Core Product Value and Entrepreneurial Success


I have previously written about Minimum Viable Product (MVP) and Product/Market Fit (PMF). These are important processes based on the scientific method that can be used to test a value hypothesis. That hypothesis does not just appear via spontaneous generation. Andy Rachleff describes what should be included in a value hypothesis as: “the features you need to build, the audience that’s likely to care, and the business model required to entice a customer to buy your product.”

At the center of any value hypothesis is “core product value” and the idea or vision behind that CPV is created by the entrepreneur.

As an example of how this process works and fits into a bigger picture, Chamath Palihapitiya’s approach has been depicted below by one of his colleagues:

top funnel

Core product value represents a solution a real problem that is valuable enough to cause people to want to pay for a product. Core product value is first recognized when the customer connects with the product in an A-Ha moment.

Core product value is an essential element of product/market fit which is a broader concept that requires additional elements. Andy Johns says: “For products that get the ‘magic moment’ and ‘core product value’ right, the top of the funnel naturally and rapidly fills.” in other words, without core product value and a magic moment it is not likely that people will convert from guests to customers. In a talk at Index Ventures Andy Johns talked about an example of what he feels is core product value:

“One current company with a clear core product value, Johns says, is Snapchat. Their core value isn’t just sexting as some like to believe; rather, it’s the removal of a designated target and mental friction from messaging. Users receiving snaps don’t have to worry about who else may be seeing a message or what their response is, and by removing that moment of hesitation, a social burden is lifted.”

You can disagree with his conclusion but I think even then you will understand his point. As another example, Johns believes that for Wealthfront, where he works now, core product value is giving any customer access to low-cost, tax-efficient, diversified investment portfolios via a direct to consumer model.

Finding a new source of core product value is: (1) very hard to do and (2) a rare event. As context, there are roughly 5,000 seed stage startups a year and only 800 of them raised a Series A round in 2016 reported Mattermark. Why do so many startups fail to successfully raise an A round? There are many reasons for this but most often it is because there is insufficient confidence among investors that the business has found or will find core product value or product/market fit. Andy Johns points out that among the right questions to ask when thinking about whether something has core product value are: Is the problem your product is solving (1) painful, (2) important for your customers, and (3) is there a sizable market behind this problem?  He adds that: “some firms create new, meaningful experiences, rather than solving an existing, painful problem. One could count Facebook, Twitter, Snapchat, Instagram, and others in this group.”

Fred Wilson describes the steps on a startup’s journey:

“The first step you need to climb is building a product, getting it into the market, and finding product market fit. I think that’s what seed financing should be used for.

The second step you need to climb is to hire a small team that can help you operate and grow the business you have now birthed by virtue of finding product market fit. That is what Series A money is for.

The third step you need to climb is to scale that team and ramp revenues and take the market. That is what Series B money is for.

The fourth step you need to climb is to get to profitability so that your cash flow after all expenses can sustain and grow the business. That is what Series C is for.

The fifth step is generating liquidity for you, your team, and your investors. That is what the IPO or the Secondary is for.”

Fred seems to be saying that he wants his portfolio companies to have product/market fit before the Series A which means they will also have discovered core product value as part of that process. It is worth pointing out that what constitutes product/market fit is always to a degree in the eye of the beholder, is not a discrete event and can disappear (e.g., a competing product emerges) and reappear like a Cheshire cat. I did a quick survey of a few VCs to get a current sense of what the standards are on product/market fit at a Series A financing. One venture capitalist I talked to said that only 5% of Series A rounds involve a company that does not have product/market fit. Another said that the percentage ranged from 10 to 40% depending on the firm and that it varies by partner. He also said that Series A rounds that do not have product/market fit “tend to skew smaller ($3-5M) and the firm tends to lead taking the whole round.” Another VC mentioned that there has been some easy grading on whether product/market fit exists and so that impacted her estimate of 20%. Another VC said: “15-20% are non-PMF A rounds, but they seem to be exceptional either by virtue of all-star founding team or a particularly hot area.” Another VC joked that that 30% of A rounds funded by VCs did not have product/market fit intentionally and another 29% failed to achieve this unintentionally. Raising funds after the seed round without proven core product value and product/market fit or can happen for many reasons including because the venture backers: (1) confuse progress on the growth hypothesis with a solution to the value hypothesis (product/market fit); (2) hope that product/market fit can still be discovered with more cash or (3) various errors in judgment caused by a really great story told by a really great story teller. The startup landscape is littered with the invisible dead bodies of failed startups.

The VCs I talked to generally said that a Series A without product/market fit was more likely if there is a huge potential market, a great story is being told and the specific venture capitalist involved is a “people person” on a relative basis.Having said that, Josh Kopelman’s advice on this issue is excellent: “Keeping your burn rate low until you have product/market fit will give you the best chance at building a big company. There’s nothing that increases your odds of a successful A round like a successful launch followed by customers that really love what you’ve built. These inflection points change year to year — so be sure you know what’s currently fundable.” Someone like Elon Musk may be able to raise an A round without core product value and product/market fit, but you are not Elon Musk, and neither am I.

As an aside, as Mark Suster and others have said:

“what actually IS the definition of a seed vs. A-round.

‘Cautionary note: No competent VC is actually fooled when you show up after raising $6M in seed financing and say you’re now raising an A!’

— Marc Andreessen (@pmarca) October 7, 2014

This is something I think entrepreneurs don’t totally understand and it’s worthwhile they do. My view: “Spending any time or energy trying to game the ‘definition’ of your round of fund raising is a total waste. Nobody cares. No VC will be so naive as not to see straight through it. And actually many will probably find the gamesmanship as a bad sign of lack of property priorities or perspective.”… If it looks like an A-round, smells like an A-round & tastes like an A-round … it’s an A-round…. if you raised $3–5 million from well-known seed funds or from a VC and you’re asking for $8–10 million in your next round … that next round is a B-round no matter what we collectively decide to call it when we VCs fund you.”

It is possible for a startup which has not proven that they have core product value and product/market fit raise a Series B? Sure. But is it likely? No. is is significantly less likely that in an A round.  Significantly fewer firms raise a Series B than a Series A. Sreies B is often called the hardest round to raise. Fred Destin writes: “Series B is hard for a simple reason: suspension of disbelief fades and is replaced by an increasingly cold, hard look at milestones and progress. Series B is the round where the rubber meets the road, where the promise has to be met with numbers and projections.”


Andy Chen has written about TTPMF – the “Time to Product/Market Fit.” Remembering that you must have core product value to get to product/market fit his view is:

“TTPMF has to be less than 1-2 years or else your startup will implode. Ask anyone who’s been working on a product for more than 2 years and doesn’t have traction to show: It really, really sucks. The first 6 months can be fun because it feels like you’re painting on a blank canvas, but soon enough, there’s just fatigue and the window of opportunity shifts. Platforms change, investors get disengaged, your employees start getting excited about other companies. So if you miss your window, then you’ll run out of money or energy or both.”

Chamath Palihapitiya has a gift for getting to the point.  You can’t make the most important point about core product value in a simpler way than this slide:



Steve Blank believes: “The best entrepreneurs are the ones who are passionate about solving a problem because they’ve had it or seen others have it, love those customers, love solving that problem or have been domain experts. Those are authentic entrepreneurs.” He believes “entrepreneurs, at their heart, are artists. … What comes out from the great artists is something completely unexpected. World class entrepreneurs understand something that is driven by passion.” He believes world class entrepreneurs are connected to their subject and with their customers.’ Blank believes entrepreneurship is a calling rather than a job.  I believe that this is why many venture capitalists describe what they do as “artisanal.” Blank believes:

“Founders fit the definition of a composer: they see something no one else does. And to help them create it from nothing, they surround themselves with world-class performers. This concept of creating something that few others see – and the reality distortion field necessary to recruit the team to build it – is at the heart of what startup founders do. It is a very different skill than science, engineering, or management. Entrepreneurial employees are the talented performers who hear the siren song of a founder’s vision. Joining a startup while it is still searching for a business model, they too see the promise of what can be and join the founder to bring the vision to life.”

The great entrepreneurs tend to be persistent, obsessive and relentless, but the really great entrepreneurs also seem to have a gift for looking at the world from a customer’s viewpoint. These entrepreneurs seem to know instinctively what the customer wants. Most artistic entrepreneur I have ever seen is Craig McCaw. He has an amazing way of putting himself in the shoes of the customer. Rich Barton is very similar in his ability to know whether (1) the customer’s problem is real and significant enough that they will pay for the solution, the market is big, and that there is a business model with a potential moat. Steve Jobs had an artist’s skills in understanding what customers wanted. Bill Gates, Jim Sinegal and Howard Schultz all fall in the artist category. The people are also missionaries rather than mercenaries.  Missionaries are far less inclined to sell the business and more inclined to build a franchise that is truly lasting. Do all founders who are missionaries, visionary, persistent, obsessive and relentless succeed?  No. But they succeed more often. I talk about this is my blog post on the wonderful Michael Mauboussin book The Success Equation. Mauboussin wrote: “The trouble is that the performance of a company always depends on both skill and luck, which means that a given strategy will succeed only part of the time. So attributing success to any strategy may be wrong simply because you’re sampling only the winners. The more important question is: How many of the companies that tried that strategy actually succeeded?” Once up a time long ago I read a book called In Search of Excellence. The authors analyzed leading companies are sorted out the secrets of success in a way that suggested that it was a formula that could be replicated easily. The best companies do X, Y and Z was the claim. What was missing of course were all the companies that did X, Y and Z and failed. Mauboussin writes:

“There are numerous books that purport to guide management toward success. Most of the research in these books follows a common method: find successful businesses, identify the common practices of those businesses, and recommend that the manager imitate them. Perhaps the best known book of this genre is Good to Great by Jim Collins. He analyzed thousands of companies and selected 11 that experienced an improvement from good to great results. He then identified the common attributes that he believed caused those companies to improve and recommended that other companies embrace those attributes. Among the traits were leadership, people, focus, and discipline. While Collins certainly has good intentions, the trouble is that causality is not clear in these examples. Because performance always depends on both skill and luck, a given strategy will succeed only part of the time.Jerker Denrell, a professor of behavioral science, discusses two crucial ideas for anyone who is serious about assessing strategy. The first is the undersampling of failure. By sampling only past winners, studies of business success fail to answer a critical question: How many of the companies that adopted a particular strategy actually succeeded?”

The best venture capitalists want to be involved in enabling entrepreneurs to be successful in this artistic process. You will sometimes hear people say “providing venture capital is just finance. You go to school and listen to a bunch of case studies and learn the formula.” That’s bullshit. I don’t know anyone with any significant degree of success in venture capital who thinks that way. The more the venture capitalist understands that finding core product value is an art and what they do is provide a service that goes beyond finance, the better their financial result.  The best venture capitalists spend a lot of time listening, let the founders do the heavy lifting and do not try to supply the vision (“You do not want a venture capitalist who hires a dog and then tries to do the barking”). Marc Andreessen says: “You want to have as much ‘prepared mind’ as you possibly can. And learn as much as you can about as many things, as much as you can. You want to enter as close as you can to a zen-like blank slate of perfect humility at the beginning of the meeting saying ‘teach me’…. We try really hard to be educated by the best entrepreneurs.”

Some of the best venture capitalists are people who ask great questions that help the entrepreneur find core product value and get to product market fit. Bruce Dunlevie is a great example of someone who has a service mentality in his work as a venture capitalist. Many entrepreneurs trust him implicitly since they know he asks great questions and has sound judgement. Here’s a story told by Jeff Hawkins about that skill:

“Hawkins: Yes, Palm was struggling. We had 27 employees, we had a couple of million dollars left in the bank. All of our partners had abandoned us on doing Zoomer 2. No one was interested in doing PDAs at all, and there was no real business selling PalmConnect and Graffiti. We were kind of bummed out, everyone was sort of miserable about it. But I still believed in the mobile computing space. So Donna Dubinsky and I went and visited one of our VCs one time, Bruce Dunlevie. We were sitting in his office and we were complaining about how our partners had abandoned us and how everything was hard, and Bruce said– my recollection was in an annoying tone, “Well, I don’t want to hear you complain about this. Do you know what you should be doing?” Something along those lines. And I said, “Yes, I know what we should be doing,” although I had no idea what we should be doing. But I said, “I can think of it”– immediately I said I can think of what we should do. If you ask me, I’ll tell you what we should do, something different. It occurred to me right away. I said, “Well we should do a new computer and we’re going to take everything we’ve learned and fix all the problems and do it again. That’s what we should do.” I didn’t know what that would look like yet because we had never really considered doing the whole computer again ourselves. We were still trying to work with Casio and GeoWorks and other people. And Bruce said, “Well if you know what to do, why don’t you go do it?” And our answer was, “We don’t really have the money to do that, we don’t really have the right type of people to do that– we only have software people. But if you think we can, if you don’t mind us trying, we’ll go do it.” And that was the beginning, the genesis of the Pilot. That night I went home and– I’m not sure, I think it was that night, maybe it was the next night, I don’t remember, I think it was that night.”

Here is an example of what WeWork’s Adam Neumann says about Dunlevie’s contribution:

“One example of this is Benchmark Capital, one of our investors. It’s a very successful VC firm, that works with companies like Uber, Snapchat, and Instagram. The partner that brought me in, Bruce Dunlevie, one of the original founders, is one of the smartest people I’ve ever met. Immediately after I met him, he became one of my five to seven close “advisors” that I asked a lot of both business and personal questions.”

There are many other successful entrepreneurs who tell the same story about Bruce. Someone I know said once: “Most stories about Bruce revolve around him being the world’ greatest person who is the best advisor anyone could ever hope for.” Those are qualities that an entrepreneur should seek in a venture capitalist. Dunlevie said to me once: “pattern recognition is an essential skill in venture capital.” While the elements of success in the venture business do not repeat themselves precisely, they often rhyme. In evaluating companies, the successful venture capitalist will often see something that reminds them of patterns they have seen before. It might be the style, chemistry or composition of the team or the nature of the business plan. Some things will be fundamentally different but other things may be familiar. While the pattern will be similar, something in what the team is doing will seem to break a rule. Part of the pattern that is being recognized is a rule breaking innovation of some kind which drives new value.

Creating “core product value” by finding a value hypothesis that is capable of being the foundation of a valuable business is a process similar to alchemy says Benchmark Capital’s Peter Fenton:

“Doing this job for almost 20 years now has taught me far more about people than about business. So let me first answer what I’ve learned about business, and in this case I mean the business of investing in startups. I started out as someone who had all the conceptual overhead needed to sound intelligent in our world, Porter’s 5 Forces, the Innovators Dilemma, and Crossing the Chasm. I would, in my former firm’s parlance, develop a “prepared mind” in a sector so I could see where the logical opportunities should exist. I became an expert on Storage, on Application Software, on Supply Chain. All of that, I came to realize, was useless without the alchemy of an entrepreneur who was playing around with explosive market forces. Yes we can look, and it helps to look with a lens, but the best ideas and companies aren’t filling logical white spaces. They are touching nuclear reactor of some force that will yield, and yield quickly, to an entrepreneurial leader.”

“I also came to realize that at the beginning, no analysis can capture ‘what can go right’ without sounding like you are clinically insane. Having seen the Series A pitch for Facebook, Uber, Snap, Twitter, Vmware…$1B in revenue for any of those companies would have been nearly impossible to imagine. Yet in each of those cases, I vividly remember the meetings, the day, the setting…and this feeling that an exceptional entrepreneur had touched on something nobody else had understood at their level of depth and insight. Each in its own way felt limitless. I’ll never forget meeting Evan Spiegel in 2012 at Sightglass in SF and leaving thinking, I know with all of my being that this person, this product, will give humanity back the playful joy of self-expression, which had been stolen away by then current social networks. Sometimes it’s obvious.”

What else helps someone find core product value? Domain expertise, beginner’s mind, and a personal desire to solve a problem that has caused the entrepreneur genuine personal pain. Jim Goetz of Sequoia believes:

“Many of the entrepreneurs that we back are attacking a personal pain.” “The common thread [between Sandy Lerner and Len Bosack (the founders of Cisco), Reid Hoffman (LinkedIn) and Omar Hamoui (AdMob)] is that these were all sketchy misfits, unknowns, who all focused on [solving] personal pain points and were all willing to put something out early and iterate.”

