get feedback that will shape the details of your vision or cause you to pivot.

Break it down

You can also engage in testing earlier if you break your grand vision down. The two most important components of your vision is 1) your value hypothesis and 2) your growth hypothesis. Testing your value hypothesis involves testing "whether a product or service really delivers value to customers once they are using it" (p. 61). Testing your growth hypothesis involves testing "how new customers will discover a product or service" (p. 61). Your small and immediate testing would ideally address both these components of your vision in some fashion.

By starting small, experimenting immediately, and breaking the startup vision down we can start to gather early feedback from the customer to test the startup vision.

The experiment you create to test your startup vision can also be viewed as your first product that allows you to gather feedback that suggests a new experiment which is the next version of your product. An experiment is a product is the last main point that Eric wanted to get across in this chapter.

What I would add to this chapter is that not only is it important to experiment, it is also important to observe and interact (Permaculture Principle # 1). If you are starting a market gardening operation you would want to observe and interact with potential customers to determine what is best to grow. Experimentation suggests a more exacting standard for achieving validated learning than is sometimes necessary. So I would add observation and interaction with customers as additional means of generating and testing hypothesis related to your startup vision.

As a final note I would like to re-affirm my belief that a great way to absorb as book is to engage in "slow reading" of it. I never got much from this chapter on my first reading and had to re-read sections a few times to appreciate the structure of the chapter and what lean startup guidance it was offering.

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The Lean Startup: Learn [Lean Startup
Posted on January 19, 2017 @ 07:45:00 AM by Paul Meagher

This is the fourth blog in my anticipated 13 blog series dedicated to each chapter of Eric Ries' seminal book The Lean Startup (2011).

The the third chapter is titled "Learn" and the main focus is to identify "validated learning" as the key dimension that startups have to be productive at in the beginning.

Imagine that you have come up with a brilliant vision for a new software product, given yourself a 6 month timeline for launch, financed the development through investors, and after 6 months worth of long days you are ready to launch. It is then that you realize no one is willing to download your product or pay for it. You then make lots of changes to see if you can get customers to download and pay for it but your earnings peak at a measly $500 per month for many months until you start pivoting from a major aspect of your initial vision and start focusing on what your customers really value. That is the story of the eventually highly successful company, IMVU, that Eric helped found in the capacity of product designer and software engineer.

Eric was schooled in the bible of lean manufacturing, lean production, agile development and lean thinking so wasting so much time and effort in developing features and software no one wanted was deeply embarrassing. The concept of the "lean startup" emerged from this deep sense of frustration:

Anything we had done during those months that did not contribute to our learning was a form of waste. Would it have been possible to learn the same things with less effort? Clearly, the answer is yes .... As the head of product development, I though my job was to ensure the timely delivery of high-quality products and features. But if many of those features were a waste of time, what should I be doing instead? How could we avoid this waste? ~p. 48-49

Fortunately, Eric had a scientific mindset and studied his users and conducted many experiments to figure out what was working and what wasn't. He also had enough investor-financed runway to recover from his initial misjudgement where many might have gone under. Perhaps setting a 6 month launch date gave him enough time to recover and figure things out for the next 6 months. I suspect the investors weren't in it just for a 6 month experiment.

When the company started firing on all cylinders was when they starting determining what was of value to their customers and what was not. This was done through numerous changes to the product, to their website, to the value proposition to customers, to branding and then measuring whether the existing versus changed versions produced different quantitatively (or qualitatively) different results. The product that a customer eventually consumes is not just the product itself but also includes the way it is branded, marketed, and distributed into the marketplace. These have to be tested as well. If, however, the "product" is not something that customers value then they won't remain customers so validated learning should be first and foremost focused on defining what product customers will value. It should be noted, however, that when Eric changed the name of their product from "avatar chat" to "3D instant chat" signups and paying customers increased so what defines your "product" is not just the software coding and UI design.

Eric doesn't explicitly define what validated learning is in this chapter but it is easy to get a sense of what it is from this passage:

Positive changes in metrics became the quantitative validation that our learning was real. This was critically important because we could show our stakeholders - employees, investors, and ourselves - that we were making genuine progress, not deluding ourselves. It is also the right way to think about productivity in a startup: not in terms of how much stuff we are building but in terms of how much validated learning we're getting for our efforts .... This is true startup productivity, systematically figuring out the right things to build. ~ p. 51-52

We see here that the definition of validated learning is linked to "quantitative validation" using "metrics" that inform us about "the right things to build". It involves forming hypothesis about what might improve the value of your product, thinking about changes that might be made to test whether it does, and then measuring whether the difference makes a difference. This is where the learn startup should focus most of its initial time and resources, not on extensive planning exercises based on preconceptions about the expected value the product.

Keep in mind that we know quite a bit about the expected value of some products in the marketplace (e.g., coffee, housing, oil wells, etc...) but startups are engaged in creating innovative products that don't currently exist in the marketplace so the best investment of time and resources early on is to validate assumptions about product value as early as you can in whatever form you can.

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The Lean Startup: Define [Lean Startup
Posted on January 16, 2017 @ 08:08:00 AM by Paul Meagher

This is the third blog in my anticipated 13 blog series dedicated to each chapter of Eric Ries' seminal book The Lean Startup.

The second chapter is simply titled "Define" and the main focus of this chapter is to define what a startup is. Here is Eric's definition:

A startup is a human institution designed to create a new product or service under conditions of extreme uncertainty. ~p. 27

Not all businesses that are started should be considered startups according to this definition. Eric elaborates:

To open up a new business that is an exact clone of an existing business all the way down to the business model, pricing, target customer, and product may be 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).

Most tools from general management are not designed to flourish in the hard soil of extreme uncertainty in which startups thrive. The future is unpredictable, customers face a growing array of alternatives, and the pace of change is ever increasing. Yet most startups - in garages and enterprises alike - still are managed by using standard forecasts, product milestones, and detailed business plans.
~ p 28-29.

This is a pretty strict definition of what a startup is. Many people opening a new restaurant or flipping their first house might think of themselves as startups but they could be wrong according to this definition. Ideally, when you decide to flip your first house you have done some reading and acquired some renovation skills so you can carry out a "textbook execution" of the flip. To acquire the skills and concepts around flipping houses you can watch TV shows and YouTube videos, read blogs, articles, and books, and practice necessary skills in smaller projects. All of these activities will serve to reduce the uncertainty of the venture. To the extent that you have not done so, your execution might be poor because you have not planned things out as well as you could have been.

Eric is not arguing that traditional management textbooks are useless when starting a new business. It depends on the type of business and how much it uncertainty there is around it. If there is alot, then lean startup principles are more useful for managing it. If less, then traditional management strategies might be appropriate (e.g., business plans, forecasts, milestones).

If you are starting a small business is there any value to learning lean startup theory? Lean startup theory is focused upon techniques for achieving "validated learning" in the context of extreme market uncertainty. It could be argued that learning these techniques is something that all types of businesses can potentially benefit from because we are all dealing with uncertain situations to some extent. Just don't forget that the uncertainty in flipping a house is not as great so you can achieve "validated learning" in other ways that a startup is not able to (from books, videos, etc...).

If I had to reduce this chapter to a principle it would be to define whether your new venture is a startup or a small business and manage it accordingly.

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The Lean Startup: Start [Lean Startup
Posted on January 8, 2017 @ 12:50:00 PM by Paul Meagher

This blog is the first in a series of 12 blog posts decidated to each chapter in the book The Lean Startup (2011) by Eric Ries. The first chapter is simply called "Start" and it addresses what mindset you should have going into your startup venture. Eric argues that launching an explosive startup is not rocket science in the literal sense that you can't fully plan beforehand the trajectory of your business from launch to landing. There are severe limitations in our ability to predict the trajectory of a new product or service in the marketplace. Eric argues that a better way to frame the predicament of the startup is to use a driving metaphor that involves tuning the engine to perform well and continually making adjustments in light of detours to reach our destination. The "driving" metaphor suggests that more cybernetic-type control is involved.

It is difficult to determine what role a business plan is supposed to play in startups that adopt a lean methodology. Eric views traditional business plans as a predictive device that may be more appropriate for established industries and businesses where there are existing business models, market data, and financial data we might use to plan the roll out of a new product or service. An explosive startup, however, is by definition a business that must navigate its way through extreme initial uncertainty regarding the product it will ultimately offer to the customers and who the customers for the new product or service will eventually be. A startup might take the advice of traditional business texts and spend alot of time trying to plan everything out beforehand but that can be a waste of time for explosive startups as you will likely need to throw away most of your business plan when you get into the product development and consumer testing cycle.

Where does that leave us then with regards to planning your startup? Eric does not get into this much in the Start chapter, but the lean startup movement has spawned newer alternative approaches to business planning that are arguably better adapted to initial startup planning for explosive startups. They don't ask us to plan out everything in advance, but they try to make sure we at least have a business model hypothesis that we are provisionally working from and can share with others if need be. I don't get the sense that banks or more conservative investors would accept a business model canvas or lean canvas as a substitute for a business plan, but explosive startups will not want to invest alot of time up front into traditional forms of business planning. You will want to be more action-oriented and engaged in validating your ideas as soon as possible rather than get stuck in a prolonged business planning exercise.

A few days ago I had a conversation with a local entrepreneur in the process of starting a third business. I talked to him about business plans and he told me he gave up on revising his business plan for the first business after the 13th iteration and that he may never write another business plan. I asked what was the alternative. He said he is just using the pitch deck that he created to raise seed funding for his venture. His route to starting a venture was concept selling to investors using his pitch deck and using that approach managed to raise 100k so far. Now he has enough money to get down to figuring out what he needs his website to do to enact the vision and to sell the concept to customers instead of investors to get traction towards the vision so he can proceed with another Series A round of funding in a year or less.