The best case happens for the venture capitalist when someone has the savant qualities I described when it comes to products and is attacking a personal pain that the care about is a missionary fashion. Michael Moritz of Sequoia not surprisingly has the same views as Goetz: “When we help organize one of these companies at the beginning, it never looks like the world’s greatest idea. I think it’s the marketing and PR department that rewrites history and tells you that it was always the world’s greatest idea. What they don’t say is that at the very beginning there was great uncertainty and a great lack of clarity.” “We just love people who perhaps to others look unbackable. That has always been our leitmotif of doing business.” “If you have been around the start of success it is far easier to recognize it again.”

Steve Blank said this during a GigaOm video interview: “I did this at SXSW. I said ‘There are 500 people in this room. The good news is, in ten years, there’s two of you who are going to make $100 million. The rest of you, you might as well have been working at Wal-Mart for how much you’re going to make.’ And everybody laughs. And I said, ‘No, no, that’s not the joke. The joke is all of you are looking at the other guys feeling sorry for those poor son-of-a-bitches.’” Financial success in creating and funding startups follows a power law.  This means that a very small number of startup founders, employees and investors will reap most all of the financial rewards.  The overconfidence heuristic will make most everyone overconfident that the winners will include them. The inevitable failures are hard for individuals, but the right thing for society as a whole.

One thing is clear: if an entrepreneur wants to discover core product value they should find a venture capitalist who knows that journey well, has a service mentality, asks great questions and has sound judgment.


Chamath Slide Deck:

Fenton in Quora:

Andy Johns:

Index Ventures:

Alex Schultz:

Bruce Dunlevie story re Palm:

Bruce Dunlevie interview:

Fred Wilson:

Steve Blank: and

Andy Chen:

Adam Neumann:

Mark Suster:

Josh Kopelman:

A Dozen Ways “Virality” Can be Misused and Misunderstood


1. “The most important thing that we did [at Facebook’s] was I teased out virality and said you cannot do it. Don’t talk about it. Don’t touch it. I don’t want you to give me any product plans that revolve around this idea of virality. I don’t want to hear it.”  Chamath Palihapitiya.

I have the same concerns Chamath is talking about in writing more about virality than I did in my previous post on product/market fit. If Lord Voldemort is He Who Must Not Be Named then virality is perhaps the Business Concept That Must Not Be Named. But since you will hear the term virality so often I have concluded that it is worthwhile to discuss the concept further.  The term virality and ideas that underlie it are borrowed from epidemiology. There is math involved, but if I dig too far into that math now you may stop reading. The best summary description I have found for the non-math inclined was written by Watts, Peretti and Frumin:


2. “A viral product is one whose rate of adoption increases with adoption. Within a certain limit, the product grows faster as more users adopt it.” Sangeet Paul Choudary.

that word

If a business’s product is able to grow organically by means of direct customer-to-customer interaction it is viral. For example, Twitter and Instagram both have viral attributes. Twitter has encountered greater limits on its customer growth as it reached very large numbers of users and that has been disappointing to shareholders. Every business has an upper limit on growth and it is just a question of where and where they appear. The point where growth plateaus for any business is determined in no small part by the size of the addressable market. Andrew Chen has written a post in which he describes a business that has saturated its market as having” jumped the shark” which I think is apt. If a company like Twitter reaches a point where customer growth plateaus it has several logical choices. It can: (1) try to increase revenue per customer; and/or (2) try to create and grow profitable complementary services that serve new addressable markets.

3. “If you don’t delight a customer you don’t create a viral effect because delight is the greatest form of virality.” Andy Rachleff.

If you love a product you are going to tell your friends. If someone tells you about a product and it is not lovable you will stop using it. This simple idea reminds me of a well-known Warren Buffett quotation: The only way to get love is to be lovable. It’s very irritating if you have a lot of money. You’d like to think you could write a check: ‘I’ll buy a million dollars’ worth of love.’ But it doesn’t work that way.” Rachleff believes that a focus om growth before the value hypothesis has been solved is dangerous to the financial health of a business.

Rachleff points to Netflix as an example of a company that is totally focused on delighting its customers instead of being paranoid about competitors. Here Netflix is parting ways with the Andy Grove dictum about being paranoid about competitors and instead being focused on delighting customers. He quotes Reed Hasting’s as saying: “being paranoid about competition is the last thing you want to do because it distracts you from the primary job at hand: Delighting the customer.” Especially in a technology business where one company can dominate a market due to network effects, losing focus on delighting customers can be a fatal mistake. Steve Blank puts it this way: “Why are so many founders so reluctant to invest even 500 or 1,000 hours upfront to be sure that, when they’re done, the business they’re building will face genuine, substantial demand or enthusiasm?  Without passionate customers, even the most passionate entrepreneur will flounder at best.” 

Charlie Munger tells the story of young people approaching him and asking how they can become as rich as he is, but much faster. This desire to get rich quick or create a successful company quick can cause people to make serious mistakes. This most often happens because people seek shortcuts like trying to work the growth and value hypothesis at the same time. If it were easy and fast to do solve the value hypothesis by creating core product value protected by a moat everyone would be rich. The reality is that is no substitute for solving the value hypothesis first. People tell their friend about businesses like Netflix and Costco because the product is delightful. Bill Gurley writes: “’Wow’ moments lead to word-of-mouth viral growth and high net promoter scores.” That is the best type of virality a business can have.

4. “Products that exhibit viral growth depend on person-to-person transmission as a necessary consequence of normal product use. Growth happens automatically as a side effect of customers using the product.” Eric Ries.

Growth should ideally be driven by a natural byproduct of customers generating core product value from their use of the product. The less optional the sharing activity the more naturally viral the product is. Customer should derive value from sharing the product with others without the process feeling forced. Three slides from a presentation by Anu Hariharan of A16z helps clarify different types of viral growth and what the objective of viral growth should be:





The need to generate positive word of mouth is greatest when the business is offering a consumer product which has low average revenue per account (ARPA) relative to what it would be for an enterprise product. This chart from a lecture by Christoph Janz nicely describes a continuum. The lower the revenue per account (mice and insects)the less the customer acquisition cost (CAC) can be and still create lifetime customer value.  


Unfortunately the pressure to keep CAC low can create tremendous pressure to use clever tricks and hacks that may get in the way of delighting a customer. Spam is spam, even if it comes from someone who calls them self a growth hacker.

5. “Virality is something that has to be engineered from the beginning…and it’s harder to create virality than it is to create a good product.  That’s why we often see good products with poor virality, and poor products with good virality. Josh Kopelman. 

Building enough delight in to a product from the start is an essential element of virality. The goal is to make the customer say “WOW” when time they use the product. Roelof Botha agrees with Kopelman: “Forget about adding ‘viral’ to your marketing to-do list after your product is already on the market. You need to bake it into your business model from the very beginning. Viral isn’t something you can just make happen. It has to be inherent in your product.” Andy Rachleff writes:

“Facebook cut its teeth in the Ivy League without spending a nickel on marketing (or growth as they call it) before making its product more broadly available. Once the company had incredible traction, it broadened its reach. The same can be said for just about every franchise technology company we know (for example: Adobe, Apple, Google, LinkedIn, Oracle, Salesforce, and Twitter). The classic counter-example is Groupon. It ramped up the hiring of salespeople well in advance of determining if it offered customers (merchants) a compelling value proposition… post IPO, the market realized Groupon didn’t have a value hypothesis. In other words, Groupon was able to succeed for a while without a proven value hypothesis, but sooner or later the truth catches up with everyone.”

6. “Most viral acquisition is built around incentives. Users are incentivized either explicitly (with a clear dangling carrot) or implicitly (through product mechanics) to invite other users.” Sangeet Paul Choudary.

One approach is to offer existing customers an additional amount of a free service for every successful customer referral. Choudary has created a taxonomy of incentives that includes categories like Network Value, Single-Player Value, Interaction Value, Immediate Value and Mutual Value. For example, in the Network Value category he cites “Draw Something where users may invite friends because they get interesting opponents in the longer run.” He adds: “Of course, platforms may use a combination of the above strategies. Dropbox uses a combination of Network Value, Single-Player Value and Mutual Value to incentivize users. Groupon uses a combination of Immediate Value, Interaction Value and, to some extent, Mutual Value.” Choudary has a great post on his blog about how natural incentives can be created:  “Today’s social startups don’t start off as networks. They start off as standalone apps. These products enable users to create a corpus of content first. They then connect the users with each other as a consequence of sharing that content.”

7. “The goal of all viral efforts is to insert (or “incept”) an idea of what a product can do into someone else’s head, and to get them so excited about it they want to try it and use it. Remember, at the end of the day, there’s only one metric that really matters. How many people are actually using your product.” Josh Elman.

In terms of the right approach and additional metrics, Adam Nash suggested this approach in a slide deck:


8. “Without first creating approximate viral memes that are (a) logically consistent a site’s primary value proposition and (b) resonate with something fundamental in the audience’s psyche, its virtually impossible to jumpstart a viral growth cycle.”  Ravi Mhatre.

Delighting customers with magic moments are critical to creating sustainable viral growth. These magic moments are sometimes referred to as “A-Ha moments.” Whatever they are called the objective of the business is to create an emotional affinity with the product for its customers. Delight and love are strong words but they are the right words. Chamath Palihapitiya describes the objective simply:How to get them to an “A-ha” moment as quickly as possible? And then how do you deliver core product value as often as possible?” Instagram, Snapchat and WhatsApp are businesses were successful in creating magic moments and therefore viral growth. Hotmail is often used as an example to illustrate viral growth. Sabeer Bhatia and Jack Smith developed a system that displayed an email displayed on a web page. People tend to forget how much delight that product created with customers. An email on a web page that could be accessed from anywhere for free was magical at that time. In terms of a built in mechanic that enabled virality on top of the underlying customer delight each Hotmail message had the words: “Get your free email at Hotmail” at the bottom. Clicking on those words took the person to the Hotmail signup page that explained that the service was free and accessible from any computer. Hotmail was able to acquire more than 12 million subscribers in 18 months despite spending less than $500,000 on marketing. Just the clickable mechanic without the customer delight would not have worked for HotMail.  

9. “No single feature determines the virality of the product – instead, it’s part of a viral loop that connects a disparate set of functions into a cohesive motivation for the user to tell their friends.” Andrew Chen.

If the business can reinforce the magic moments in a feedback loop that continuously reinforce core product value that generate not only growth but a moat against competition. If data is collected during that process the moat gets even stronger. Adora Cheung pointed out in a Stanford Class called How to Start a Startup: “there are three types of growth. Sticky, viral, and paid growth. Sticky growth is trying to get your existing users to come back and pay you more or use you more. Viral growth is when people talk about you. So you use a product, you really like it and you tell ten other friends, and they like it. That’s viral growth. And the third is paid growth.”  It’s hard to overemphasize the value of retaining customers (stickiness) in generating lifetime customer value. As I pointed out in my post on growth, the best way to grow is not to shrink. This may seem like a paradox but it is clear when you do the customer lifetime value math.

10. “My biggest fear was we spam our users and we trick them and it will alienate these people. You won’t see it today but you’ll see in three years from now or four years from now, and it accelerates when you compound that with a competitor who actually builds a better product that doesn’t alienate people.” Chamath Palihapitiya.

Using tricks to generate invitations when there is no core product value is suicidal. Virality without product market fit means what will be communicated virally is that the product sucks. Alex Schultz describes a healthy process where the business actually builds a better product as follows:

“with virality, you get someone to contact import that site. Then the question is, how many of those people do you get to send imports? Then, to how many people? Then, how many click? How many sign up? And then how many of those import. So essentially you want people to sign up to your site to import their contacts. You want to then get them to send an invite to all of those contacts – ideally all of those contacts, not just some of them. Then you want a percentage of those to click and sign up. If you multiply all the percentages/numbers in every point in between the steps, this is essentially how you get to the point of ‘What is the K factor?’ For example, let’s says 100 people get an invite per person who imports, then of those, 10% click, and 50% sign up, and of those only 10 to 20% import, you’re going to be at 0.5 – 1.0 K factor, and you’re not going to be viral. A lot of things like Viddy were very good at pumping up stories. They got the factor over 1, which is perfectly doable. But if you’ve got something that doesn’t have high retention on the backend, it doesn’t really matter. You should look at your invite flow and say ‘okay, what is my equivalent to import, how many people per import are invites sent to, how many of those receive clicks, how many of those convert to my site, how many of those then import,’ in order to get an idea of you K factor. The real important thing is still to think about retention, not so much virality, and only do this after you have a large number of people retained on your product per person who signs up.”

11. “The most disappointing answer is when [entrepreneurs] say ‘Oh, we’ll just make it viral.’  As if virality is something you can choose to add in after the product is baked – like a spell checker. The reason that over $150 Billion is spent on US advertising each year is because virality is so hard.  If virality was easy, there would be no advertising industry.” Josh Kopelman. 

The cost of acquiring a customer is never zero especially after you consider cost of goods sold (COGS) that is often hidden CAC (e.g., freemium). There is nothing in a viral marketing approach that is inconsistent with mass media advertising, including spending on marketing to create a seed of customers that has viral attributes. Marc Andreessen agrees with Kopelman: “Many entrepreneurs who build great products simply don’t have a good distribution strategy. Even worse is when they insist that they don’t need one, or call no distribution strategy a ‘viral marketing strategy. Andreessen Horowitz is a sucker for people who have sales and marketing figured out.”’ As an example, Blake Masters writes about a lecture Peter Thiel gave that discusses virality and seeding a market as follows:

“The PayPal team reached an important conclusion: Business development didn’t work. They needed organic, viral growth. They needed to give people money.  So that’s what they did. New customers got $10 for signing up, and existing ones got $10 for referrals. Growth went exponential, and PayPal wound up paying $20 for each new customer. It felt like things were working and not working at the same time; 7 to 10% daily growth and 100 million users was good. No revenues and an exponentially growing cost structure were not. Things felt a little unstable. PayPal needed buzz so it could raise more capital and continue on. (Ultimately, this worked out. That does not mean it’s the best way to run a company. Indeed, it probably isn’t.).”

12. “There are many products that exhibit virality without exhibiting network effects. A case in point being email and cross-platform communication products. There are many others that exhibit network effects without exhibiting virality. Products with indirect network effects such as marketplaces may not grow virally.” Sangeet Paul Choudary.

I have already written a post about network effects but this is the first time I have written specifically about virality. The two terms are often confused since they can occur at the same time. Network effects exist when a product gets more valuable the more people use it. Network effects are about increasing value and drive business success by increasing the size of a business’s moat. Virality is about increasing speed of adoption and lowering customer acquisition cost (CAC). As an example, the game Angry Birds was viral, but it did not have network effects. Many multi-sided marketplaces have network effects, but are not really very viral. Facebook is viral and has network effects. Andy Rachleff provides the best closing quote for this post by reinforcing a key idea this post has tried to drive home: “Network effects often drive virality. But another thing that drives virality is delight.” When I took driver education classes years ago the teacher would say that if you found yourself headed for a telephone pole look where you want the car to go not at the pole or else you wold steer into the pole.  Similarly a startup should look at delighting its customers with magic moments and avoid the pole which is an early focus on the growth hypothesis.


Rachleff Twenty Minute VC podcast:

Rachleff Essay in Fast Company:

Rachleff on First Round Review: 

Watts, Peretti and Frum:

Anu Hariharan A16Z Slide Deck

Bill Gurley:

Sangeet Paul Choudary:


David Skok: 


Andrew Chen:

Virality vs Network Effects

How to Start a Startup:

Chamath Palihapitiya

Incentives: How to Engineer User Growth and Virality

The Five Types of Virality

Steve Blank:

Seven Ways to go Viral:

How to measure the product virality

Why Trello Failed to Build a $1 Billion+ Business:

From 0 to $1B – Slack’s Founder Shares Their Epic Launch Strategy





You have Discovered Product/Market Fit. What about a Moat?

I have previously written blog posts about (1) growth, (2) product/market fit and (3) minimum viable product. The most logical topic for the next post is: Why does a business need a moat? The answer is simple: even if a business discovers solutions to the value hypothesis and the growth hypotheses without a moat the probability of the business being financially successful over time is remote. Revenue alone is not enough to sustain a business given the inevitable competitive response. A sustained return on invested capital is a prerequisite for the long-term survival of a business. In other words, “for what shall it profit a business, if it shall discover solutions to the value a growth hypotheses, but fail anyway because it does not have a moat?” At worst, the business without a moat is never profitable (like At best, the business without a moat is profitable for a while, but over time is gradually overtaken (as may be happening right now to GoPro).

Questions about the creation, maintenance and destruction of moats are the most fascinating and challenging aspects of business and investing. This is true because what Joseph Schumpeter called “creative destruction” is more powerful than any phenomenon in business. Michael Mauboussin says it best: “Companies generating high economic returns will attract competitors willing to take a lesser, albeit still attractive return, which will drive aggregate industry returns to opportunity cost of capital.”

The moat creation and destruction process is similar to what happens during evolution in nature. What’s an example of a specific moat analogy from nature? The sword-billed hummingbird is a species from South America. The bird’s very long sword-like bill acts as a moat against competitors by allowing it to reach a unique source of nectar from long-tubed passion flowers.


Why did I select this hummingbird to illustrate my point? First I wanted to leverage the fact that you may have recently watched one notable episode the BBC’s Planet Earth series. Second, while the humming bird has a moat due to its long beak, the bird’s market is limited to a small number of flowers in a relatively small territory. Some moats are operative in small markets and some are big. Twitter’s moat may only protect something that generates $600 million a quarter in revenue, which some people might consider to be relatively small like the hummingbird’s territory. Or Twitter’s revenue may grow much larger. Therein lies much of the fun and challenge in investing. As an aside, since I know you want to know, hummingbirds do tweet.

Mistakes are easy to make when trying to make predictions about moat strength, value and duration. For example, even if a business currently has a moat, that does not mean it will continue to do so for very long. Some businesses were at one point very highly valued since investors mistakenly thought they had a strong moat in a large and valuable market. GoPro would seem to be an example:


Predicting the future of a moat is so hard because the markets in which they operate are complex and adaptive. I wrote about why it is hard to predict the future in this post. Factors that can create a moat are constantly in flux and because they often interrelate to create nonlinear positive and negative changes. An example of negative outcomes for a business from a shift in the strength of a moat is what happened to the newspaper industry when publishers lost their physical distribution-based moat.

Without a moat this can happen:

“There are all kinds of wonderful new inventions that give you nothing as owners except the opportunity to spend a lot more money in a business that’s still going to be lousy. The money still won’t come to you. All of the advantages from great improvements are going to flow through to the customers.” Charlie Munger

The point Munger just made so clearly is counter-intuitive for many people, but essential to understand. Moat creation is incredibly hard and rare and maintaining one is hard as well. It is a big mistake to confuse a moat shortage with an innovation shortage. Some innovation does not produce any profit and in fact can destroy profit. For every firm creating disruption, some other firms are being disrupted.

The test of whether a moat exists is quantitative, even though the factors that create moats are qualitative. If a business has not earned returns on capital that substantially exceed the opportunity cost of capital for three to five years, it does not have a moat.  That is quantitative. As for the qualitative side of this topic, there are no formulas or recipes that govern the creation and sustainability of moats, but there is enough commonality that you can get better at understanding how they are created and whether they can be maintained over time. Charlie Munger told Howard Marks once: “It’s not supposed to be easy. Anyone who finds it easy is stupid. There are many layers to this and you just have to think well.” The existence and need to understand the many layers Munger is talking about explains why there are so many different posts on this blog. And why Warren Buffett believes that business is the most interesting game ever invented. The need to “learn more about more” never ends. Ever. What are these “layers” that Munger is talking about?  Marc Andreessen puts it this way:

“I have always been a fan of something that Andy Rachleff taught me years ago, which he calls the onion theory of risk. Which basically is, you can think about a startup like on day one, as having every conceivable kind of risk and you can basically make a list of the risks. So you’ve got founding team risks, are the founders going to be able to work together; then you have product risk, can you build the product; you will have technical risk, maybe you need a machine learning breakthrough or something. Are you going to have something to make it work, or are you going to be able to do that? You will have launch risk, will the launch go well; you will have market acceptance risk, you will have revenue risk. A big risk you get into with a lot of businesses that have a sales force, is that can you actually sell the product for enough money to actually pay for the cost of sales? So you have cost of sales risk. If you are a consumer product, you have viral growth risk. So a startup at the very beginning is just this long list of risks, right, and the way I always think about running a startup is also how I think about raising money. Which is a process of peeling away layers of risk as you go.”

Among the risks Andreessen talks about are technology, product, market, competition, timing, financing, distribution, marketing, hiring and founder. Each must be retired at some point by the business. The existence of a moat is critical to reducing competition risk. In my blog post on Eugene Kleiner I quote him as saying“Risk up front, out early.” A famous venture capitalist said to me that Kleiner: “Always had a strong bias for eliminating the biggest risks quickly, which was much more relevant in the days of backing companies with high technical risk and low market risk.” Another famous VC who knew Kleiner well wrote to me that what he meant by this sentence was: “Reduce the biggest risks first for the fewest dollars. This may mean out of order execution to minimize loss in case of failure.”

I view the great moat creators of the world as artists. When someone like Rich Barton creates or is involved in the creation of successful business after successful business (Expedia, Zillow, Glassdoor, Avvo, Realself, Nextdoor) when the failure rate for startups is as high as it is, I can’t help but be impressed. Bill Gates created several moats for different product as did Steve Jobs. When someone does something repeatedly you can be assured that the skill to luck ratio weighted strongly toward skill.  One point is clear from the numbers (AKA, empirical evidence): moat creation in a really large and valuable market is rare event. This must be the case since the number of financial exits is top-down constrained by the size of the economy and its ability to absorb profitable new businesses. Venture-backed businesses overwhelmingly fail financially as I wrote in my post last week on minimum viable products.

The major factors that can create a moat are:

  1. Demand-side Economies of Scale

Demand-side economies of scale (also known as “network effects”) result when a product or service becomes more valuable as more people use it. Microsoft, Amazon, Google, Facebook and other multi-sided markets have demand-side economies of scale that operate on their behalf. Network effects represent the most valuable factors creating a moat since the benefits of demand-side economies of scale can increase in business value a nonlinear manner, especially in software businesses. Moats created by network effects are vastly more scalable than other types of moats. This means that the benefits realized by the major software-based platforms are far larger than those realized by a large steel or cement producer based on supply-side economies of scale. Network effects are extremely hard to create and, as Blackberry found, can be very brittle.  Of all the factors that can create a moat, nothing is more important than network effects in my view. A great example of the value of network effects are Bloomberg terminals. The more people who use these terminals the more valuable they become to other users. The FT writes:

“Bloomberg’s pioneering instant messaging and chat rooms, not data or news, are arguably one of the biggest drivers of its dominance. The bond market — where trading mostly happens discreetly between fund managers, brokers and banks, rather than on bourses — is particularly dependent on the Instant Bloomberg messaging function. But “I’ll IB you” has become lingua franca across the financial world. The dominance of Bloomberg chat is a significant “economic moat” for the company.”

  1. Supply-side Economies of Scale

A business generates supply-side economies if per-unit costs fall with increasing output. Economies of scale, with a few rare exceptions, are exhausted well before businesses dominate the entire market.” For example, despite having significant supply-side economies of scale, General Motors never was able to obtain 100% market share. Costco has supply side scale economies of scale that help create its moat, but it is not even the only warehouse club in terms of market share. Costco is nevertheless a hugely valuable business that is Charlie Munger’s favorite business after Berkshire Hathaway. Both Amazon AWS and Microsoft Azure have supply-side economies of scale that benefit their business.

  1. Brand, Patents and Intellectual Property

Charlie Munger and Warren Buffett discovered soon after they bought See’s Candies that they could regularly raise prices and customers did not seem to care. Buffett and Munger call this ability “pricing power.” Charlie Munger has pointed out that before See’s Candies: “We didn’t know the power of a good brand. Over time we just discovered that we could raise prices 10% a year and no one cared. Learning this changed Berkshire. It was really important.” People do conduct surveys and try to rank brands which is in my view is the equivalent of guessing.


A patent or other form of intellectual property like trademarks or copyrights can create a moat. Qualcomm is an example of a company that has created a moat mostly via intellectual property. Open source makes moats on some areas of the software business problematic. Proprietary software kept secret in a server does not need to have the same intellectual property protection as client side software.

  1. Regulation:

There are certain businesses which have created a competence with regard to regulation that is so high that regulation serves as a moat. As an example, lawyers and other professional are able to reduce supply and create a moat through regulation. As an example, having the regulatory expertise to qualify to do business as a web services provider on a global basis on behalf of customers is a form of moat.

Can great management or better business execution create a moat? Warren Buffett’s famous quip on that point is: “When a management with a reputation for brilliance tackles a business with a reputation for bad economics, it is the reputation of the business that remains intact.” Professor Michael Porter agrees: “It’s incredibly arrogant for a company to believe that it can deliver the same sort of product that its rivals do and actually do better for very long.” Competition will in that case eventually be based on price and price-based competition inevitably degrades to a point where profit disappears. This is not to say that great management is not highly valuable. It is. But people like Buffett and Porter believe it isn’t enough to reliably sustain profitability over long periods of time. Some companies which execute operationally have a great run of success but eventually fall victim to competition catching up with best practices.  Buffett puts it this way: “The key to investing is not assessing how much an industry is going to affect society, or how much it will grow, but rather determining the competitive advantage of any given company and, above all, the durability of that advantage. The products or services that have wide, sustainable moats around them are the ones that deliver rewards to investors.”


Mauboussin- Measuring the Moat

A Dozen Things I’ve Learned from Charlie Munger about Moats

Lecture 9 How to Raise Money

FT on the Bloomberg Terminal:

Eugene Kleiner

A Dozen Lessons about Minimum Viable Products

  1. “It’s only cheap to build 2-3 person companies with sweat equity. The minute you start paying engineers you will realize it is quite expensive.” Bill Gurley.  Assume a startup has raised a seed round of ~$2 million. Also assume that what the startup has is a hypothesis that a big market composed of dogs will want to eat the dog food described by the hypothesis. The founders of the startup have no proof that their hypothesis is true, but some investors have voted with their money that there is significant hope that the startup’s hypothesis is correct. Every penny of the $2 million raised by the startup is precious. If the startup runs out of cash it is dead, since that is the only unforgivable sin in business. Now let’s look at the overall context in which this is happening. The odds that the startup will be financially successful are, simply put, not good. How many startups raise a seed round? There is no way to know for sure since many startups at seed stage live and die and don’t leave a trace that can be tracked. Reported seed stage startups typically number about 1,200 in a given a quarter (plus or minus a couple of hundred) depending upon the business climate.  Assuming ~5,000 seed stage startups a year both reported and unreported, only 800 of them raised a Series A round in 2016 says Mattermark. That’s about an 84% fatality rate just at seed stage. Mattermark also calculates the odds of survival here at far less than 10%. This calculation is based simply on a startup not getting to the next phase.


Other research, which uses different definitions, concludes:

About 75% of U.S. venture-backed start-ups fail, according to Harvard Business School senior lecturer Shikhar Ghosh. Ghosh’s research estimates 30% to 40% of high potential start-ups end up liquidating all assets–a failure by any definition. But if a start-up failure is defined as not delivering the projected return on investment, then 95% of VC companies are failures, Ghosh said.

Being a founder or early employee of a startup is not a rational act given the odds of success. Of course, as George Bernard Shaw wrote in Man and Superman: “all progress depends on the unreasonable [human being].” The reason why books like The Hard Thing about Hard Things by Ben Horowitz and Shoe Dog by Phil Knight resonate so strongly with people who have been involved in startups is that they accurately describe the terror, inevitable setbacks and daily struggle of life in a startup business, not just the seemingly glamorous parts. .

  1. “If you create something and no one uses it, you’re dead. Nothing else you do is going to matter if people don’t like your product.” Jessica Livingston. The first rule of startups is that without making something that people want to buy, you’re dead. The second rule is that you should not forget the first rule. Particularly when the odds of survival are low in an activity, it pays to be very aware of methods that can increase the probability of survival. Michael Mauboussin’s advice should be front and center in every founder’s mind: “If you compete in a field where luck plays a role, you should focus more on the process of how you make decisions.” What should that process be for a startup? In thinking about the right process it is wise not to forget that the startup’s goal is to establish product/market fit before they run out of money. Unfortunately, at the very early stages of the startup’s existence it faces many challenges related to at least one untested hypothesis. “Hypothesis”, of course,  is just a fancy word for “guess.” Steve Anderson the founder of the seed stage venture capital firm  Baseline Ventures points out: “Generally speaking, most of my investments are pre-product launch – they’re just an idea. My goal as an investor is to make sure there’s enough financing to give companies time to do that, a year to 18 months. The worst scenario is to try to raise more money when you haven’t achieved that goal. If you don’t have it, eventually you’ll run out of cash, say the experiment is wrong, and fold up your tent. That’s why when I invest I want to leave enough room for pivoting or reexamining your goals.” Making matters even more challenging for the early stage startup is the point Ev Williams makes here: “You know that old saw about a plane flying from California to Hawaii being off course 99% of the time—but constantly correcting? The same is true of successful startups—except they may start out heading toward Alaska.”
  1. “A full executive team with a salesforce and all that stuff before you have a killer product is a complete waste of time.” Marc Andreessen.  A startup should defer spending time and energy proving and developing its growth hypothesis until it has established the value hypothesis. I have recently written a blog post on precisely this “first value THEN growth” point here. The key point in that post is made by Andy Rachleff: “A value hypothesis identifies the features you need to build, the audience that’s likely to care, and the business model required to entice a customer to buy your product. Companies often go through many iterations before they find product/market fit, if they ever do.” When the startup is still searching for the elements of its value hypothesis, money and time spent on growing the business is a bonfire of cash generating zero value. The early days of the life of a startup are focused on “search” rather than “execution” advises Steve Blank, a serial entrepreneur, professor and author who is justifiably famous in the startup world.
  1. “The minimum viable product (MVP) is that product which has just those features (and no more) that allows you to ship a product that resonates with early adopters; some of whom will pay you money or give you feedback.” “The lesson of the MVP is that any additional work beyond what was required to start learning is waste, no matter how important it might have seemed at the time.” Eric Ries. The goal of the MVP process is to validate the hypothesis in a speedy and cost efficient manner. The key word in this quote from Ries above is feedback since that is how anyone learns. The most effective processes are based on feedback loops which are in turn based on the scientific method: build, measure, learn. What the startup offers as its MVP should be compete in what it does to deliver and capture value, not a fully complete implementation of the vision. The MVP is an experiment that is intended to generate validated learning about what customers value enough to pay for. An MVP approach is not the only way to go forward with a startup. Eric Ries describes two extreme alternatives:

“One, which I call maximizing chance of success, says ‘Look, we only got one chance at this so let’s get it right.’ We’re going to ship it when it’s right and that actually is perfectly rational. If you only have one shot, you want to take the best shot you can and build the most perfect product you can. The issue is, of course, you know, you can spend, I don’t know, say five years of stealth R&D building a product you think customers want and then discover to your chagrin that they don’t. The other possible extreme approach is to say, ‘Well, let’s just do ‘release early, release often.’ This approach is: ‘Look, we’ll just throw whatever crap we have out there and then we’ll hear what customers say and we’ll do whatever they say.” But the issue there is if you show a product to three customers, you get 30 opinions, and now what do you do? So minimum viable product is kind of a synthesis of those two possible extremes.”

  1. “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.” “If you want to do minimum viable product, you have to be prepared to iterate. And so you have to have the courage to say, ‘Yeah, we’ll ship something, get negative feedback and respond.’” Eric Ries.  A minimum feature set is not a goal but a tactic to create cost-effective and speedy validated learning about the hypothesis. The goal is to learn and steer based on feedback rather than try to predict and emerge with a killer fully formed product. Some people like Peter Thiel who is quoted just below, have a different view:

“Even in engineering-driven Silicon Valley, the buzzwords of the moment call for building a ‘lean startup’ that can ‘adapt’ and ‘evolve’ to an ever-changing environment. Would-be entrepreneurs are told that nothing can be known in advance: we’re supposed to listen to what customers say they want, make nothing more than a ‘minimum viable product,’ and iterate our way to success. But leanness is a methodology, not a goal. Making small changes to things that already exist might lead you to a local maximum, but it won’t help you find the global maximum. You could build the best version of an app that lets people order toilet paper from their iPhone. But iteration without a bold plan won’t take you from 0 to 1. A company is the strangest place of all for an indefinite optimist: why should you expect your own business to succeed without a plan to make it happen? Darwinism may be a fine theory in other contexts, but in startups, intelligent design works best.”