This reminded me that there is not just one way to start a business. My friend comes from a sales background so it is probably more natural for him to start a business by selling the concept to investors rather than building a demo. When I say selling a concept, I specifically mean he believes that he has a good business model that would address a large and growing market in the health care industry. To begin testing the business model hypothesis is a $100k proposition.

One template that is often used for Lean Startup planning is the Lean Canvas, a simplified version of the Business Model Canvas. It is also called the One Page Business plan and here is what it looks like:

If you want more information about the lean canvas, there is a useful article by lean canvas creator, Ash Maurya, on Why Lean Canvas vs Business Model Canvas?

In this blog, I used the term "explosive startup" when referring to startups because lean startup advice may not be appropriate for all forms of startups. If you are starting up a coffee shop there are alot of "knowns" that you might consult to help you better plan out your business and know what your costs are, wages are, and so on. It would be foolish not to do some research on the coffee industry and demographics and formalize it into a business plan before you go looking for money to start a coffee shop. Where you have the ability to forsee the future somewhat you should try to do it to avoid the school of hard knocks. There are explosive startup scenarios, however, where you cannot predict the future that well for a variety of reasons. Here you need to be more action oriented to figure things out and the main "planning" you should be engaged in is about how to test key business model hypothesis and assumptions as fast and efficiently as you can so you can evolve that business quickly. Pitch decks and business model canvases might be the preferred business planning/selling tools in these contexts.

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Lean Startup Series [Lean Startup
Posted on January 4, 2017 @ 12:07:00 PM by Paul Meagher

I'm planning on re-reading the landmark book The Lean Startup (2011) by Eric Ries.

My reading strategy is going to be a bit different this time. I am going to adopt a reading strategy similar to the strategy I used for another landmark book I did a series of blogs about; namely, Permaculture Principles: Principles & Pathways Beyond Sustainability (2002) by David Holmgren. The Permaculture Principles book is broken down into twelve sustainability principles with wide ranging discussion around each of these principles. You can also go online and see what others have said about each Permaculture principle by googling the name of the principle (e.g., permaculture principle "Obtain A Yield"). It is common for those wanting to learn Permaculture as a set of principles to think up their own applications of each Permaculture principle and either enact it, blog about it, write a song about it, or do something that expresses their take on the principle. My approach was to blog about each principle which I did over a 4 month period from mid April to early July, 2015 (April 2015, May 2015, June 2015, July 2015).

Interestingly, the core of the Lean Startup book also consists of 12 chapters and each of these chapters could be viewed as a lean startup principle. The book is broken down into three main parts (Vision, Steer, Accelerate) with 4 principles per part:

  1. Vision
    1. Start
    2. Define
    3. Learn
    4. Experiment
  2. Steer
    1. Leap
    2. Test
    3. Measure
    4. Pivot (or persevere)
  3. Accelerate
    1. Batch
    2. Grow
    3. Adapt
    4. Innovate

For completeness, I should mention that there are also "Introduction", "Epilogue", and "Join the movement" chapters that I consider non-core and don't intend to blog about. Each of the above-mentioned lean startup principles is only one word long so it is quite open ended as to what the principle is specifically asking you to do. Each chapter/principle is stated in imperative mode (i.e., start, define, learn, experiment, etc...) which suggests that they are to be taken as stating a particular design principle indexed by that action label.

Even though I could reread this book in a few days, I will resist the urge to mow through it. I will read each chapter/principle separately, think about it, check online discussion of it, and then blog about it before moving onto the next chapter/principle. Advocates of slow food encourage people to slow down on food-related matters in order to better appreciate the many aspects of it. Likewise, I will slowing down on reading this book to better assimilate the message and relate it to my own experiences and ideas.

Because lean startup theory takes a scientific approach to starting a business, I am particularly interested in exploring the idea of whether some recent research in causal models might be used to extend or clarify some of the lean startup principles. Previously I discussed how lean startup theory might be formalized/visualized when I talked about the lean startup lens. I think more can be done to formalize/visualize what "validated learning" actually consists of, but it will involve incorporating some newer ideas about causation that Judea Pearl has been pioneering in his own landmark book Causality: Models, Reasoning, and Inference (2009, 2nd Edition):

I recommend reading the epilogue of the book (PDF link) to get a sense of issues the book addresses in technical detail.

For me, it comes down to this. Does including the word "causal" in the phrase "lean startup" add anything useful to the lean startup approach? What role might causal modelling and reasoning have in the success of a lean startup? Maybe nothing, or maybe incorporating a few of these ideas can help to make the lean startup approach stronger just as recent critical discussion of Permaculture has helped to make Permaculture stronger. I'm also interested in relating some lean startup ideas to some Permaculture ideas as there many similarities, one being the focus on design principles as a way to convey a body of knowledge.

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