Thiel or an entrepreneur like Elon Musk are not as capital constrained as the typical seed stage startup. They can afford to adopt what Ries called a “maximizing the chance of success” approach. Thiel in particular makes many bets and is nicely hedged since he owns a portfolio of wagers. In contrast the founders and early employees of a startup typically have all their eggs on one basket. The founders and early employees are far from hedged. What is right for Thiel may not be right for founders or early employees for that reason.

  1. “An MVP is a process that you repeat over and over again: Identify your riskiest assumption, find the smallest possible experiment to test that assumption, and use the results of the experiment to course correct.” Yevgeniy Brikman. The MVP process is depicted as a flywheel or loop for a reason. Most of the time actual testing of a hypothesis will reveal that customers do not value the product or even the vision the product represents. If the hypothesis is not validated by the experiment the business must iterate by replacing the hypothesis or shut down. I like this description of the process from an interview of Steve Blank by Chris Dixon:

“An MVP is really just a tool for discovering a scalable business model through customer development. An MVP should have the smallest possible feature set that creates gains for customers and reduces pain—but it can’t be so small that customers have nothing to evaluate. In other words, an MVP gives startup entrepreneurs something to demonstrate when they get out of the building and talk to current and potential customers about what they really need.”

  1. “The worst fate of any shipping of any product is that nobody cares. You don’t get any feedback at all. That’s what most features or most products do. They’re just dead weight.” Eric Ries.  What Ries says here is an unfortunate fact. Chamath Palihapitiya describes reality bluntly: “Core product value is really illusive and most products don’t have any.” Faced with the reality of shutting down many companies just push the button and start working on the growth hypothesis without having solved the value hypothesis, starting a process in which they will usually fly the business at high speed into the side of a mountain.
  1. “The common phrase that most people use today is,”You should build a minimum viable product.” And I underlined viable because I think a lot of people skip that part and they go out with a feature and the whole user experience in the very beginning is flat. Minimal viable product pretty much means what is the smallest feature set that you should build to solve the problem that you are trying to solve. I think if you go through the whole story-boarding experience you can kind of figure that out very quickly. But again, you have to be talking to users, you have to be seeing what exists out there already, and what you should be building should solve their immediate needs.” Sam Altman. The graphics which best describe what Altman is talking about depict the MVP as being complete in terms of what it does but not as complete as it will eventually be in implementing the vision once the feedback is obtained from early adopter customers.



  1. “A minimum viable product is not always a smaller/cheaper version of your final product.” “Launching a new enterprise—whether it’s a tech start-up, a small business, or an initiative within a large corporation—has always been a hit-or-miss proposition. According to the decades-old formula, you write a business plan, pitch it to investors, assemble a team, introduce a product, and start selling as hard as you can. All MBA tools are irrelevant on a startup’s day one. This wrong belief is based on that we can start absolutely any company just by spending a lot of time on writing complicated operating plan and financial model and then hire people to execute this plans. But now we know that no plan survives first contact with customers! First days of startup are completely unpredictable. Business plans and financial forecasts are just silly as it was in the Soviet Union.” Steve Blank. A classic example of a MVP is what was done by Zappos Founder Nick Swinmurn: “My Dad told me, you know I think the one you should focus on is the shoe thing. That’s a real business that makes sense. So I said okay, focused on the shoe thing, went to a couple of stores, took some pictures of the shoes, made a website, put them up and told the shoe store, if I sell anything, I’ll come here and pay full price. They said okay, knock yourself out. So I did that, made a couple of sales.” If you can validate your thesis without paying to create lots of code that approach is like found gold. As another example, the MVP for AngelList mostly took the form of making introductions by email. The Virgin Airlines MVP was just a single plane flying back and forth between two cities. The less money spent on proving the hypothesis, the more money that is left to pivot or execute on the idea.
  1. A MVP is not just a product with half of the features chopped out, or a way to get the product out the door a little earlier. And it’s not something you build only once, and then consider the job done.” Yevgeniy Brikman. The MVP should deliver value to the customer even though it is not as complete at is could be. Some people argue that an MVP can be as simple as a landing page, but I am skeptical. Eric Ries writes: “The idea of minimum viable product is useful because you can basically say: our vision is to build a product that solves this core problem for customers and we think that for the people who are early adopters for this kind of solution, they will be the most forgiving. And they will fill in their minds the features that aren’t quite there if we give them the core, tent-pole features that point the direction of where we’re trying to go.”


  1. “MVP is quite annoying, because it imposes extra overhead. We have to manage to learn something from our first product iteration. In a lot of cases, this requires a lot of energy invested in talking to customers or metrics and analytics. Second, the definition’s use of the words maximum and minimum means it is decidedly not formulaic. It requires judgment to figure out, for any given context, what MVP makes sense.” Eric Ries. It does not make much sense to build a MVP unless you do the work to collect data about the experiments and conduct an analysis using modern tools. This data collection and analysis is a lot of work and is not as glamorous to some people as product design, creating marketing plans and attending fancy conferences and parties.
  1. “In the real world not every customer is going to get overly excited about your minimum feature set. Only a special subset of customers will and what gets them breathing heavy is the long-term vision for your product. The reality is that the minimum feature set is 1) a tactic to reduce wasted engineering hours (code left on the floor) and 2) to get the product in the hands of early visionary customers as soon as possible. You’re selling the vision and delivering the minimum feature set to visionaries not everyone.” Steve Blank. Every potential customer does not need to value the MVP for it to be a success. Eric Ries elaborates: “Early adopters can be very forgiving of missing features. They see the vision and you can be in dialogue with them going through that learning feedback loop.” Operating in this process is faith that the customers will help evolve the MVP into something fantastic that will support a very profitable business with a scalable and repeatable business model.


Eric Ries: Minimum Viable Product: a guide

Eric Ries: What is minimum viable product?

Steve Blank: Perfection by Subtraction.

75% of Venture-backed Start-ups Fail

Eric Ries:

Steve Blank

Neil Patel: Developing an MVP: Your Key to Success

LinkedIn Founder Reid Hoffman’s Advice for Entrepreneurs


Minimum Viable Products in Biotech

Chris Dixon interviews Eric Ries:

Sam Altman:


A Minimum Viable Product Is Not a Product, It’s a Process

Eric Ries on 4 Common Misconceptions About Lean Startup

Why has the level of business competition levels been turned up to 11? Or: Why is the lean customer development process important?


The world has been fundamentally changed by digital networks and software. Businesses and customers which are connected by networked digital systems create amplified network effects which means the velocity of business and the level of competition and innovation are higher than they ever been ever been. To survive in this new environment every business, from the largest enterprises to the smallest sole proprietor, must accelerate and fundamentally change their customer development processes. Increasing the ability of a business to adapt to a changing world has never been more important. Virtually every niche in the business world is being constantly explored by challengers using a lean customer development process which I wrote about in my previous post. This constant experimentation by entrepreneurs makes profit harder than ever to sustain, especially if its source was traditionally information asymmetry (i.e., the buyer knew more about something than the customer). Unless a business has a moat based on something like network effects, there is nowhere to hide from the constant onslaught of competition.

Even if a business is fortunate enough to have a moat based on network effects, the life of a business can still be nasty, brutish and short. In other words, since network effects are brittle and work in both directions, a moat can be torn down just as fast or faster than the time it took to create it in the first place. Steven Sinofsky wrote a great Tweet on this point the day before yesterday: “It isn’t enough to build a better mousetrap. Your mousetrap must connect to all the other mousetraps and improve as mice evolve.” Steven is saying that it is not enough to write great software any more or create a great device. Network and network effects matter more than ever.

Once producers and customers are connected via digital networks and telemetry like usage data is being shared it is possible to use the lean start up process to find product/market fit in ways that are more effective, faster and cheaper than ever before. Experiments can be conducted at speeds that were never before possible. Intensifying the process further is that fact that competition can come from anywhere on the globe. I wrote in my post on Naval Ravikant:

“The cost of starting a company has collapsed.” “As the cost of running a startup experiment is coming down, more experiments are being run.”“Three years ago, companies could for the first time get all the way through a prototype of a service before they even raised seed money. Two years ago, they could make it through launch before raising money. Now, they can start to get traction with a user base by the time they come looking for seed money.” A capitalist economy is an evolutionary system.  Innovation and best practices are discovered by the experimentation of entrepreneurs who try to establish the evolutionary fitness of their business. Products and services created as part of this experimentation which have greater fitness survive and other less fit products and services die. Entrepreneurs are essentially running experiments in this evolutionary system when they create or alter a business.

“Success rates are definitely coming down but that is because the cost of running a startup experiment is coming down…so more experiments are being run. In the old days, we would have one company spend $10 million to figure out if it has a market. Today, maybe that same company could do it under $1-2 million. The capital, as a whole, may make the same or better returns, but yeah, if the failures don’t cost a half of what they used to, you are actually saving money, it is a more efficient market.” More experiments inevitably means more failures on an absolute basis. In addition, as the rate of business experimentation rises there will inevitably be an increase in the number of poseurs trying to create new businesses and that will increase failure rates. A lower overall success rate caused by an increase in the number of experiments is a positive trade off overall since society benefits from the increased level of innovation. This net benefit for society is created even though most experiments fail. What the collapse of the cost of running business experiments has done is radically increased the pace of the discovery process that creates innovation.

Any business that does not have connected customers who are sharing telemetry and a modern agile customer development process is bringing a pickle to a gunfight where the competitors have machine guns. Do products get created that do not use the lean process? Sure. That has always been the case. That vast majority fail and a few are a spectacular success creating a distribution that looks like a power law, but that is a topic for another post.

When I worked for Craig McCaw we would meet with various CEOs on a regular basis. It was interesting to see how different styles and approaches impacted business outcomes. One particularly memorable set of meetings we had involved a CEO who represented the third generation of his family to run a major public company which his grandfather started. When we met with him he was nearly always focused on macroeconomic issues like Federal Reserve interest rate policy and forecasts about the economy. Talking about these macro issues seemed to make him feel better. He never seemed to know much about his actual business. Over the years that business has declined to a point where all that is left today is the brand. It is a tragic story that negatively impacted not only him and his family, but tens of thousands of people. Of course, startup founders can fail for essentially the same reason at this CEO when they spend too much time on macro, attending industry conferences and shows and posing for photo shoots.

The CEO I am referring to attended one of the most well-known business schools in the world where he was taught that the systems his company had to deal with could be explained by concepts borrowed from physics like “equilibrium.” This was both unfortunate and fatal since the reality is that a capitalist economy is an evolutionary system and the best metaphor for how it works is biology rather than physics. Charlie Munger agrees: “I find it quite useful to think of a free-market economy – or partly free market economy – as sort of the equivalent of an ecosystem.” Unfortunately for people like this CEO there is no formula that will tell someone like him what to do. People who claim to have such a formulas are never right more than once in a row. The good news is that there are processes which can be followed that will greatly increase the probability of success. One process that killed the huge business was customer development. The pace at which new products were developed at his company was so ponderous and expensive that they were unable to react with sufficient speed when customer demand changed.

Fifty years ago this CEO would not have found himself in so much trouble so quickly. There has always been change in the business world and competition is not a new phenomenon in business. This was true during Georges Doriot’s heyday of the 1950s and 1960s, when he famously said “Someone, somewhere is making a product that will make your product obsolete” competition was significant. The competition Doriot describes is central to what Joseph Schumpeter called “creative destruction.” Schumpeter believed: “The process of industrial mutation—if I may use that biological term— incessantly revolutionizes the economic structure from within, incessantly destroying the old one, incessantly creating a new one.” What is new about business today is that the many systems that make up businesses, markets and an economies are part of globally connected digital networks. When systems get connected via digital networks, feedback effects become stronger. When feedback effects get stronger, outcomes become more uncertain and nonlinear. This name Nassim Taleb gives to this phenomenon is Extremistan. Taleb advises that in such an environment:

“Be prepared for the fact that the next large surprise, technological or historical, will not resemble what you have in mind (big surprises are what some people call ‘unknown unknowns’). In other words, learn to be abstract, and think in second order effects rather than being anecdotal – which I show to be against human nature. And crucially, rare events in Extremistan are more consequential by their very nature: the once-every-hundred-year flood is more damaging than the 10 year one, and less frequent.”

Capitalism has always been an unforgiving system. Capitalism without failure is like religion without hell, it doesn’t work. There is something important and new happening with respect to the level of failure: digital systems that are connected via networks have turned the level of competition and innovation in the business world “up to 11.”

People are not unaware of this competition levels have been turned up to 11 phenomenon, which means they are starting fewer new business. I am not talking about venture capital backed businesses which are a tiny percentage of new business starts each year. In 2016 there we only 800 new businesses that received a series A financing round from a venture capital firms in the United States. What I am talking about is small businesses that are bootstrapped or rely on bank financing:


This competition levels turned up to 11 phenomenon is perhaps most easily explained by another example. Mike Lazaridis was working out at home on his treadmill in 2007 when he first saw an iPhone on a television.  Lazaridis is a co-founder of a business which at that time was selling millions of BlackBerry phones and secure network services to many of the world’s most famous people, including the President of the United States. The phone his business sold was nicknamed the CrackBerry since it was so addictive. The future of the business seemed as secure as its network.  It was not simply possible for Lazaridis to have fully realized the extent to which Apple’s iPhone was about to radically diminish the fortunes of the fabulously successful business he had created. The Globe and Mail newspaper describes what happened:

“That summer, he pried [an iPhone] open to look inside and was shocked. It was like Apple had stuffed a Mac computer into a cellphone. The iPhone broke all the rules. The operating system alone took up 700 megabytes of memory, and the device used two processors. The entire BlackBerry ran on one processor and used 32 MB. Unlike the BlackBerry, the iPhone had a fully Internet-capable browser. That meant it would strain the networks of wireless companies like AT&T, something those carriers hadn’t previously allowed. RIM by contrast used a rudimentary browser that limited data usage. Mr. Lazaridis recalled ‘It’s going to collapse the network.’ And in fact, sometime later it did. “If that thing catches on, we’re competing with a Mac, not a Nokia,” he recalled telling his staff.”

The iPhone, of course, would go on to be an industry and global phenomenon, pummeling the fortunes of BlackBerry and other businesses, reshaping several industries and changing the global economy. BlackBerry was not just competing with “a Mac in a phone” but an entirely new hardware, software and services ecosystem unlike anything the world had ever seen before.

This is a chart of the Blackberry stock price beginning about the time I started using their pager for the first time in 1999.


Another chart tells the story of how quickly the business changed:


What happened to BlackBerry can now happen to any business at any time. NYU Professor Aswath Damodaran points out: “We can no longer assume that competitive advantage will last a century as it used to for the old and mature companies. Instead competitive advantage for tech companies comes with a life span that continues to shorten. What this means is that you’ll climb faster as a business but fall faster too – Blackberry being a classic example.”

You are not employed by or invested in a tech business you say? Every business is now a tech business. There is no escape from Extremistan.  Let’s be clear about the point I am making here: I am saying that my generation rode a bike downhill to school both ways over a very short distance in balmy weather conditions and that young entrepreneurs today walk uphill both ways to school in the snow. Business is more competitive today than it ever has been. Thirty years ago my grandfather was a property developer who went to a club for lunch on most days where he played cards and had a cocktail. My friend’s dad was a stock broker who was playing tennis every day by 3:30 (on the West coast). There’s none of that any more that I can see. I would not want to go back to that time for any reason, but the competitive slack that existed in the system is gone. As another example of increased competition, I saw a woman in the grocery store last night “show rooming” containers of pre-washed lettuce on her phone (she was as an individual shopper comparing supermarket lettuce prices on a hand held supercomputer connected to the internet). That show rooming represents new competitive pressure which impacts the profitability of every product and service, from wealth management to services to retail of all kinds. Show rooming on mobile phones is great for consumers and is not going away! But for producers it adds to the competitive pressure they encounter every day.

Michael Mauboussin describes why the creative destruction process is inevitable: “Companies generating high economic returns will attract competitors willing to take a lesser, albeit still attractive, return which will drive down aggregate industry returns to the opportunity cost of capital.” Charlie Munger has said the same thing as Buffett many times, including this statement: “Over the very long-term, history shows that the chances of any business surviving in a manner agreeable to a company’s owners are slim at best. Capitalism is a pretty brutal place.”  Warren Buffett recently said during an interview at Columbia University: “The first question I ask myself when I look at a business, is it important and easy. And a lot of [businesses] don’t make it. I’m looking for the one-foot bars to step over versus the eight-foot bars to jump over.”

When it comes to moats, durability matters. Some moats atrophy gradually over time and some much more quickly. This is not a completely new phenomenon. As Ernest Hemingway once said in his book The Sun Also Rises, a business can go bankrupt in two ways: gradually and then suddenly. The speed of moat destruction has greatly accelerated over time due to advances in technology and the way it spreads information. For some people this increase in speed can at times be disorienting. For example, the speed at which a company like Blackberry lost its moat was shocking to many investors and employees. This disorientation is having many second and third order effects like heightened political discord. People in many cases are terrified about losing their jobs. Angry, scared and confused people can do unexpected things.

How long a moat lasts in a business is called a “Competitive Advantage Period” (CAP) writes Michael Mauboussin. The speed of moat dissipation will be different in each case and need not be constant.  The rate at which a moat atrophies is similar to what academics call “fade” argues Mauboussin. Even the very best companies can see competition make their moats shrink or even disappear. Munger has said: “Frequently, you’ll look at a business having fabulous results. And the question is, ‘How long can this continue?’ Well, there’s only one way I know to answer that. And that’s to think about why the results are occurring now – and then to figure out what could cause those results to stop occurring.”

That moats are hard to create and inevitably deteriorate over time is one very important reason why capitalism works. What happens over time is so-called “producer surplus” is transferred into “consumer surplus.” What I am saying in this post is that I suspect that the average “competitive advantage period” (CAP) of a business is shrinking. There is some supporting data such as a study which concludes: “over time competitive advantage has become significantly harder to sustain …seen across a broad range of industries.”

To illustrate the points I have made in this post with an example, if a business person opens a successful restaurant that success will inevitably attract imitators and competitors. Some of these restaurants will adapt and survive and thrive and others will fail. Charlie Munger describes the process: “The major success of capitalism is its ability to drench business owners in feedback and allocate talent efficiently. If you have an area with 20 restaurants, and suddenly 18 are out of business, the remaining two are in good, capable hands. Business owners are constantly being reminded of benefits and punishments. That’s psychology explaining economics.” The consumer wins because the products and services offered to them get better and better over time. What happens over time is what economists call “producer surplus” is transformed into “consumer surplus.” Producer surplus is lower since competition has been turned up to 11 and this makes GDP growth look anemic, but competition and innovation are anything but anemic. In Extremistan, producer surplus becomes consumer surplus faster. For a business this is problematic since producer surplus is what delivers the profits that makes the process called capitalism work.


25iq post on Naval Ravikant

A Dozen Lessons About Product/Market Fit

The product/market fit (PMF) concept was developed and named by Andy Rachleff (who is currently the CEO and co-founder of Wealthfront, and is a co-founder of Benchmark Capital). The core of Rachleff’s idea for PMF was based on his analysis of the investing style of the pioneering venture capitalist and Sequoia founder Don Valentine.”

Why market matters more than anything 

  1. “Give me a giant market — always.” “Arthur Rock is the representative of: you find a great entrepreneur and you back him. My position has always been: you find a great market and you build multiple companies in that market.” “Our view has always, preferably, been: give us a technical problem, give us a big market when that technical problem is solved so we can sell lots and lots and lots of stuff. Do I like to do that with terrific people? Sure. Are we unwilling to invest in companies that don’t have them? Sure. We invested in Apple when Steve Jobs was about eighteen or nineteen years old — not only didn’t he go to Harvard Business School, he didn’t go to any school.”  Don Valentine

One way to look at venture capital investing and creating a valuable business is as an effort to build a stool with three legs: people, markets, and innovative products. All three legs are required for success, but different venture capitalists and entrepreneurs emphasize and weight each of the three core elements differently at different times. While Valentine believed that yes, of course you need decent people, “the marketplace comes first, because you can’t change that, but you can change the people” (according to Pitch Johnson, who was a venture capital industry pioneer at the same time Valentine was developing his investing style).

A famous example of changing people was when the Cisco board of directors replaced the then-husband-and-wife team who founded the company. In other cases, new team members are brought in to supply new skills instead of replacing people; Eric Schmidt being recruited to Google is a famous example of that approach.

What is product-market fit, really?

  1. “A value hypothesis is an attempt to articulate the key assumption that underlies why a customer is likely to use your product. Identifying a compelling value hypothesis is what I call finding product/market fit. A value hypothesis identifies the features you need to build, the audience that’s likely to care, and the business model required to entice a customer to buy your product. Companies often go through many iterations before they find product/market fit, if they ever do.” “When a great team meets a lousy market, market wins. When a lousy team meets a great market, market wins. When a great team meets a great market, something special happens.” “If you address a market that really wants your product – if the dogs are eating the dog food — then you can screw up almost everything in the company and you will succeed. Conversely, if you’re really good at execution but the dogs don’t want to eat the dog food, you have no chance of winning.” Andy Rachleff

One way to rephrase a key point Rachleff is making is to say that that nothing is as irreplaceable as a great market. In saying this, no one is saying this means that a great team isn’t an accelerant to what a great market can enable! (The modified Gary Larson cartoon below captures this idea):


Nor is anyone rejecting the idea that PMF is needed. There are other venture capitalists, like Pitch Johnson and Arthur Rock, who put the quality of entrepreneurs first. But it’s a matter of emphasis and timing. Rachleff observes that if you look at the most successful startups, they actually didn’t have “the world’s best management teams in the very early days. They happened to have conceived, or more likely pivoted into, an idea that addresses an amazing point of pain around which consumers where desperate for a solution”.

The process behind product-market fit

  1. “You often stumble into your product/market fit. Serendipity plays a role in finding product/market fit but the process to get to serendipity is incredibly consistent. What we do is teach that incredibly consistent process.” Andy Rachleff

Even though serendipity plays a role here, there is a process — which is why Rachleff later created and teaches a course at Stanford, Aligning Startups with their Markets. Steve Blank also developed a customer development process based on the idea that startups should apply the scientific method just like scientists do: start with a hypothesis, test it, prove it, move on or further iterate on the hypothesis. Similarly, Rachleff observes that “First you need to define and test your value hypothesis. And then only once proven do you move on to your growth hypothesis. The value hypothesis defines the what, the who, and the how. What are you going to build, who is desperate for it, and what is the business model you are going to use to deliver it?” Startups should therefore start with the product and try to find the market, as opposed to starting with the market to find the product. It’s important to emphasize here that the iteration is more about the market and the business model than the product itself.

Finally, as Reid Hoffman notes, “Product/market fit requires you to figure out the earliest tells.” Using an analogy to poker is appropriate since the process finding PMF fit is an art rather than a science. PMF emerges from experiments conducted by the entrepreneurs. Through a series of build-measure-learn iterations, PMF is discovered and developed during a process rather than a single Eureka moment. A-ha moments of inspiration do happen, but PMF is not created that way.

How can you tell whether you do (or don’t) have product-market fit?

  1. “You can always feel when product/market fit isn’t happening. The customers aren’t quite getting value out of the product, word of mouth isn’t spreading, usage isn’t growing that fast, press reviews are kind of ‘blah’, the sales cycle takes too long, and lots of deals never close. And you can always feel product/market fit when it’s happening. The customers are buying the product just as fast as you can make it — or usage is growing just as fast as you can add more servers. Money from customers is piling up in your company checking account. You’re hiring sales and customer support staff as fast as you can. Reporters are calling because they’ve heard about your hot new thing and they want to talk to you about it. You start getting entrepreneur of the year awards from Harvard Business School. Investment bankers are staking out your house. You could eat free for a year at Buck’s.” Marc Andreessen

According to Andreessen, “product/market fit means being in a good market with a product that can satisfy that market.” But too often the focus is on latter part of the sentence (a product that can satisfy the market) and not the former (in a good market). Andreessen emphasizes that market matters most: “You can obviously screw up a great market — and that has been done, and not infrequently — but assuming the team is baseline competent and the product is fundamentally acceptable, a great market will tend to equal success and a poor market will tend to equal failure.” That’s why time spent building a business around the product alone is pointless: “Best case, it’s going to be a zombie. … in a terrible market, you can have the best product in the world and an absolutely killer team, and it doesn’t matter — you’re going to fail. You’ll break your pick for years trying to find customers who don’t exist for your marvelous product, and your wonderful team will eventually get demoralized and quit, and your startup will die.” The converse is also true. You can have an OK team and a buggy and incomplete product but if the market is great and you are the best product available success can happen both suddenly and quickly. That success won’t last unless those products are fixed, but at least the business has the beginnings of something wonderful.

  1. “The term product/market fit describes ‘the moment when a startup finally finds a widespread set of customers that resonate with its product’.” Eric Ries (Lean Startup, p. 219)

The “satisfy the market” part of the Andreessen definition is where the PMF concept necessarily starts to get qualitative. Various math tests have been devised in an attempt to quantify PMF, but they are proxies for something that is fundamentally like Justice Stewart’s famous definition of pornography: “I know it when I see it.” Even if there is a best practices test for whether PMF exists that does not mean that creating PMF can be reduced to a formula.

So what are considered some of the best tests for PMF? Rachleff writes that “You know you have fit if your product grows exponentially with no marketing. That is only possible if you have huge word of mouth. Word of mouth is only possible if you have delighted your customer.” Tying together the concepts, Rachleff also shares that entrepreneurs too often confuse product/market fit with growth in what Ries calls vanity metrics (“numbers or stats that look good on paper, but don’t really mean anything important”). So what does? Rachleff suggests Net Promoter Score (NPS) as a great tool to predict the magnitude of customer love for one’s product/service — ideally a score of 40 or higher “to know you’re on the right track.” However, while NPS is a pretty good proxy for likely fit, it is “not nearly as accurate as having market feedback in the form of purchases.” People vote with their dollars, after all.

  1. “The number one problem I’ve seen for startups, is they don’t actually have product/market fit, when they think they do.” Alex Schultz

Many founders seem to believe that what they have developed is the modern equivalent of magic beans and that people will accept them as payment for a cow. My post last weekend on growth talks about the need for an offering to have core product value. Chamath Palihapitiya believes that a value hypothesis is driven by core product value — “what the market desires about a product” … but that it “is elusive and most products don’t have any.” And in fact, Rachleff has observed that this is where technology inflection points can play a role: “Truly great technology companies are the result of an inflection point in technology that allows the founder to conceive a new kind of product. The question then is: who wants to buy my product?”  Marc Andreessen writes: “In a great market — a market with lots of real potential customers — the market pulls product out of the startup.” Ideally in the easiest stages of a product development process pull is happening organically (i.e., without any advertising spending).

Common misconceptions about product-market fit

  1. “First to market seldom matters. Rather, first to product/market fit is almost always the long-term winner.” “Time after time, the winner is the first company to deliver the food the dogs want to eat.” “Once a company has achieved product market fit, it is extremely difficult to dislodge it, even with a better or less expensive product.” Andy Rachleff

Rachleff has cited examples like Intuit, Apple, and Google as examples of how being the first mover isn’t necessarily the advantage here. Facebook was not the first social network either. Finding product market fit is a process that is not unlike “creating a ‘dance’ between the product and the market” as Mike Maples Jr has described it. It also involves taking the most powerful and compelling aspects of the product and delivering them in the form of ‘WTF’ level features that are not merely compelling — they rise to the level of changing people’s points of view about what’s even possible and create intense delight in customers.” To reach that level, the target isn’t just product-market fit, but “product-market scale,” observes Casey Winters. As I explained in my previous post on growth, Facebook has a superior approach to generating growth and in a business with network effects that not only captures customers and changed points of view, but keeps out competitors.

  1. “Product/Market Fit Myths: Myth #1: Product market fit is always a discrete, big bang event; Myth #2: It’s patently obvious when you have product/market fit; Myth #3: Once you achieve product/market fit, you can’t lose it; and Myth #4: Once you have product/market fit, you don’t have to sweat the competition.” Ben Horowitz

Even though tight product-market fit and product-market scale help beat out the completion, that doesn’t mean that the struggle stops there. Markets and the actions of competitors in that market (which are not always visible to outsiders) are always changing. Constant adaptation is therefore required to retain PMF. Steve Blank observes, “What matters is having forward momentum and a tight fact-based data/metrics feedback loop to help you quickly recognize and reverse any incorrect decisions.”

One mistake many people make is to believe that the process described in feedback loop diagrams does not apply to them. The reason the process is depicted as a circle is that it is both iterative and continuous. It is highly unlikely that even a hundred internal whiteboard product planning sessions will result in a product that has perfect PMF from the start.

  1. “Getting product right means finding product/market fit. It does not mean launching the product. It means getting to the point where the market accepts your product and wants more of it.” Fred Wilson

One of the most common ways that startups die is “premature scaling,” a term first used by Steve Blank. A business is “scaling prematurely” if it is spending significant amounts of money on growth before it has discovered and developed PMF. Steve Blank describes one important reason why premature scaling can happen: “Ironically, one of the greatest risks … is high pressure expectations to make these first pass forecasts that subvert an honest Customer Development process. The temptation is to transform the vision of a large market into a solid corporate revenue forecast — before Customer Development even begins.” A study conducted by Startup Genome concluded:

“Startups need 2-3 times longer to validate their market than most founders expect. This underestimation creates the pressure to scale prematurely… In our dataset we found that 70% of startups scaled prematurely along some dimension. While this number seemed high, this may go a long way towards explaining the 90% failure rate of startups.”

An entrepreneur quoted by the authors of the study said:

“Premature scaling is putting the cart before the proverbial horse…As an entrepreneur there’s always the temptation to grow the sales team at the first sign of revenue traction, but there is always the danger that this early traction is coming from the subset of the market that are early adopters and not the actual market itself. Additionally, too often I’ve seen startups ramp up sales before they’ve figured out the most efficient way to achieve profitability. A vicious cycle ensues wherein the more a company grows, the more it farther away from profitability it becomes.”

Viddy is often cited as an example of a company that died of premature scaling.  For a period of time Viddy was able to use Facebook OpenGraph to grow its user base to millions of users before it ever had PMF. That mistake eventually meant the company was sold for very little and it faded away like the Cheshire Cat. Other business that suffered from premature scaling included Friendster, Orkut, and Digg. Groupon suffered from premature scaling but was able to pivot and save itself so far.

By the way: Not everyone uses this premature scaling terminology. For example, if you look at this list of reasons why startups fail from CB Insights, premature scaling is not even listed but is perhaps buried in other categories:


[As an aside, “Pivot Gone Bad” is a popular Country Western song written by a founder who wrote: “My Co-founder Left with my Husband and I’m sure going to Miss Her.”]

How to get there

  1. “In the early days of a product, don’t focus on making it robust. Find product market fit first, then harden” Jeff Lawson

Again, the process is discovery-based and experimentation is required. There is no value in hardening something that customers don’t want to buy. Andreessen argues that “The product doesn’t need to be great; it just has to basically work. And, the market doesn’t care how good the team is, as long as the team can produce that viable product.” If nearly everyone at the business is focused on trying to fulfill product demand instead of “siting around” trying to dream up new feature to create demand, there is almost certainly PMF — but the reverse is not the case.

PMF is not a magic elixir. It signifies an important milestone that is necessary but not sufficient for success. Once a company has PMF it still must find a sustainable growth model and create a moat against competitors and so on. What PMF does do is help prevent businesses from spending money trying to grow a business (often inorganically) in a way that is doomed to fail.

  1. “In general, hiring before you get product/market fit slows you down, and hiring after you get product market fit speeds you up. Until you get product/market fit, you want to a) live as long as possible and b) iterate as quickly as possible.” Sam Altman

What Altman is saying is reflected in what co-founder Jessica Livingston calls the Y Combinator motto: “make something that people want. If you create something and no one uses it, you’re dead. Nothing else you do is going to matter if people don’t like your product.”

Andreessen argues that the life of any startup can be divided into two parts: before product/market fit (what he calls BPMF) and after product/market fit (APMF):

When you are BPMF, focus obsessively on getting to product/market fit. Do whatever is required to get to product/market fit. Including changing out people, rewriting your product, moving into a different market, telling customers no when you don’t want to, telling customers yes when you don’t want to, raising that fourth round of highly dilutive venture capital — whatever is required. When you get right down to it, you can ignore almost everything else. I’m not suggesting that you do ignore everything else — just that judging from what I’ve seen in successful startups, you can.

  1. “Founders have to choose a market long before they have any idea whether they will reach product/market fit.” Chris Dixon

Some venture capitalists want to see product-market fit before they invest and leave it to angels to do the investing pre-product market fit. They would rather buy a business with product-market fit than try to predict whether a founder will find it. Key as always is for the venture capitalist to let the founders do the heavy lifting (“You do not want a venture capitalist who hire a dog and then tries to do the barking.”). The key point Dixon makes is that founders have control over this by thinking carefully about what they’re trying to do and why. There is also founder-market fit.


  1. Page 219 of The Lean Startup

The Startup Genome Report:

Mixergy Rachleff interview:

CB Insights:

Mike Maples:

Casey Winters:

  1. Casey Winters of Pinterest
  2. Andy Rachleff
  3. Mike Maples:
  4. Marc Andreessen:
  5. Sachin Rekhi
  6. Don Valentine


A Dozen Lessons on Growth

  1. A growth team has the “responsibility to measure, understand and improve the flow of users in and out of the product and business. That’s the role of growth.” “A finance team by definition measures, understands and improves the flow of capital in and out of a business. That’s important because it contributes to all sorts of incredibly important business decisions. Finance uses its knowledge to help the business operate. What’s interesting is every company — and certainly the finance team — eventually realizes that the single biggest lever that it has for maximizing revenue potential is the number of users.” Andy Johns – Vice President of Product at Wealthfront  (formerly Facebook, Twitter, Quora).

Every business must accomplish a range of objectives to be successful:

  1. Measuring financial metrics,
  2. Marketing products,
  3. Optimizing products to enable the business to grow.

This post is about the third objective, which obviously can make a huge positive difference for a business. To illustrate the point about the value of a growth team, as of the end of last quarter, Facebook had 1.23 billion daily active users (DAU) and 1.86 billion monthly active users (MAU). These numbers represent stunning growth. It is easy to forget that not too long ago the total number of Facebook users seemed to have plateaued. Facebook Vice President of Growth Alex Schultz recalls:

“[Facebook grew to] 50 million, and then we hit a brick wall. When we hit that brick wall that was when a lot of existential questions were being asked inside Facebook whether any social network could ever get to more than 100 million users. It sounds stupid now, but at that time, no one had ever achieved it. Everyone had tapped out between 50 and 100 million users, and we were worried that it wasn’t possible. That was the point at which the growth team got set up; Chamath Palihapitiya [Founder of the venture capital firm Social Capital] brought a bunch of us together.”

A small wealth manager needs to grow. Twitter needs to grow. Last weekend I wrote about how everyone poops, well, everyone needs to grow. Especially since everyone has churn and a belly button. Growth can create problems, but they are high quality problems.

Every aspect of a product has the potential to help make the business grow. Or not. The opportunities to create growth by making product choices are nearly endless since it is simply not possible for a product to be technically neutral. For example, you cannot design a neutral automobile, a neutral building or neutral software. Choices must be made in creating and offering a product and those choices can impact growth in either a positive or negative manner.

  1. “The number one problem I’ve seen for startups, is they don’t actually have product market fit, when they think they do.” Alex Schultz

Y Combinator’s Jessica Livingston made a critical point about what drives growth in any business when she said: “Our motto is to make something that people want. If you create something and no one uses it, you’re dead. Nothing else you do is going to matter if people don’t like your product.” Psychological denial can be very powerful. People who want something very badly often just pretend that that have created something that people want to buy when there is no evidence that this is the case since reality is too terrible to contemplate.  For example, a team under pressure from investors that is running out of seed funds can convince itself that it has created a product desired by consumers, even though a child of ten knows the product is crap.

Marc Andreessen has written: “Product/market fit [which] means being in a good market with a product that can satisfy that market.” Until product market fit is discovered by a business, that process should represent the near total focus of everyone at the business. Andy Rachleff elaborates:

“A value hypothesis is an attempt to articulate the key assumption that underlies why a customer is likely to use your product. Identifying a compelling value hypothesis is what I call finding product/market fit. A value hypothesis addresses both the features and business model required to entice a customer to buy your product.”

“A growth hypothesis represents your best thinking about how you can scale the number of customers attracted to your product or service. [What is] the best way to cost-effectively acquire customers? Unfortunately many people mistakenly pursue their growth hypothesis before their value hypothesis.”

Chamath Palihapitiya believes that a value hypothesis is driven by core product value which is:  “what the market desires about a product.” Chamath believes “core product value is elusive and most products don’t have any.”

  1. “[Once] you understand core product value you can create loops that expose that over and over again. You have to work backwards from ‘what is the thing that people are here to do?’ ‘What is the A-ha moment that they want?’ and giving that to them as fast as possible.” Chamath Palihapitiya.

Chamath is saying that in addition to: (1) finding product market fit and (2) identifying core product value, a business must (3) identify A-ha moments (sometimes called magic moments), which are based on positive experiences with the product. The A-ha moments represent an opportunity to build a growth hypothesis. It is useful to think about what Chamath is saying about A-ha moment experiences in the context of a current example. The Snap IPO documents set out what they believe is the core product value: “Snapchat, is a camera application that was created to help people communicate through short videos and images.” As with Facebook, the experience of seeing that your friends are on Snap’s service and being able to chat with them and tell them stories is an A-ha moment. The sooner potential customers get to that A-ha moment the better for the business because every moment that passes before then increases the probability that person will not become a customer. A business which delivers a series of A-ha moments as part of feedback loops will “early and often” expose its customers to core product value, which drives growth.

  1. “Zuck would say ‘You really think that if no one gets a friend, that they’ll be active on Facebook? Are you crazy?’” Alex Schultz.

Facebook famously directed employees to place have near total focus on getting users to have a specific number of friends on the service in a specific number of days as possible given the importance of A-Ha moments. Other Ah-ha moments include getting a like or retweet on Twitter or finding a product you want to buy. People who experience an AH-Ha moments are more likely to become and stay engaged. Richard Price summarizes some industry engagement metrics here:

“Josh Elman, a VC at Greylock, and a former growth lead at Twitter, said that the leading indicator of engagement at Twitter was related to Facebook’s metric: the user following a certain number of people, and a certain percentage of those people following the user back.

Ellior Schmuker has said that the leading indicator of engagement at LinkedIn is also similar to Facebook’s: the user getting to X connections in Y days. He didn’t say what the X and the Y were.

Characteristics of leading indicator metrics

The various leading indicators fit into three categories:

  • Network density: friend or following connections made in a time frame
  • Content added: files added to a Dropbox folder
  • Visit frequency: Day one retention”

Chamath spoke about how his growth team discovered the “7 friends in 10 days” leading indicator. He said that they looked at cohorts of users that became engaged, and cohorts of users that did not become engaged, and the pattern that emerged was that the engaged cohorts had hit at least 7 friends within 10 days of signing up.

  1. “Knowing true core product value allows you to design the experiments necessary so that you can really isolate cause and effect.  As an example, at Facebook, one thing we were able to determine early on was a key link between the number of friends you had in a given time and likelihood to churn. Knowing this allowed us to do a lot to get new users to their A-ha moment quickly.  Obviously, however, this required us to know what the A-ha moment was with a fair amount of certainty in the first place.” Chamath Palihapitiya.

Innovation and best practices are discovered by the experimentation of entrepreneurs who try to establish the evolutionary fitness of their business. Products and services created as part of this experimentation which have greater fitness survive and other less fit products and services die. Entrepreneurs are essentially running experiments in this evolutionary system when they create or alter a business. What is different today is that the tools and systems which exist which allow experiments to be conducted more cheaply and rapidly than ever before. It has never been so possible to know so much about so much. The trick is being able to use these tools to separate signal from noise.

  1. “If you can run more experiments than the next guy, if you can be hungry for growth, if you can fight and die for every extra user and you stay up late at night to get those extra users, to run those experiments, to get the data, and do it over and over and over again, you will grow faster.”  “Startups only have so many opportunities to run an experiment in the product, and they’re also time bound by the cash they have in the bank. With that said you need to run experiments that matter.” “Experiments that count when you are using smaller samples have to be incredibly thoughtful.” Alex Schultz.

Entrepreneurs are engaged in “deductive tinkering” as they search for better products and services. Eric Ries describes the process in this way: “Learning how to build a sustainable business is the outcome of experiments [which follow] a three step process. Build, measure, learn.” To illustrate with an example, the Snap S1 describes how it conducted an experiment via a “build, measure learn” process to enhance its core product value:

“We saw so many people having fun with the Creative Tools we made, like drawing and captions, and we thought people might want to purchase additional ways to express themselves. To test this hypothesis, we built a Lens Store where our users could buy new Lenses, in addition to the free ones we already provided. The results were disappointing. Only a small number of people wanted to buy Lenses, and the number of people using Lenses decreased. After a few weeks, we got rid of the Lens store and made all of the Lenses available for free. Almost immediately, our community began to use Lenses more and create more Snaps to send to their friends and add to their Story.” 

  1. “Basic growth equation: Top of the funnel (A) x Magic Moment (B) = Sustainable Growth (C).”  Andy Johns channeling Chamath.

 Chris McCann of Greylock Partners describes a common mistake made by people seeking growth: “Most growth professionals come into a new company and start working on A) the top of the funnel right away. The problem with this is if you don’t really understand B) and C) then you are fundamentally adding people into a leaky bucket. He believes that the top of the funnel is about “the various mechanisms where you can drive traffic and conversions to your product (SEO, Paid Acquisition, SEM, Social, etc.).” The magic or A-Ha Moment is acompelling experience that creates an initial emotional response that your customers first experience.”

Chamath and people who worked for him at one time or another typically talk about a customer acquisition process that has these elements:


  • What do people want to accomplish (what is core product value)?
  • What is the best way to get people experiencing the service quickly?


  • What is the A-ha moment?
  • How do you get people to this point as fast as possible?


  • How can the business deliver as much core product value as possible to customers?

Only after these three objective are achieved should methods be used to make the service more genuinely viral.

Andy Johns elaborates: “Growth is broken down into a few fundamental questions: (1) How do I increase the rate of acquisition i.e. get more signups? (2) What can I do to activate as many users as quickly as possible in their first ‘N’ days? (3) What are the levers for engagement and retention and how can I pull them? (4) How do I bring churned users back into the system to “resurrect” them from the dead?”


  1. “Anything you can do to move friction out of the flow, do it.” “There’s a really fine line between removing friction and duping users. Tricking users hurts users. Adding friction hurts users.” Alex Schultz

If unnecessary friction impedes people from getting to that A-ha moment the growth team is not doing their job. An important goal of the growth team is to eliminate any unnecessary friction in the customer acquisition process. When in doubt remove steps that a customer must take to get to an A-ha moment.  How can the potential customers be given an A-ha experience in just seconds in ways that are almost frictionless?  LuLu Cheng has written this below about

“How do you evaluate different sign-up flows and decide where to allocate time and resources?

The first step is understanding all of the various channels that are bringing new users to your product. Determine the conversion rate of each and prioritize based on the following 4 points:

It’s easier to build on a strength than to improve a weakness.

Likewise, it’s easier to get an active user to do more than to get an inactive user to do anything. LinkedIn, for instance, sends “Who’s been viewing your profile” emails to active users of the site (20% CTR) rather than inactive members (5% CTR).

Desire – Friction = Conversion. It’s a lot easier to reduce friction than to create desire.

Apply the 10% rule: Assuming you can increase the conversion rate of each channel by 10%, how many incremental users do you get from each flow?

After you find a flow that works, run A/B tests to optimize it. Having an A/B testing framework helps you make informed decisions, and it fosters a culture where data trumps opinions and where rapid iteration is encouraged. Keep in mind, however, that A/B testing will only get you to a local maximum, not a breakthrough change.”

  1. “Think about what the magic moment is for your product, and get people connected to it as fast as possible, because then you can move up where that blue line has asymptotic, and you can go from 60% retention to 70% retention easily if you can connect people with what makes them stick on your site.” Alex Schultz.

Not losing customers is a highly under-rated way to generate growth in a business. Venture capitalist Tom Tunguz describes the importance of retention with an example:

“Churn is a limiting factor on the business. Like fiction, at some scale, churn will prevent the business from growing. To maintain the subscription revenue from the existing customer base requires ever greater mountains of cash. A $20 million ARR business losing 50% of its customers every year will have to replace $10 million worth of customers each year to achieve 0% growth. Assuming 18 month payback, that’s $15M in sales and marketing spend. That means the business will be fundraising constantly.”

  1. “Focusing on short term optimization never works.” Chamath Palihapitiya

If you have not discovered core product value no amount of growth is going to save you. Customers attracted via “hacks” before product market fit exists are going to leave anyway. It should go without saying that it is unwise to try to make a product “viral” without product market fit since what will be communicated virally is that you product sucks, which is like self-administering poison. Alex Schultz believes: “Those users will cease to trust you.” As an example, Twitter tolerating abuse to keep MAU and DAU high was classic short term optimization. It does not work long term anyway due to the negative impact it has on retention. Twitter is finally moving to adopts a longer term attitude about this set of issues.

  1. “Most people when they think about growth they think it’s this convoluted thing where you’re trying to generate these extra normal behaviors in people. That’s not what it’s about. What it’s about is a very simple elegant understanding of product value and consumer behavior.” Chamath Palihapitiya

It is far better to create a process based on a deep understanding of consumer behavior than to relay on some trick or hack since the former is sustainable while that latter is not only transitory but can destroy good will with customers. Good resources to better understand consumer behavior include books on behavioral economics like Influence, Thinking Fast and Slow and Misbehaving. If you are not familiar with concepts like reciprocity and social proof you don’t understand some of the most important drivers of growth.

  1. “Retention is the single most important thing for growth.” “Retention is the number one thing we focus on [at Facebook]. You can’t trick users into doing that.” “Retention comes from having a great idea, and a great product to back up that idea, and a great product/market fit.” “The way we look at, whether a product has great retention or not, is whether or not the users who install it, actually stay on it long-term, when you normalize on a cohort basis, and I think that’s a really good methodology for looking at your product and say ‘okay the first 100, the first 1,000, the first 10,000 people I get on this, will they be retained in the long-run?” “The one thing that’s true, over and over again is, if you look at this curve, ‘percent monthly active’ versus ‘number of days from acquisition’, if you end up with a retention curve that is asymptotic to a line parallel to the X-axis, you have a viable business and you have product market fit for some subset of market. But most of the companies that you see fly up, we’ve talked about packing and virality and all of this stuff, their retention curve slopes down toward the axis, and in the end, intercepts the X-axis.” Alex Schultz.

It is always a challenge to write about a topic like this in less than my target of ~3500 words. I try to include many more resources in the Notes for people who want to dig deeper. Since I have already written a post about the importance of reducing churn and this post is already running a bit long at ~3,300 words I will end with a link to that post, so you don’t churn:


Snap S-1

Tom Tunguz:

Andy Johns:

Andy Johns:

Andy Johns:

Alex Schultz:

Alex Schultz:

Peter Thiel:

Adam Berke

My blog post on Chamath Palihapitiya

Slide deck:

Genius transcript of Chamath Palihapitiya:

Interview of Chamath:

TechCrunch Interview:

StartupGrind Interview:

Chamath Palihapitiya on Quora:

Wired article:

Vanity Fair interview:

Semil Shah Interview of Chamath Palihapitiya:

Every Company Needs a Growth Manager:

Richard Price:

Dave McClure

Chris McCann

LuLu Cheng

Andy Rachleff

Gross Margin for Fun and Profit – Involves Beer and Music Streaming!

The primary challenge with this blog post on gross margins is to make it interesting enough for people to read. So let’s start out with beer. Who doesn’t like beer!  One way to understand more about the cost of making beer is to look at the financials of Ballast Point Brewing on a percentage basis at the time it filed for an initial public offering. By the last quarter before its IPO the “gross profit” (in yellow)  of Ballast Point was 53% (the cost of net revenue was 47%).  I don’t like small print, but I do like these charts that set out the financials of a business on a percentage basis when trying to convey ideas since it is simple for people who may be allergic to accounting to understand.


The gross margin of a business is the percentage of each dollar of net revenue that is available after accounting for cost of net revenue.  If a business has a cost of making products or services of $50M and total net revenue is $100M the gross margin is 50%. The dollar amount is commonly referred to as gross profit.

Businesses come in all varieties and the gross margins generated by various businesses are no exception. Software businesses and pharmaceutical firms have high gross margins. Costco and Exxon have low gross margins. Some firms make up for relatively low gross margins by selling a lot of products and some don’t. Some companies have high operating cost below the gross margin “line” on the income statement  and some don’t. If a business does have low gross margins it does not have a lot of elbow room for operating expenses. Bill Gurley describes a key point out beautifully here:

“There is a huge difference between companies with high gross margins and those with lower gross margins. Using the DCF framework, you cannot generate much cash from a revenue stream that is saddled with large, variable costs. As a result, lower gross margin companies will trade a highly discounted price/revenue multiples. All things being equal, gross margin percentage should have a direct impact on price/revenue multiple, as there will obviously be more gross margin dollars to contribute to free cash flow. Journalists who quickly apply 10x multiples to all private companies should at the very least consider gross margin levels in their analysis.”

Like many things in life, high profit margins can be a double edged sword since it is much easier for a disruptive new business to attack an incumbent that has high margins. Jeff Bezos famously said: “Your margin is my opportunity.” In other words, Bezos sees a competitor’s love of margins and other financial “ratios” as an opportunity for Amazon since the competitor will cling to them while he focuses on absolute dollar free cash flow and slices through them like a hot knife through butter. If you do not have a moat, your margins are at risk.

Let’s return to beer to keep this blog post from getting boring! The craft brewer in the example below is not as profitable as Ballast Point probably due to lower scale and higher levels of competition. But the cost break down is interesting:


Many business face a large and innovative set of competitors and brewing beer is no exception. There are more than 5,000 breweries in the US alone right now.

The quality of life for a business can be much better for a business if gross margins are approach 80-90% as they can be in some software as a service (SaaS) businesses. An attractive SaaS business might have unit economics that look like this:


Three venture capitalists talk about what you want to have in a startup business below:

Mark Suster:

“In the startup world, low gross margin almost always equals death which is why many Internet retailers have failed or are failing (many operated at 35% gross margins). Many software companies have greater than 80% gross margins, which is why they are more valuable than say traditional retailers or consumer product companies. But software companies often take longer to scale top-line revenue than retailers so it takes a while to cover your nut. It’s why some journalists enthusiastically declare, ‘Company X is doing $20 million in revenue’ (when said company might be just selling somebody else’s physical product) and think that is necessarily good while in fact that might be much worse than a company doing $5 million in sales (but who might be selling software and have sales that are extremely profitable).”

Fred Wilson:

“There are providers in the market who are not passing through the true cost, in effect subsidizing the cost of the service, to gain market share. This results in fast growth but negative gross margins. Again, the companies that are doing this are hoping that once they get to scale and users are “locked in”, they can raise prices. The thing that is wrong with this strategy is that taking prices up, or using your volume to drive costs down, in order to get to positive gross margins is a lot harder than most people think. If there are other startups competing with you and offering a similar service, you aren’t going to be able to take prices up without losing customers to a similar competitor, unless your service truly has “lock in.” And most don’t. Using volume to drive costs down can work, but if there are similar services out there, the provider who is being asked to take a cut by you might just move their supply over to another competitor offering a higher price.”

Chamath Palihapitiya:

“Most companies in e-commerce right now are negative-gross-margin businesses. These companies are in the delivery businesses (Postmates, DoorDash, Instacart) and in the food business (SpoonRocket, Munchery). Basically, a lot of these new-generation, remote-control-type businesses—where the phone acts like a remote control to replace an offline experience—are generally, to date, highly, highly, highly unprofitable. There’s a lot of what I call “venture philanthropy” to prop these businesses up. Time will tell whether any of those can become a real business. We have to get back to this world of having pretty reasonable discipline on business models and understanding that many of these gross-margin businesses will never, never break even or become profitable.”

Here is an example from the recent news where a company is buying assets that generate high gross margin that is quite attractive: “We believe the AppDynamics [just acquired by  Cisco] is likely to be accretive to gross margins (77% vs Cisco at 65%) and consistent with the company’s strategy to capture more high margin recurring software revenue.”

Here is another example illustrating the importance of gross margins. In the streaming portion of the music industry the numbers look approximately like this:

  1. Labels get 60% of total revenue
  2. Publishers get 10.5%
  3. 10.5% goes for billing, bandwidth and back end service and support.

Just considering these three items of expense, 80.5% of industry revenue is not available for streaming distributor profit. Some reports put the percentage of revenue going to these categories even higher. Where does the rest of the revenue go given that the streaming distributors are unprofitable?

  1. Personnel costs and general and administrative costs (G&A)
  2. R&D
  3. Customer acquisition costs (CAC)

To illustrate, Mattermark has assembled these unofficial figures regarding Spotify from available reports:

2015 results:

Aggregate Revenue: $2.2 billion.

Revenue via Advertising: $219 million.

Revenue via Subscriptions: $1.95 million.

Royalty Payout Costs: $1.8 billion.

Revenue sans Royalty Costs: $400 million.

Net Loss: $194 million.

As I have discussed many times, Spotify must obtain less costly deal on “wholesale transfer pricing” from the rights holders so as to have a more attractive gross margins. A TechCrunch article linked below describes the current situation well:

“the crux of the matter is that Spotify has been locked into licensing deals that do not give it a strong enough margin. As of September 2016 — the last time Spotify publicly updated its figures — the company has cumulatively paid out $5 billion to music rights holders….But one source tells us that depending on the region and other factors — deals are negotiated case-by-case, covering an artist’s or group’s music but also the number of times a stream is played, and whether it’s a free user listening with ads, or a paying subscriber with no ads — that overall payout can go up as high as 84 percent.“The message to license holders from Spotify is: we can’t really make this work, guys,” our source said. “But, on the other hand, we’ve taken Spotify now to such a size that we need to make it work.”

The best defense of a firm like Spotify to the wholesale transfer pricing power of its suppliers would be to have enough distribution power so that it gets a favorable deal. The battle between Spotify and the rights holders is bound to be intense, so the best thing to do as an observer is buy some popcorn, find a comfortable chair and watch. I have a post coming soon on profit pools that will discuss Spotify’s wholesale transfer pricing situation more fully.

As a way to close this blog post, this list below illustrates how gross margins can differ by business and industry (I find this fascinating, but I am not normal). All the figures below are from Morningstar and vary with time:


Adobe                                                   86%

Pfizer                                                     80%

Oracle                                                    80%

eBay                                                       79%

Bristol-Myers                                        76%

Eli Lilly                                                   75%

Salesforce                                              75%

Comcast                                                 70%

Southwest Airlines                               70%

Johnson & Johnson                               69%

Alibaba                                                   66%

Cisco Systems                                       63%

Intel                                                         63%

Microsoft                                               61%

Google                                                     62%

Coca-Cola                                             61%

Verizon                                                 60%

Anheuser Busch                                 60%

Starbucks                                              60%

Pepsi                                                      56%

AT&T                                                     54%

Boston Beer                                         52%

Disney                                                   46%

The Hershey                                        46%

Nike                                                      45%

AAPL                                                     39%

McDonalds                                           39%

Mondalez                                              39%

GNC                                                        37%

Kohls                                                     36%

Whole Foods                                        35%

Amazon:                                               33%

Netflix                                                  32%

Kraft Heinz                                          31%

GameStop                                            31%

Shake Shack                                         32%

Dollar Tree Stores                                30%

Target                                                    30%

HPE                                                        30%

Exxon                                                     30%

Wal-Mart                                              25%

Walgreens                                             26%

Best Buy                                                 23%

Sears                                                       23%

Tesla                                                       23%

Kroger                                                    22%

Daimler                                                 21%

Toyota Motor                                       20%

Panera Bread                                       20%

HPQ                                                        18%

KB Homes                                             16%

Supervalu                                              15%

Toll Brothers                                        20%

Costco Wholesale                                13%



Ballast Point Brewing:

Huffington Post Craft Beer:

Bill Gurley:

Fred Wilson:

Mark Suster:

Chamath Palihapitiya:



Everyone Poops and has Customer Churn (and a Dozen Notes)


Everyone Poops is the title of the American edition of a Japanese children’s book written and illustrated by Tarō Gomi. This post will explain why every business from Tesla to a hot dog stand has churn, just like everyone poops. I decided to use this analogy because both churn and poop are both inevitable and important parts of an essential process. For example, both individual and business customers all die at some point. This is called death churn and is inevitable, just like taxes. I’m not going to take this poop analogy any further since my family was not amused that I have done so at all when I told them of my intentions. They were more pleased with my “everyone has customer acquisition cost and a belly button” idea. I will try to keep this post short, snappy and relatively math free so you don’t get bored.

A churn rate measures of how much of something is lost over a given period. Retention is the inverse of churn. One important type of churn calculation determines how many customers are retained by a business. The simplest explanation of customer churn is visual:


The financial impact of “lost customers” as depicted in the illustration (churn) is too often underestimated by a business.

I’m not going to dig too deeply into the math of churn in this post or the many versions of that math since most of you will stop reading. Let’s just say the math associated with churn can be very complex (or not) and some data scientists (i.e., statisticians who work in a city like San Francisco or Seattle) can spend their entire careers measuring it. Product development, marketing and other people can spend their entire careers trying to improve churn.

Despite my desire to avoid math, I am going to repeat a short explanation of the math of churn from a post by Lighter Capital:

“Customer Churn Rate = Number of preexisting customers who left during a given period / Total customers at the start of that period. For example, assume your company has 50 customers at the beginning of the month. During that month, 12 customers left. That would mean you had a monthly customer churn rate of 24% (12/50 = 0.24). Mathematically, this means churn is the inverse of customer retention.”

The impact of churn, once calculated, is best conveyed graphically:


When it comes to the financial impact of churn, even a fraction of a percentage point change can make a huge difference in an outcome. This is why churn is often tracked in terms of basis points (a hundredth of a percent). A business that ignores even small changes in churn often ends up dead. As with any metric, it is better to be roughly right in calculating churn than to be precisely wrong. It is also important to understand that there are sneaky ways to hide the adverse effects of churn and so you need to be careful with any claims made by promoters based on churn calculations. It is always wise to examine the assumptions that promoters use in a churn calculation since it is possible to tell a very tall tale by misrepresenting the impact of churn. Ben Horowitz writes about hearing this sort of talk from people who want to raise money from his venture capital firm:

“’We have a very high churn rate, but as soon as we turn on email marketing to our user base, people will come back’ – Yes, of course. The reason that people leave our service and don’t come back is that we have not been sending them enough spam. That makes total sense to me, too.”

Going back to the bucket analogy for the impact of churn, if you don’t know how much water is flowing out of the bucket via leaks you have a very incomplete picture of the financial health of a business. Sometimes a business does not reveal its customer churn. For example, I suspect some of the food preparation and delivery startup businesses have very high churn but are not revealing their customer losses. Groupon had punishing churn levels in its early glamorous but highly unprofitable days. The churn rate for some mobile gaming firms and mobile apps is perhaps best measured in hours rather than days.

The higher the customer acquisition cost (CAC) of a given customer the more important lower churn becomes in the unit economics calculation. For example, if you pay $700 in CAC to acquire a satellite TV customer you can’t afford much churn and have positive return on investment. If you pay only $30 to acquire a prepaid cellular customer, churn can be relatively high and yet the business still can make financial sense depending on the other variables that determine unit economics. Here is a short description of why churn matters from a post by Joel York:

“In plain English, you spend an awful lot of money, time and energy acquiring customers. You recover this investment over time, so you want your customers to stick around as long as possible. The longer they stay, the stronger your business. This is why the value of one divided by the churn rate is often quoted as the average customer lifetime; lower churn equals longer customer lifetimes equals larger customer lifetime value.”

Often the math of the unit economics produces a result that makes clear that the benefits of retaining a customers are nonlinear. In short, investments in churn reduction (retention) can yield a far better return on investment than generating new users on a relative basis. There are some research studies that claim that increasing customer retention rates by 5% can increase profits by 25% to 95%.

What is an acceptable churn rate varies by business model and industry. In some cases in some industries it can be wise to require that a customer commit to a contract with a term that is relatively long so as to reduce the risk of churn. The trade off that exists is that services which are not terminable on a monthly basis have higher customer acquisition cost. For example, there is a reason why a service provider does not ask you to commit to a service for five years (answer: it is too expensive in terms of CAC to get you to do so). You must also keep in mind that if the customer is not creditworthy a contract is not worth much anyway. Customer quality matters a lot in thinking about churn. So-called “freemium” services are particularly prone to churn. For example, mobile apps can experience churn that is just short of stunning as depicted in this graph:


No one has ever captured my view of the reflexive nature of unit economics model better than Bill Gurley did in one of his Above the Crowd blog posts:

“Tren Griffin, a close friend that has worked for both Craig McCaw and Bill Gates refers to the five variables of the LTV formula as the five horsemen. What he envisions is that a rope connects them all, and they are all facing different directions. When one horse pulls one way, it makes it more difficult for the other horse to go his direction. Tren’s view is that the variables of the LTV formula are interdependent not independent, and are an overly simplified abstraction of reality. If you try to raise ARPU (price) you will naturally increase churn. If you try to grow faster by spending more on marketing, your SAC will rise (assuming a finite amount of opportunities to buy customers, which is true). Churn may rise also, as a more aggressive program will likely capture customers of a lower quality. As another example, if you beef up customer service to improve churn, you directly impact future costs, and therefore deteriorate the potential cash flow contribution. Ironically, many company presentations show all metrics improving as you head into the future. This is unlikely to play out in reality.”

By watching how a specific “unit economics” model changes as the inputs change you can actually get an analog “feel” for the best ways to create an optimal finance return. Make no mistake: getting the balance right is hard and is an art as much as is it a science, since human emotions and complex adaptive systems are involved. Once you understand the sensitivities of the business/unit economics model to changes in inputs you are better able to answer questions like these if you have a feel for the process:

  1. Should you deliver more value to customer even if COGS rise?
  2. Should you increase resources given to the customer retention team?
  3. Should you focus on acquiring higher quality customers?

In a blog post linked below Sixteen Ventures set out a nice example. If a business wants to have an annual churn rate of 7%, they must keep monthly churn to ~.5 percent, which means only 1 in 200 customers can leave every month. That is not a small challenge in many businesses given the level of competition. Many consumer businesses have 5% customer churn each month. A 5% monthly churn results in a 46% annual churn rate, which means that the business must work hard and spend significant amount of money to replace those customers. Customer replacement requires new CAC, which is expensive.  There is nothing bad about acquiring new customers if the unit economics are positive, but too often businesses forget to invest enough in customer retention given the higher return on invested capital from those investments.

If you don’t know the churn of a public business you are considering investing in and they won’t reveal it, the best an investor can do in understanding the unit economics of that business is to look at comparable situations and build in a margin of safety to an estimate. As an example, Netflix no longer reveals churn, but when it did so it looked like this:


You can then take a look at net growth of revenue and customers and roughly calculate a churn rate.

Understanding churn has always been important for a business, but it is even more so today because the challenges associated with attracting and retaining customers have greatly increased. Jeff Bezos understands this new environment well:

“The balance of power is shifting toward consumers and away from companies…the individual is empowered. The right way to respond to this if you are a company is to put the vast majority of your energy, attention and dollars into building a great product or service and put a smaller amount into shouting about it, marketing it. If I build a great product or service, my customers will tell each other. In the old world, you devoted 30% of your time to building a great service and 70% of your time to shouting about it. In the new world, that inverts.”  

Almost every customer is “showrooming” (comparing provider prices and quality) using the tools that the Internet and a range of modern hardware devices have made ubiquitously available. The days where a business could take advantage of a wide information asymmetry to earn a higher profit on the sale of a good or service are either rapidly disappearing or are gone. The showrooming phenomenon has resulted in lower gross profit margins and increasing focus on customer retention that often takes the form of a subscription business model and a membership mentality. The theory behind this new approach is simple: by delighting customers on a more regular basis, treating the customers as if they are members of a club and tracking their engagement via a range of metrics, the risk of churn can be reduced. Netflix is an example of a business that has a membership mentality as if Amazon Prime. Another less obvious example is Costco which makes the bulk of its profit on membership fees. The minimally marked up merchandise in Costco stores (~14%), the cheap hot dogs and the free samples are all about delighting customers enough that they renew their membership.

Keeping a customer in today’s hyper-competitive business world is often more profitable than paying to acquire a new customer. This increased focus on customer retention in no small part explains why so many business are shifting to a “lifetime value” approach to valuing customers. Having to re-acquire customers again and again for every transaction is not an ideal way to run a business when customer acquisition costs (CAC) are as high as they are today and customer switching costs are so low. Businesses are as a result focused on delivering nearly constant value to their customers and keeping them engaged and happy. Eric Ries describes the opportunity here:

“…every action you take in service development, in marketing, every conversation you have, everything you do – is an experiment.  If you can conceptualize your work not as building features, not as launching campaigns, but as running experiments, you can get radically more done with less effort. Process diagrams [in major corporations] are linear, boxed diagrams that go one way. But entrepreneurship is fundamentally iterative”

Descriptions of a service development process must increasingly look like flywheels. Failure is an essential part of the process which is a feedback loop. The model is: “build, measure, learn” [repeat]. This is the scientific method at work, but systematized with telemetry allowing timely and accurate measurements like never before and cloud computing radically lowering the cost of the experiments.

Customer churn can be separated into sub-types such as: (1) voluntary churn where customers switch to another provider or terminate their use of the product and (2) involuntary churn where a product is no longer provided to the customers due to missed payments, bad debts, etc.  There are also plenty of other permutations like gross churn, net churn and cohort churn if you want to go down those roads. There is also revenue churn in addition to user churn for example. Time periods used in the calculation can also vary. Churn can be calculated on a monthly quarterly or annual basis. One post is even entitled: “43 ways to calculate SaaS churn” since there are so many methods for doing the math. There are way more tan 43 but you get the idea. When it comes to the math of churn the Richard Feynman line comes to mind: “The first principle is that you must not fool yourself, and you are the easiest person to fool.”

As an aside, one thing you may encounter is the term: “negative churn.” What drove the creation of this term was the need for a metric that describes increasing revenue from the customers that remain and the term “negative churn” was invented to meet that need. A business has achieved net negative churn: “When, for a given time period, expansion revenue more than offsets any revenue you lose from customer churn, downgrades, lower usage, etc.”  I am not fond of the term, but it is out there. It sounds more like up-selling and cross-selling to me. But that is a topic for another post.

In the end, churn is about useful life: how long will the customer relationship last? Here’s a chart of the useful life of mobile customers in about 2009


If churn is defined and tracked properly it can be a powerful motivating and unifying force for a business. A simple metric can result in everyone in effect “rowing together toward a common goal.” Unfortunately, too often “key performance indicators” (KPIs) roughly related to churn are created that vaguely measure engagement. The result is often a hand wavy distraction for the business that is mostly useful for the employee at performance review time. A customer not paying any longer, is a customer not paying any longer. That is what matters.

Other issues must be considered in dealing with churn such as: “for virtually all businesses, new customers will have a higher churn rate than mature customers. But what this means is that some form of segmentation is necessary to have a useful churn rate. For example you may want to only report the churn rate for customers who have been around for at least 90 days. Or you may want separate churn rates for all sorts of demographics and tenure.” This is where the data scientists can really earn their keep.  A well-run business with great information about its customers and systems can do things like refer higher value customers to a different level of customer service. A business that knows the customer well can tailor the customer service response to net present value of the stream of income from the specific customer. Having granular data about customers and their lifetime value has never been more important. For example, in the early days of the mobile business we used to know that X dropped calls over a period of Y days, meant risk of churn rose by Z and we would often intervene with a bill credit to stop churn proactively. There are many similar techniques businesses can use to manage and reduce churn. The churn act itself is often the result of a long process that the consultancy Bain argues looks like this:


It is true that frustration can build up over time resulting in an eventual churn event of some kind. A situation that results in churn is often a process that is long enough in duration to allow pro-active intervention by a business before the churn event happens. Great analytical systems can go a long way to reducing churn by identifying triggers and ways to reduce customer frustration. A Harvard Business School case linked in the Notes to this post argues:

“By the time you see an increase in your churn rate it is six or eight months after the point in time when you actually failed the customer. If churn is your only measure of customer happiness, then you’re always six months too late to influence your future.” HubSpot and many other firms have developed analytics and accompanying metrics to predict who is going to leave. “The most innovative firms are using churn rate analysis as an opportunity to get ahead of losing customers rather than just accept it.”

To close this post (since this post is running long at ~3,000 words), I will point you to work done by Pacific Crest, which has a well-known survey in which they track churn in the software as a service (SaaS) business, and one of their charts look like this:


There are many more useful charts in the links in the Notes. If you want to dig into churn mathematics and methodology there many other resources available.

A Dozen Notes:

  1. 2026 Pacific Crest Survey:
  1. Joel York- What is Churn:
  1. Lighter Capital:
  1. TechCrunch:
  1. Bain
  1. Shopify:
  1. Sixteen Ventures:
  1. Bill Gurley:
  1. Consultants! Booz:  PWC:
  1. HBS! HBS: HBS
  1. Medium:

12. Horowitz:

Why is it so Hard to Forecast the Future?


For most of human history the life experiences of people have been overwhelmingly linear. Human are accustomed to encountering situations that reflect a simple proportional relationship between cause and effect. People expect that when they do X that Y will happen if Y was what happened in the past. This type of linear relationship is comforting to people since it is familiar. Ray Kurzweil believes: “our intuition about the future is linear because that is the way the world worked for most of history. Prey animals did not get exponentially faster for example.” Except for a virus or bacterial infection multiplying inside your body, few things in ordinary life are nonlinear.

The rise of modern science combined with modern distribution and other processes developed by businesses has resulted in people increasingly encountering nonlinear change. The economist Paul Romer explains one common reaction: “People are reasonably good at estimating how things add up, but for compounding, which involves repeated multiplication, we fail to appreciate how quickly things grow. As a result, we often lose sight of how important even small changes in the average rate of growth can be.” When something is sufficiently nonlinear, a phenomenon can seem almost magical. Especially when the outcome of a nonlinear change is negative, tendencies like loss aversion can kick in and people can have a strong tendency to react in highly emotional ways. Even people who are otherwise rational may not think clearly in this situation. Nobel Prize winner Daniel Kahneman describes one important reaction: “People talk of the new economy and of reinventing themselves in the workplace, and in that sense most of us are less secure.” People who feel less secure can feel confused and even angry. In one of his famous memos sent to readers just this week Howard Marks frames the current challenge:

“I realized recently that in my early decades in the investment business, change came so slowly that people tended to think of the environment as a fixed context in which cycles played out regularly and dependably.  But starting about twenty years ago – keyed primarily by the acceleration in technological innovation – things began to change so rapidly that the fixed-backdrop view may no longer be applicable.  Now forces like technological developments, disruption, demographic change, and political instability and media trends give rise to an ever-changing environment, as well as to cycles that no longer necessarily resemble those of the past.  That makes the job of those who dare to predict the macro more challenging than ever.”

We all must make decisions including about events that will happen in a future that is risky, uncertain and may involve unknown unknowns. What’s the right response to this reality? Howard Marks describes an approach that works for him: “We can’t predict, but we can prepare…. the key to dealing with the future lies in knowing where you are, even if you can’t know precisely where you’re going.  Knowing where you are in a cycle and what that implies for the future is very different from predicting the timing, extent and shape of the next cyclical move.” When change is both inevitable and gaining speed a person’s ability to adapt to the environment based on what he sees in the present is far more useful than trying predict the future. Steering as you react to signals being generated in the present is very different from trying to predict events that may happen in the future. The changes we are seeing in society and business effectively mean that evolution, as broadly defined, has accelerated. Evolution favors individuals who can adapt the fastest and most effectively to that accelerated change.

One way to be smarter about the ways in which we adapt is to change the way we think. One of many important ideas that have resulted from the work of Kahneman and Amos Tversky is the “distinction between two profoundly different approaches to forecasting, (1) the inside view and (2) the outside view. In his book Thinking, Fast and Slow Kahneman describes a view he and Tversky followed when they tried to predict when they would finish a project:

“The inside view is the one that all of us, including Seymour, spontaneously adopted to assess the future of our project. We focused on our specific circumstances and searched for evidence in our own experiences. We had a sketchy plan: we knew how many chapters we were going to write, and we had an idea of how long it had taken us to write the two that we had already done. The more cautious among us probably added a few months as a margin of error. But extrapolating allow for what Donald Rumsfeld famously called “unknown unknowns.” At the time, there was no way for us to foresee the succession of events that would cause the project to drag on for so long: divorces, illnesses, crises of coordination with bureaucracies. These unanticipated events not only slow the writing process, but produce long periods during which little or no progress is made at all. Of course, the same must have been true for the other teams that Seymour knew about. Like us, the members of those teams did not know the odds they were facing. There are many ways for any plan to fail, and although most of them are too improbable to be anticipated, the likelihood that something will go wrong in a big project is high.”

In his book Think Twice, Michael Mauboussin describes alternative to the inside view: “The outside view asks if there are similar situations that can provide a statistical basis for making a decision. Rather than seeing a problem as unique, the outside view wants to know if others have faced comparable problems and, if so, what happened. The outside view is an unnatural way to think, precisely because it forces people to set aside all the cherished information they have gathered.”

In addition to adopting a better viewpoint so to increase the probability of making a successful forecast, a person can also try to avoid making predictions that by their nature are particularly hard to make successfully. Howard Marks describes the objective best: “The more we concentrate on smaller-picture things, the more it’s possible to gain a knowledge advantage. With hard work and skill, we can consistently know more than the next person about individual companies and securities, but that’s much less likely with regard to markets and economies. Thus, I suggest people try to ‘know the knowable.’” Charlie Munger has a similar view: “Micro-economics is what we do and macro-economics is what we put up with.” Warren Buffett describes the objective of people like Marks and Munger in this way: “I don’t look to jump over 7-foot bars: I look around for 1-foot bars that I can step over.” Munger makes a similar point by joking that he wants to know where he will die, so he can just not go there.

What categories of phenomenon are less knowable? Grace Hopper famously said: “Life was simple before World War II. After that, we had systems.” When systems are involved it can get especially hard to make forecasts. What is a system? Nobel Prize winner Murray Gell-Mann has said that a scientist would rather use someone else’s toothbrush than another scientist’s definitions. Nevertheless, one dictionary definition a “system” is: a regularly interacting or interdependent group of items forming a unified whole. There are many types of systems, but the system that creates the greatest challenges for people in today’s world is a specific type known as a complex system. A complex system is a system composed of many interacting independent agents or elements that can lead to outcomes that are either difficult or impossible to predict by looking at the components. Capital markets, ecosystems, ant colonies and the human immune system are all example of complex systems. Michael Mauboussin describes a new reality: “Increasingly, professionals are forced to confront decisions related to complex systems, which are by their very nature nonlinear. Complex adaptive systems effectively obscure cause and effect.  You can’t make predictions in any but the broadest and vaguest terms.  Complexity doesn’t lend itself to tidy mathematics in the way that some traditional, linear financial models do.” Nassim Taleb identifies key ideas about complex adaptive systems in this way:

“The interactions matter more than the nature of the units. Studying individual ants will never (one can safely say never for most such situations), never give us an idea on how the ant colony operates. For that, one needs to understand an ant colony as an ant colony, no less, no more, not a collection of ants. This is called an “emergent” property of the whole, by which parts and whole differ because what matters is the interactions between such parts. And interactions can obey very simple rules.”

A small system like a business is not easy to make predictions about, but on a relative basis these predictions bout small systems are easier than making other more macro predictions. In other words, understanding enough about a single business like a hot dog stand, a grocery or Ford in order to make predictions with some reasonable degree of accuracy is far more possible to do than making predictions about the future state of an economy on a given date. Even with a focus individual businesses it is still hard to value a business. If it was easy to do so or if there was a simple formula to follow, everyone would be rich. Munger says anyone who thinks investing is easy is stupid.  Howard Marks writes: “investing can’t be reduced to an algorithm and turned over to a computer. Even the best investors don’t get it right every time. No rule always works. The environment isn’t controllable, and circumstances rarely repeat exactly.” Since the future is uncertain you must think probabilistically.

Until the Internet made facts as transparent and retrievable as they are today many people took comfort that experts from branded institutions understood what was going on and could reliably predict the future. The situation today is that people are able to easily “showroom” the predictive performance of experts against what actually happened and they do not like what they see. Sometimes you will hear someone say: “people have lost faith in our institutions.” That may be true, but no small part of what they may actually be saying is that they have lost faith in experts who claim to be able to predict the future. As people recognize that “experts” can’t predict the future any better than chance so they are doing things like flocking to index funds. Only when experts stop predicting the unpredictable will their credibility return. The expert may forget about their prediction failure due to hindsight bias, but the public can now easily fact check the record of the pundit. This reality will not change. Ever.

The number of and magnitude of systems that impact our lives is increasing as digitization of the economy proliferates and systems are to a far greater degree interconnected by networks. Mauboussin writes in Think Twice: “Unintended system-level consequences arise from even the best-intentioned individual-level actions has long been recognized. But the decision-making challenge remains for a couple of reasons. First, our modern world has more interconnected systems than before. So we encounter these systems with greater frequency and, most likely, with greater consequence. Second, we still attempt to cure problems in complex systems with a naïve understanding of cause and effect.” A networked and always connected world is an environment where people encounter complex adaptive systems in many more situations than in the past. Andrew Haldane, Chief Economist, Bank of England believes: “These systems are even more unpredictable because they nest within each other creating even more turbulence. Modern economic and financial systems are not classic complex, adaptive networks.  Rather, they are perhaps better characterised as a complex, adaptive ‘system of systems.’  In other words, global economic and financial systems comprise a nested set of sub-systems, each one themselves a complex web.”

Whether we like it or not, the economy is currently changing in ways that are increasingly nonlinear. Feedback of all kinds is being amplified in new ways and that increases the magnitude of nonlinearity. That crazy event you watch on a video is like the screeching of a microphone at a concert feeding back on itself. When nonlinear change happens the aggregate behavior of systems is vastly more complicated than would have been predicted by summing the inputs into the system. With a nonlinear system when we do X sometimes instead of Y happening Z can happen which we did not expect at all or had never even conceived of as a possible outcome. For example, in a nonlinear world you can train for profession X that has been around for many years and it can disappear over a very short period of time.

Volatility in the prosperity of businesses and professions produced by exponential change can be seen virtually everywhere. For example, many giant corporations are losing the competitive advantages that come with greater economies of scale and are under assault by smaller more nimble competitors. Smaller nimble businesses spend as much time competing with each other as attacking more established businesses, creating unprecedented levels of competition. Jobs disappear in one profession and are yet are being created in new industries, if you have the right skills. People are comparing prices of virtually everything using their mobile phones, which means profits that once were made possible by taking advantage of information asymmetry between producers and consumers are disappearing. The CEO of Costco told the CEO of American Express that the credit cards they provide are no different than ketchup. Shopping malls are being decommissioned as e-commerce rises. New markets are proliferating, value chains are breaking up, and profit pools are shifting. Industry boundaries are blurring and barriers to entry are disappearing. Sources of competitive advantage are fundamentally changing at unprecedented speed.

Cars and trucks driving themselves is a nonlinear change, especially if your job is to drive for a living. Stores that do not need checkers is a nonlinear change, especially if you do that or a living. People being able to get news for free instead of buying a newspapers is a nonlinear change, especially if you are a reporter. If you make your living selling any of the devices that a modern smartphone replaces that is nonlinear change.  As was noted above, since we are not accustomed to nonlinear change, when it happens it can be confusing. When humans get confused they start telling stories to make the world make sense again. These stories may or may not have any tie to reality, but they make us feel better. Daniel Kahneman has said: “What we have is a storytelling system and the coherence of the story determines how much faith we have in it.”

I have written a book on Native American legends entitled Ah Mo that captures how people in the Northwest part of what is now the United States used stories to make sense of the world before Columbus arrived in the Americas. These stories explained simple things like where fire came from and why salmon appear in the streams in the fall to spawn. A different sort of storytelling about complex systems is happening today, most notably as politicians or promoters try to use stories to explain things like why someone’s job has disappeared. As these stories proliferate being able to tell the difference between the truth, what we don’t know and what we can’t know, will be increasingly important.

On the subject of forecasting, in addition to reading what Howard Marks has written on the subject, I suggest that you read Philip Tetlock and Mauboussin, who writes in this cautionary note:

“The predictions of the average expert were ‘little better than guessing,’ which is a polite way to say that ‘they were roughly as accurate as a dart-throwing chimpanzee.’ When confronted with the evidence of their futility, the experts did what the rest of us do: they put up their psychological defense shields. They noted that they almost called it right, or that their prediction carried so much weight that it affected the outcome, or that they were correct about the prediction but simply off on timing.”


Think for yourself.



Howard Marks:

Thinking, Fast and Slow

Think Twice:



Ah Mo: