Innovator: Sonia Rykiel

I unexpectedly stumbled upon fashion designer Sonia Rykiel's obituary as I was reading this weekend's FT. I'd never heard of her and intended to skim it, but a number of things caught my eye, making me read more closely:

  • "Her lack of formal training was, in a perversely strong sense, Rykiel’s liberation."
  • "Rykiel loved to dance and have fun. On the catwalk she wanted her models not to stride and scowl but instead amble up and down in laughing, chattering clusters."

Intrigued, I searched for more about her and found this W Magazine piece from early 2015:

  • "In 1955, pregnant with her daughter, Nathalie, Rykiel devised a dress that flouted the convention of the time, when pregnant women camouflaged their bulging bellies."
  • "A writer herself, Rykiel has published more than a dozen works, including a novel, a children’s book, an abecedarian self-portrait in which 400 of her favorite words serve as points of departure for observations about fashion and life, and an epistolary exchange with her friend Régine Deforges, an editor, publisher, and outspoken author known for her erotic literature."

No formal training and therefore an ability to create unhindered by old paradigms, designing for herself, carving her own path and flouting conventions, and pursuing a broad range of interests...I love seeing that innovators in pretty much any field are actually quite similar.

 

Software Programs the World

I got a lot out of this podcast! I initially listened to it driving, but the insights were so interesting that I had to listen to it twice afterwards, taking notes to absorb it all. It's an exciting brave new world we have coming.

The most interesting parts:

  • Foundations (jump to clip)
    • Foundational element one: Moore’s Law has "flipped" over last 7-8 year
      • Traditional Moore's Law: a new chip was release every 1.5 yrs that was 2x as fast at same price (this lasted for about 40-50 years)
      • This is the dynamic that drove mainframes, minicomputers, PCs, and smartphones
      • About 7-10 years ago, this ended; chips "topped out" at about 3 GHz
      • Now the dynamic has "flipped" so that every 1.5 years, chips are just as fast but half the price
      • This is a "massive deflationary force ... where computing is becoming essentially free"
      • In this business, we “chart out the graphs and assume we get to the end state," one where chips will be essentially free
      • Chips will be embedded in everything (this is a new world)
    • Foundational element two: all of those chips will be on the network
      • Wifi, mobile, wired, etc.
    • Foundational element three (continuation of "Software Is Eating The World"): software, then, will allow you to program the world
      • Cars, things in the sky, buildings, homes, businesses, factories, etc.
      • "This is just starting"
      • Entrepreneurs are more interesting, more aggressive than ever before because they assume "if there is something to be done in the world, software can be written to do it"
      • Consequences: "...investing in markets that 7-8 yrs ago we would have never anticipated operating"
  • New platforms (jump to clip)
    • Platforms will be different than what we've had until 5-10 years ago: the platform was a new chip (faster) and a new OS
    • Platforms today: distributed systems, scale out systems
    • These are not on a chip, rather built across a lot of chips (distributed systems)
    • Cloud was first example (AWS) - can now create a program that can run across 20k computers (run for 1h, cost $50)
    • Rise of Hadoop, Spark (distributed processing)
    • Financial technology: bitcoin, cryptocurrency
    • Now: AI (machine learning, deep learning) which is "inherently parallelizable" - can run across many chips and get very powerful as they do so
    • "Can do things in AI with distributed computing that you couldn’t imagine 5y ago"
  • The GPU
    • Initially developed for gaming for very high resolution graphical processing → unexpected uses
    • "New application of an old idea"
    • Thirty years ag in physics lab -- if you need a simulatio with large number of parallel calculations (e.g., black holes, biological simulations), write algorithms to parcel problem into pieces and run in parallel
    • In this days days: vector processors ("sidecar computers")
    • 30y later: GPU is basically a vector processor (sits alongside CPU)
    • Ben Horowitz at Silicon Graphics → physics applications, flight simulations, computational fluid dynamics
    • Simulating real world → need same capability (exact same processor)
    • HW platform emerging: NVIDIA
    • NVIDIA has become “seemingly overnight” → market leader in GPUs and chips for AI
    • All entrepreneurs in AI building on NVIDIA chips (in contrast to Intel in previous years)
  • In AI, what are the things that lend themselves well to startups versus larger companies (e.g., FB, Google, Apple)
    • Challenge: people think of AI as narrower than it really is; rather, it is an entirely new way to write a computer program (broadly applicable to problems)
    • Could use AI to analyze consumer data (hard to compete with Google)
    • BUT: many areas where no one has any data yet (HC, autonomy)
    • Big company advantage: lots of data
    • In reality: just a new way to write a program
  • Interfaces
    • The smartphone was an advance over WIMP
      • WIMP interface: windows, icons, menus, printer
    • That was an advance over text based interface of DOS
    • BUT -- life is different
    • Natural interfaces: natural language
    • AI can enable natural language and natural gestures
    • Opportunity to build interfaces for things you couldn’t before
    • One idea: what applications couldn’t you have before because there wasn’t a workable use interface for it
    • Amazon/Alexa
    • Not tied to old generations
    • No “strategy tax”
  • Ability to leapfrog 
    • For many major new advances, interfaces depend on platform
    • But - big companies also have strategy tax -- existing agenda, have to fit next thing into old platform
    • Example: Amazon has taken lead from Apple, Google -- even though it flopped with phone!
    • Lack of phone became an advantage! Clean breakthrough product
  • But where can startups play?
    • A year ago, would have said AI would be domain of big companies (can afford engineers, hardware; data sets)
    • All three have changed
    • AI technology is standardizing (open source → cloud)
    • AI as a service
    • “AWS for AI” (Google, Amazon, Microsoft, etc.)
  • TensorFlow ("This is a big deal")
    • A lot of students on TensorFlow (“trickling down very fast”)
    • Most teams at hackathon had AI and machine learning components
    • Hardware costs coming down across the board
    • In one year -- AI supercomputing chips with algorithms in the cloud (massive deflation)
    • Big data sets -- startups can assemble big data sets BUT...
    • Newest generation of experts -- focusing on small data sets
      • "They'll say, Primitive and crude machine learning required large data sets but not the newer algorithms (they can work on small data sets) - early but enticing (brings problems into small company realm)
    • With these GPUs -- can create simulated versions of the real world using video game tools (can train AI)
    • Earthquakes, floods, thunderstorms, swarms of birds
    • Train AI - "AI actually has no idea it’s working in a simulated world and not the real world"
    • Potentially: run millions of hours of simulated training at very low cost
    • Google’s Deep Mind data set -- "game playing itself"
  • Why are simulations so important?
    • Ten years ago - AI, neural nets, and deep learning were frowned upon
    • Improbable (company) - can do large-scale scale out simulations using cloud computing technologies and new, proprietary technology
    • Can get a complete picture of the world, can generate a data set
    • "It’s expensive to make things happen in the world (physical changes...building roads, planes...are hard, expensive; have consequences)..."
    • In contrast: simulation, run experiment, introduce change -- easier, cheaper, no consequences
    • Can run millions, billions of simulations
    • Can make real world decisions with more foreknowledge
  • What are other areas where you can think of real world applications of this technology? (jump to clip)
    • New platforms: health + computer science
    • AI hardware for different type of programming; today, Google has a new chip for deep learning cloud
    • New breakthroughs for quantum computing (more powerful deep learning systems)
    • Chip in everything: platforms to run/manage those chips
  • Theme: tech reaching into new places; “tech is outgrowing the tech industry”
    • Thesis: software is eating the world BUT “hard investments” (Soylent, Oculus, Nutribox)
    • Oculus was actually software (breakthrough tech often needs new hardware)
    • Soylent and Nutribox -- same thing
    • Big believers: big breakthroughs in knowledge (Turing, Shannon) -- new model of the world, companies that build on that new knowledge
  • SaaS -- acquisitions; what is left to do there? SFDC or vertical or totally new platforms
    • SaaS as old versions of things in the cloud (WDAY, SFDC, SFSF) -- big categories
    • Changed from on-premise to cloud: seeings sw applications for things that in the old days were cost prohibitive (“screwing it in and hiring army of Accenture consultants) (e.g., expense reporting: Concur) -- new things come into economic viability
    • What was unviable before?
    • Can also scale down to small companies as buyers (<1k employees) - Oracle Financials v. NetSuite
    • Verticals (real estate, construction)
    • Interesting trend
    • Historically: SAP, IBM, Oracle … accessible to top 500-1,000 companies in a handful countries
    • So previously big companies in big countries had an advantage (dominated by 2k-3k multinationals globally)
    • N. Am and Western Europe v ROW
    • Interesting conclusion: smaller company or not one in the Western world (leapfrog similar to what happened with telecom)
    • Larger companies may have harder time adapting
    • Maybe: power shift from larger to smaller companies
    • Companies in western world to those in ROW
    • “Leveling of playing field”
  • The macro view of the economy -- “world is starved for innovation and growth”
    • $10t of capital held in gov’t bonds trading at negative yields
    • “Paying the bank interest”
    • “People cannot find enough productive places to put their capital”
    • Negative, conventional view: starved for growth
    • Positive view: $10t waiting for new opportunities (HC, education, consumer products, media, art, science, cars, housing, etc.)
    • “What needs to be done in the world?”
    • “World has never been more ripe for a VERY large wave of innovation that would be quite easy to finance”
    • More money than ideas and creative, effective people
  • Company building and founders -- types of founders; what has changed?
    • “Gotten more risk tolerant”
    • “We’re much more interested in the magnitude of the strengths than the number of weaknesses”
    • Lack of experience is a strength: “Hard to rewrite the world if you’re too steeped in the world”
    • Financial terms: “buying volatility”
    • “World class strengths where we care about them”
  • One piece of advice
    • Management: “The most common mistake founders is making decisions based on very proximate perspectives without taking the time to think about how others in the company will see the decision...let’s look past the person I’m talking to.”
    • Strategic: “People need to raise prices.”
    • Most companies have sophisticated views on product, design, engineering and naive views on prosecuting a campaign
    • One dimensional view between price and volume (“pricing cheap, selling more”) 
    • Two dimensional view
    • Raise prices, and you can afford a bigger sales/marketing effort
    • Most companies have prices that are too low to get people to buy
    • Too hungry to eat problem
    • Vicious cycle
    • When you charge higher prices, people take the product more seriously, impute more value, make a serious decision, and when they buy it, they experience a greater sense of engagement, commitment, and stickiness

Tagore

 

Where the mind is without fear and the head is held high;
Where knowledge is free;
Where the world has not been broken into fragments by narrow domestic walls; ...
Where the clear stream of reason has not lost its way into the dreary desert sands of habit; ...
Into that heaven of freedom, my Father, let my country awake.

 Rabindranath Tagore, Gitanjali

 

Amazon

“This is still day one in such a big way.” – Jeff Bezos

The Everything Store by Brad Stone is a tremendously good book. Below are my notes and thoughts.

Bezos on what makes Amazon unique:

At Amazon...

  • We are genuinely customer-centric
  • We are genuinely long-term oriented
  • We genuinely like to invent

“Very few companies have all three of those elements.”

On point of view, or thinking differently:

Alan Kay: “Point of view is worth 80 IQ points.” Examples in the book of unique points of view:

  • When Amazon launched book reviews, he received an angry letter from a publisher telling him his business was to sell books, not trash them. “We saw it very differently,” Bezos said. “When I read that letter, I thought, we don’t make money when we sell things. We make money when we help customers make purchase decisions.”
  • D. E. Shaw, where Bezos worked before starting Amazon: “While the rest of Wall Street saw D. E. Shaw as a highly secretive hedge fund, the firm viewed itself differently … [as a] versatile technology laboratory full of innovators and talented engineers who could apply computer science to a variety of different problems. Investing was only the first domain where it would apply its skills.”

Bezos "stealing" ideas:

  • “I don’t think there was anybody Jeff knew that he didn’t walk away from with whatever lessons he could.”
  • “Good artists copy, great artists steal” (Picasso). 
  • "Stealing" ideas from Jim Sinegal, CEO of Costco
    • Sinegal “didn’t have an exit strategy” – “he was building the company for the long term.”
    • “It was all about customer loyalty.”
    • “Costco buys in bulk and marks up everything everything at a standard, across-the-board 14 percent, even when it could charge more. It doesn’t advertise at all, and earns most of its gross profit from the annual membership fees.” 
    • Sinegal doesn’t regret educating Bezos: “I’ve always had the opinion that we have shamelessly stolen any good ideas."
    • Stone doesn’t make the connection, but Prime looks a lot like Costco’s membership fee. 

Competition:

At the outset: “There was competition already. It wasn’t as if Jeff was coming up with something completely new.” At least not at first, but he was thinking about it very differently than the others. 

When Barnes & Noble launched a competing website and sued Amazon, there was a highly publicized Forrester Research report in which Amazon was referred to as “Amazon.Toast. "Jeff to employees: “Look, you should wake up worried, terrified every morning. But don’t worry about our competitors because they’re never going to send us money anyway. Let’s be worried about our customers and stay heads-down focused.”  

Bad news? Assemble the SWAT team:

In early 1998, Mark Breier, the VP of Marketing, showed Bezos a survey that the majority of consumers did not use Amazon.com and were unlikely to do so because they bought very few books. Bezos instructed Breier to assemble a “SWAT team” of recent hires from Harvard Business School to research categories that had high SKUs, were underrepresented in physical stores, and could be easily mailed. Breier: “I brought him very bad news, and for some reason he got excited.” Bezos had the playbook in his head from the beginning. Breier seemed to have nudged him to the next phase. 

On Marketing:

“Over the first decade at Amazon, marketing VPs were the equivalent of the doomed drummers in the satirical band Spinal Tap; Bezos plowed through them at a rapid clip, looking for someone with the same low regard for the usual way of doing things that Bezos himself had.”

“We spend only forty basis points on marketing.” 

Athletes: On hiring Harrison Miller to lead the rollout of a new category—toys: “Miller knew nothing about toy retailing, but in a pattern that would recur over and over, Bezos didn’t care. He was looking for versatile managers—he called them ‘athletes’—who could move fast and get big things done.”

Articulating culture in 1998: customer obsession, frugality, bias for action, ownership, and high bar for talent.

The flywheel: lower prices → more customer visits → increased volume (direct and third party) → increased efficiency from fulfillment and compute infrastructure → lower prices

Coordination: As Amazon grew, coordination became more difficult. At an offsite, a group presented ideas to improve communication between groups. Jeff stood up with a red face and the infamous blood vessel in his forehead pulsing and said, “I understand what you are saying, but you are completely wrong. Communication is a sign of dysfunction. It means people aren’t working together in a close, organic way. We should be trying to figure out ways for teams to communicate less with each other, not more.” The right question wasn’t, How do we communicate better? It was: How do we improve effectiveness. He later said, “A hierarchy isn’t responsive enough to change. I’m still trying to get people to do occasionally what I ask. If I was successful, maybe we wouldn’t have the right kind of company.”

The Innovator's Dilemma as a manual. The Innovator’s Dilemma had a significant impact on Jeff Bezos (and, being an avid reader, many other books did as well). 

  • Steve Kessel ran the book category for a few years until about 2004, when Bezos asked him to take over the emerging digital business. 
  • Bezos: “If you are running both businesses you will never go after the digital opportunity with tenacity.”
  • Bezos had learned that he needed to set up a new and independent business to pursue a disruptive technology properly
  • He told Kessel: “Your job is to kill your own business.”
  • Bezos was influenced by the book Creation by Steve Grand in which Grand described his approach to a 1990s video game called Creatures. Creatures gave players the ability to “guide and nurture a seemingly intelligent organism on their computer screens.” His approach was to allow complex, higher-level behaviors to emerge from simple computational blocks called primitives.
  • Bezos: “Developers are alchemists and our job is to do everything we can to get them to do their alchemy.”
  • Can this apply to businesses as well? What if within a business the functions—Product, Marketing, Sales, etc.—were primitives on top of which young, relatively untested leaders built new, experimental businesses?
  • How many large, successful businesses emerged unexpectedly as the result of solving an internal problem? Palantir is one example.

Gut calls, intuition, vision. One recurring theme in the book is Bezos’s “gut calls”—times when data wasn’t available, was inconclusive, or even pointed to a conclusion contrary to what Bezos believed and Bezos proceeded in line with his intuitions anyway. A few examples:

  • Prime. “In many ways, the introduction of Amazon Prime was an act of faith.” 
    • “The service was expensive to run, and there was no clear way to break even.” 
      • Diego Piacentini, a senior executive running international operations, said, “We made this decision even though every single financial analysis said we were completely crazy to give two-day shipping for free.”
      • Bezos, however, knew from “gut and experience” that it had the potential to change customer behavior—and the overall company—dramatically. He had seen Super Saver Shipping lead to bigger orders and purchases in new categories. He had seen the increase in spending due to lower friction from 1-Click ordering. 
      • And Bezos was right: “The service turned customers into Amazon addicts.” 
      • And costs did come into line. The fulfillment organization “got better at combining multiple items from a customer’s order into a single box, which saved money and helped drive down Amazon’s transportation costs by double digit percentages a year.”
        • This led me to recall this quote from the philosopher Albert Hirschman (source): “Creativity always comes as a surprise to us; therefore we can never count on it and we dare not believe in it until it has happened. In other words, we would not consciously engage upon tasks whose success clearly requires that creativity be forthcoming. Hence, the only way in which we can bring our creative resources fully into play is by misjudging the nature of the task, by presenting it to ourselves as more routine, simple, undemanding of genuine creativity than it will turn out to be.”
  • AWS and pricing. The AWS team, having some sense of Bezos’s philosophies, initially proposed EC2 pricing at $0.15 an hour at which they would breakeven. Bezos unilaterally changed that $0.10.
    • Bezos believed Amazon had a natural costs advantage and that at such pricing IBM, Microsoft, Google, etc. would hesitate to enter the market. 
    • Stone doesn’t mention this, but I wonder if it was also another platform vision, another flywheel. Perhaps Bezos saw that compute infrastructure would become another flywheel connected to the distribution infrastructure flywheel. Increased usage of the distribution infrastructure flywheel led to lower prices, whereas increased usage of the compute infrastructure flywheel led to product innovation. 
  • Kindle pricing. Bezos priced the books at $9.99. “There was no research behind that number—it was Bezos’s gut call.” The price for digital books was the same as that for physical books, typically $15, so it meant they would lose money, but Bezos believed that publishers would eventually lower their prices on digital books to reflect their lower costs. 
  • Kindle wireless. Wireless connectivity to a cellular connection had never been tried before, but Bezos believed that consumers should be able to download a book easily without having to connect to wifi. Bezos faced resistance on both the engineering and the economics but pushed them to do it anyway. 
  • Random customer anecdotes. “Random customer anecdotes, the opposite of cold, hard data, also carry tremendous weight and can change Amazon policy. If one customer had a bad experience, Bezos often assumes it reflects a larger problem and escalates the resolution of the matter inside his company with a question mark.” Wilke: “It’s an audit that is done for us by our customers. We treat them as precious sources of information.”
    • This is why Medallia is an incredible product and on track to be an incredible business.
  • Distribution centers. “[Amazon’s accounting group] fretted about opening seven costly distribution centers and even about having gotten so deeply immersed in the muck of distribution in the first place. Bezos insisted the company needed to master anything that touched the hallowed customer experience, and he resisted efforts to project profitability. ‘If you are planning for more than twenty minutes ahead in this kind of environment, you are wasting your time,' he said in meetings.”

Bezos on Blue Origins, his space travel venture: 

  • “Slow steady progress can erode any challenge over time.”
  • The group’s motto: Gradatim Ferociter, which means “Step by Step, Ferociously.”

Miscellaneous notes:

  • Ignore what other people think.
  • Think. Don’t use PowerPoint. Use six page written narratives.
  • Work backward from the outcome you want. Example: write the press release for a product before starting development.
  • Bezos is a big fan of Nassim Taleb’s book The Black Swan. A key lesson: avoid narrative fallacy. Favor experimentation and clinical knowledge over storytelling and memory. 
  • Nurture the idea. “When a company comes up with an idea, it’s a messy process. There’s no aha moment” 
  • At D. E. Shaw, Bezos was “constantly recording ideas in a notebook.”

  • Alan Kay: “It’s easier to invent the future than to predict it.”

  • “[Bezos] embraces the truth. A lot of people talk about the truth, but they don’t engage their decision-making around the best truth at the time.”

  • “Jeff almost always prefers to build it.” 

Amazon's early timeline:

  • July 5, 1994: founded
  • November 1, 1994: www.amazon.com registered
  • April 3, 1995: first order
  • July 16, 1995: site goes live
  • Early 1996: revenue growing 30-40 percent a month
  • 1996 revenue: $16m
  • 1997 revenue: $144m (which was than what Bezos had predicted as “best case” revenue for 2000 when he was raising capital in 1995)
  • 1998 revenue: 3x growth?

Funding:

  • Early 1994 – early 1995: $10k from Bezos, $5k from Shel Kaphan (first employee), $84k in interest free loans from Bezos
  • Early 1995: $100k from Bezos’s parents (Bezos told parents there was a 70 percent chance they could lose it all)
  • Mid 1995: another $145k from Bezos’s parents
  • Late 1995: $1m at $5m (post-money?) valuation from local investors investing about $50k each (20 of 60 approached)
  • Mid 1996: Projecting $16m in sales, Kleiner Perkins, with its $60m valuation, beats out General Atlantic’s initial $10m valuation, investing $8m (Bezos insisted that Doerr, not a junior team member, join the board)
  • May 5, 1997: IPO (raising $54 million)
  • 1998-2000: $2.2 billion in bonds
    • May 1998: $326 million in junk bonds
    • February 1999: $1.25 billion in convertible debt at 4.75 percent
    • Early 2000: $700 million bond offering

Claude Debussy

My wife, Neval, and I celebrated our sixth anniversary a few weeks ago and did something new: we went to the San Francisco Symphony. 

Garrick Ohlsson played Rachmaninoff's Third Piano Concerto, which we enjoyed. For the encore, however, Ohlsson announced the piece as something "that is so famous it needs no introduction" and went on to play a breathtakingly beautiful, elegant piece. 

We actually didn't recognize the piece, but @sfsymphony helped out:

And here it is:


Innovators

It is commonly thought that the most usual conservatives are the old, and the innovators are young people. That is not quite correct. The most usual conservatives are young people. Young people who want to live, but who do not think and have no time to think about how one should live, and who therefore choose as a model for themselves the life that was.

— Leo Tolstoy, The Devil

Chinese BATs

Apparently, the three Chinese internet congolomerates, Baidu, Alibaba, and Tencent, are referred to as BAT, and Alibaba was a wake up call to the world.

For the twelve month period ending June 2014, this was Alibaba's performance:

  • Revenue: $9.3 billion
  • Revenue growth rate: 92 percent
  • ROIC: 97 percent
  • Pre-tax operating margins: 50 percent

Aswath Damodaran gave Alibaba an equity valuation of $160 billion, or $66 per share. And, by the way, he assumed the 92 percent annual revenue growth dropped to 25 percent for the next five years and tapered to 2.41 percent, the U.S. risk free rate, by year ten—a pretty significant assumption given Alibaba's recent growth, immense market leadership, and relatively low penetration of the Chinese market. Alibaba is currently trading at $88, or about a $215 billion market cap.

Stepping back, Mike Moritz had some interesting things to say in the WSJ:

What has been apparent to very close observers for several years and people who were interested in investing in China is that the whole global online chess board is being rearranged, and Alibaba is the latest and most profound example of that.

Over the next decade, what has effectively been separate theaters of activity — China and the West — will become one global battlefield.

People in the U.S. and Europe are probably in a state of suspended denial about the ambition of the four or five leading Chinese companies. If you do a stack ranking of the most valuable Internet companies in the world and you throw in Google, Facebook, Alibaba, eBay, Tencent, Baidu, Naspers — it isn’t an overwhelming percentage that is American.

Over the next decade, to some extent, I think the advantage lies with the Chinese companies. The Chinese companies will have an easier time competing in the West then the Western companies will have competing in China.

The following are slightly dated, from The Financial Times in March 2014 (so the Alibaba market cap is off), but a good summary of the global internet landscape nonetheless:

Some of the noteworthy insights in that article:

  • They're disrupting the status quo.

Their expansion highlights rapid evolutions in the Chinese internet market, a source of wealth and power that challenges the country’s traditionally state-led economy.

Two factors have shaken up the cosy world of China’s internet. The first is that internet companies have become the largest private-sector companies in China by capitalisation and revenues and are awash with cash. Second, the arrival of the mobile internet has given them something to fight over. Nearly half a billion Chinese use smartphones that cost as little as $50 each to get online.

  • Mobile is forcing them to adjust.

“Before, each of these companies had a distinct sphere, but with the arrival of mobile internet there is more and more convergence on a single model, and more areas of overlap. That’s where the battle lines are now,” says Arthur Kroeber of Gavekal Dragonomics, the research group.

Most of the new acquisitions have been made with mobile in mind. Baidu paid $1.9bn in July for 91 Wireless, an app store designed to give it an edge with mobile users. Alibaba has an undisclosed stake in UC Web, China’s top mobile browser. Last month Alibaba made an offer for AutoNavi Holdings, a mapping service, valuing it at $1.6bn, and allowing it to compete head-to-head with Baidu’s mobile map app.

  • Conglomerates are emerging (and not just in China; Google, Apple, Facebook, and Amazon in the U.S.)

China’s internet giants are becoming what analyst Anne Stevenson-Yang of J Capital Research calls “tech Kereitsus”, referring to the national champions that dominated the Japanese economy in the 20th century with interests in multiple industries. “When companies are this big in China, the difference between public and private is not that important,” she says. “For all intents and purposes these companies have become the ministry of the internet.”

Tren Griffin pointed out that Yuri Milner at DST was able to connect the dots on Facebook's future better than many others because he was able to look at models in Russia. He called it "information arbitrage". I read something similar about Roelof Botha looking at Korea and Japan for indicators that the U.S was ready for various new offerings and how this framework was an important factor in his YouTube investment.

Another fascinating read, also touching on the idea of information arbitrage, was the FT's interview with Zhang Lei of Hillhouse Capital, an early investor in Tencent.

Zhang holds informal Hillhouse gatherings with the leaders of private companies, many of them the consumer and tech enterprises in which Hillhouse invests, and many of them on the verge of going public. “The entrepreneurs in my portfolio companies learn from each other,” Zhang says, noting that he has fostered study sessions between JD and a hypermarket chain he has invested in. “Etailers learn how offline companies think and retailers learn how ecommerce companies think.”

He cites a practical example of companies learning from each other: Zhang invested in Blue Moon, a liquid detergent maker, and had its executives meet JD. That session led Blue Moon to redesign its liquid detergent refill packs so they could fit more easily into JD’s delivery bins. “Bulky is an advantage to attract consumers in a physical world but it is a disadvantage in a virtual world,” he says.

Now Zhang is taking the Chinese template offshore. “The Chinese model, which is mobile-driven, is more suited to emerging markets than the US model, which is desktop driven,” he says. “The socio-economic profile is more similar. We can help companies like Tencent go abroad and accelerate the growth of the mobile internet elsewhere and others also can leapfrog. It is a win-win situation. We are changing intra-Asian trade.”

In Indonesia, for example, Zhang created a joint venture between Tencent’s WeChat mobile messaging platform and Global Mediacom, Indonesia’s largest media, television and pay TV conglomerate. “Indonesia now is like China some years ago,” he says.

There's a lot to think about here, but the biggest takeaway for me from all this is that while Silicon Valley retains a lead in the global technology marketplace, they dynamic is shifting in an unexpected yet interesting way. At least studying in far greater detail what Baidu, Alibaba, and Tencent are doing in the context of their environment, if not spending a significant amount of time in China to truly understand the dynamics, will yield tremendous returns for the ecosystem in Silicon Valley. It's hard to break out of the Valley mindset at times, and this is one way to do that. 

Marc Andreessen's Library

It started with Peter Sims's profile of Andreessen Horowitz on Medium. What really caught my eye was the second sentence in which Sims describes entering the A16Z office: "...you enter a lobby that doubles as a library featuring some of the favorite books of Marc Andreessen."

So I tweeted:

And Marc Andreessen kindly obliged:

In more detail:

Creating a full list of these books will take some time. For now, I'll note what I've read, and what I'd like to read in the near future.

What I've read

  • The Innovator's Dilemma by Clayton Christensen
  • Seeing What's Next by Clayton Christensen
  • The Information by James Gleick
  • Exit, Voice, and Loyalty by Albert Hirschman
  • Antifragile by Nassim Nicholas Taleb

If those books are any indication of the quality of the entire collection, I'm very excited to read the books from his collection that I owned but hadn't yet read:

  • The Second Machine Age by Erik Brynjolfsson and Andrew McAfee
  • Technological Revolutions and Financial Capital by Carlota Perez
  • The Box by Marc Levinson

JMW Turner

Last weekend's FT Weekend had a piece on the upcoming JMW Turner exhibit at the Tate Britain. Turns out, the exhibit will also be at the de Young in San Francisco next year, June to September. 

The paintings are spectacular:

Chichester Canal, 1828

Chichester Canal, 1828

Norham Castle, Sunrise, 1845

Norham Castle, Sunrise, 1845

The Dogano, San Giorgio, Citella, from the Steps of the Europa, 1842

The Dogano, San Giorgio, Citella, from the Steps of the Europa, 1842

The Parting of Hero and Leander, 1837

The Parting of Hero and Leander, 1837

Intuition and Conviction

The Everything Store, the story of Amazon by Brad Stone, has a number of interesting stories, one in particular about Bezos’s time at the hedge fund D. E. Shaw.

David Shaw saw the opportunity in the internet early and tapped Bezos to help him investigate. As Stone writes, “Intrigued by Shaw’s conviction about the inevitable importance of the internet, Bezos started researching its growth.”

It was only then that Bezos learned from the February 1994 issue of Matrix News, a monthly newsletter with facts and analysis about the internet, that from January 1993 to January 1994, essentially the first year of the internet, the number of bytes transmitted over the internet had increased by a factor of 2,057. Another fact was that the number of packets had increased by a factor of 2,560. Bezos summarized the two facts to say that the internet had grown by a factor of about 2,300 in its first year. (It's worth noting that Bezos later mistakenly characterized the growth as 2,300%, which while still large, is still off by two orders of magnitude.)

Shaw and Bezos went on to investigate three ideas:

  • Email. They created a free, advertising-supported email system called Juno, which went public in 1999 and merged with rival NetZero.
  • Online trading. Shaw created FarSight Financial Services, an early E-Trade, in 1995 and sold it to Merrill Lynch. 
  • The everything store. They also discussed e-commerce, the idea of “an Internet company that served as the intermediary between customers and manufacturers and sold nearly every type of product, all over the world.”

Bezos dived into “the everything store” idea and concluded that such scope would be impractical at first. He listed twenty categories, including software, office supplies, apparel, and music, and concluded that books were the ideal starting point. It was then that Bezos decided to leave D. E. Shaw to pursue the idea.

What I find fascinating about this story is that it’s actually not what common lore about Amazon’s founding leads you to believe. Legend says that Bezos was led down the Amazon path when he saw the 2,300 times growth, when in fact, it was David Shaw that saw the opportunity first. It was conviction first, research and facts later.

Shaw saw the opportunity because of his technology orientation and his framework for D. E. Shaw:

While the rest of Wall Street saw D. E. Shaw as a highly secretive hedge fund, the firm viewed itself somewhat differently. In David’s estimation, the company wasn’t really a hedge fund but a versatile technology laboratory full of innovators and talented engineers who could apply computer science to a variety of different problems. Investing was only the first domain where it would apply its skills. 

Framed differently, others had access to the same data in Matrix News that Bezos saw. It was those facts and analysis overlaid on the framework and mindset from Shaw that compelled the idea.

This echoes what I’ve seen elsewhere in early stage companies: conviction emerging from experience and intuition matter more than facts and analysis. In fact, almost by definition with early stage opportunities, the facts and analysis won’t justify the opportunity. 

Capabilities

Earlier this year, there was a bit of an uproar when it emerged that Chamath Palihapitiya had sold his 10 percent stake in Tinder to IAC for $500 million, implying a Tinder valuation of $5 billion and a mind-boggling windfall for Chamath. 

(Chamath, if you're not familiar, was a key person at Facebook, leading its growth team. He made a bit of cash and became an investor, starting the venture capital firm Social + Capital. And Tinder, of course, is a popular dating app, incubated at the internet company, IAC.)

It turned out the reports weren’t true, and while the exact figure wasn’t known, it was clear the stake had been sold and, while far lower, for a pretty decent sum nonetheless: later reports put his stake at 11 percent and the sale price at $55 million.

As I read more, I was fascinated, and I’m writing the story and my thoughts down now, almost four months after the fact, because I find a lot to admire in what Chamath's moves leading up to that sale imply about Chamath’s thinking.

This is the story:

  • Tinder was jointly developed by IAC and a Toronto-based mobile development firm called Xtreme Labs. IAC retained control and the lion’s share of equity, with Xtreme Labs retaining, it seems, 11 percent. The app was developed inside a joint venture between the two called Hatch Labs, which was shuttered in late 2013. 
  • Chamath bought a majority stake in Xtreme Labs in late 2012 for $20 million of his own money (i.e., not via his venture firm). The co-founder and CEO of Xtreme Labs, Amar Verma, and Chamath had known each since college, and Chamath had worked with Xtreme on some projects at Facebook. 

As detailed by Liz Gannes of All Things Digital:

Palihapitiya told me the deal makes sense in light of the current scarcity of good mobile developers. It will be worth it to him to be able to use Xtreme’s spare time to help with Social+Capital projects, and to spin out interesting start-ups. And Xtreme is now working on open-source frameworks that will bring its native app expertise to a broader audience.

— All Things Digital

  • The structure was $6 million up front and $20 million (unclear whether that is inclusive of the $6 million) over the next three years.
  • The most recent comment in the All Things Digital piece, which about captures the general confusion around the purchase (and that from the small number that cared at all), read:

This is the worst thing I’ve seen an investor do. Are you serious? This is a development shop with low margins. I know this team, and I know this space incredibly well. Just pull out of this deal ASAP or reduce your stake for Jesus sakes. Wow. I had respect for Chamath once. Horrible.

  • In late 2013, Xtreme Labs was sold to Pivotal Labs (EMC) for $65 million cash plus incentive compensation to the staff of 300 or so. Chamath, however, kept the equity stake in Tinder for himself as part of the deal. Exactly what Chamath earned on the sale is a function of how much of the total $20 million committed he ended up investing and what exactly “majority stake” means, but regardless it’s a decent multiple. Let’s say it was $20 million for 80 percent. The $65 million sale would have netted him $52 million for a 2.6 return with a holding period of about a year. 
  • Then, about six months later, Chamath sold the stake in Tinder for $55 million if we believe the reports. The $52 million from Xtreme plus $55 million for Tinder yields $107 million for a 5.4x return in about a year and half. Not bad. 

This is what I think is noteworthy:

  • Forget the economic return, though that alone is noteworthy. 
  • When everyone else, including Chamath himself via his venture firm, was investing in applications, Chamath bought a development firm. 
  • My guess is that, having looked at a large sample of companies and given his own experience, he did the back of the envelope math on number of possible opportunities and the scarcity of good development teams. He referenced the "scarcity of good mobile developers" thesis to Gannes, but I’d word it differently: there’s a scarcity of good development engines, groups that can work together to put out good product.
  • A slightly different lens is that there even fewer good mobile development engines with great optionality. Yes, good teams come together and start companies in which case the thesis is defined and the initial direction largely set. Good teams leave themselves open to insight and groups do "pivot" so often there is a lot of optionality, but there aren’t many truly experimental development engines. I believe Chamath saw Xtreme Labs as a way to learn and experiment, to engage in "black swan farming" (stealing Paul Graham's phrase). He ended up selling and netting a great outcome, but there’s an argument that he may have sold too soon.
  • What I like most about this story is the unconventional, first-principles thinking. If you're investing in ideas around mobile and being thoughtful about the macro trends, comparing the supply of talent with the demand for (and dramatic upside in) offerings, buying a development firm is a natural outcome of that logic. Unlike others though, Chamath was willing to follow the logic of his analysis all the way into core development talent. He was investing in teams and companies via his venture firms, but he was also building core capabilities and learning via the investment in Xtreme Labs. 

Clarification: Beyond the facts, I don't know if any of this is trueall conjecture. 

Invent the future

We are still the masters of our fate. Rational thinking, even assisted by any conceivable electronic computors, cannot predict the future. All it can do is to map out the probability space as it appears at the present and which will be different tomorrow when one of the infinity of possible states will have materialized. Technological and social inventions are broadening this probability space all the time; it is now incomparably larger than it was before the industrial revolution—for good or for evil.

The future cannot be predicted, but futures can be invented. It was man’s ability to invent which has made human society what it is. The mental processes of inventions are still mysterious. They are rational but not logical, that is to say, not deductive.

— Alan Kay, Inventing the Future

Be Guided by Beauty

Jim Simons in a nutshell:

Dr. Simons received his doctorate at 23; advanced code breaking for the National Security Agency at 26; led a university math department at 30; won geometry’s top prize at 37; founded Renaissance Technologies, one of the world’s most successful hedge funds, at 44; and began setting up charitable foundations at 56.

— The New York Times

In the video below, Simons shares some guiding principles with MIT students. I liked one piece of wisdom in particular:

Be guided by beauty. Everything I’ve done has had an aesthetic component to me. Building a company trading bonds…what’s aesthetic? If you’re the first one to do it right, it’s a terrific feeling and a beautiful thing to do something right, like solving a math problem.

My other notes and takeaways from the talk:

  • Be first: do something no one else is doing. 
  • Powerful insights on data everyone else has = success. Powerful insights on data no one else has = tremendous success. As Simons recounts:

In those days, we sent people down to the NY Federal Reserve to copy histories of interest rate numbers. They didn’t exist in the ‘70s. You couldn’t buy data, and there certainly wasn’t online delivery. To build the original models, you had to collect a lot of data by hand, which we did.

"What’s the secret to our success?"

  • People. "We start with great scientists. We start with first class people that have done first class work, or that we have reason to believe will do first class work. Because I was there at the beginning with a few other people that were pretty good at math and science, we had pretty good taste."
  • Infrastructure. “We provide people with a great infrastructure. It’s easier to get to work here than anywhere else.”
  • Open environment. “The most important thing we do is have an open atmosphere. My belief is that the best way to conduct research on a broad scale is to make sure as much as possible that everyone knows what everybody else is doing. (At least as quickly as possible. Sometimes you want to keep an idea to yourself for a bit so you don’t look like an idiot.) The sooner the better, start talking to other people about what you’re doing. Because that’s what will stimulate things the fastest. No compartmentalization. Everybody meets once a week. Any new idea gets brought up, discussed, vetted, and hopefully put into production. It’s an open atmosphere.”
  • Alignment to firm success. “And people get paid on the overall profits, not on their own work. Everyone has an interest in everyone else’s success.”

"Those policies—no one of which seems remarkable—turn out to be a pretty winning combination: great people, great infrastructure, open environment, and try to get everyone compensated roughly based on overall performance."

"My guiding principles"

  • Different. “Do something new. I love to do something new. I don’t like to run with the pack. For one thing, I’m not such a fast runner. If you’re one of n people working on the same problem in different places, I know if it were me I’d be last. I’m not going to win that race. But if you can think of a new problem or a new way of doing something, that other people aren’t all working on at the same time, maybe that would give you a chance.” 
  • People. "Collaborate with the best people you possibly can. When you see a person, or get to know a person, that seems like a great guy or a great gal to work with at something, try to find a way to do it. Because that gives you some reach and some scope. And it’s also fun to work with terrific people.”
  • Beauty. "Be guided by beauty. Pretty much everything I’ve done has had an aesthetic component, at least to me. Now you might think, building a company trading bonds—what’s so aesthetic about that? What’s aesthetic about it is doing it right. Getting the right kind of people, approaching the problem, and doing if right. If you feel you’re the first one to do it right—and I think we were—that’s a terrific feeling. It’s a beautiful thing to do something right. It’s also a beautiful thing to solve a mathematics problem or create some mathematics that people hadn’t thought of before. 
  • Persistence. "Don’t give up. At least, try not to give up. Sometimes it’s appropriate to be at something, trying to do something, for a hell of a long time."
  • Luck. "Hope for some good luck." 

Remember the Obvious

I read this today by Charlie Munger, and it echoed something I’d been thinking about:

[We] to try more to profit from always remembering the obvious than from grasping the esoteric. … It is remarkable how much long-term advantage people like us have gotten by trying to be consistently not stupid, instead of trying to be very intelligent. There must be some wisdom in the folk saying, `It’s the strong swimmers who drown.’

 

Business Education

I’m slowly making my way through Clay Christensen’s latest piece in HBR, "The Capitalist Dilemma," which seeks to understand why “despite historically low interest rates, corporations are sitting on massive amounts of cash and failing to invest in innovations that might foster growth.”

Christensen, along with co-author Derek van Bevers, lays out a nuanced argument, detailing three different types of innovations—performance-improving, efficiency, and market-creating—and argues that various incentives have combined to drive “companies [to] invest primarily in efficiency innovations, which eliminate jobs, rather than market-creating innovations, which generate them.”

Christensen and van Bevers explore the reasons driving this shift and also lay out four proposed solutions. One called “Rebalancing Business Schools” caught my eye because it echoes something I’ve been thinking about and am starting to believe is a significant shortcoming in how we think about businesses.

From that section:

Much as it pains us to say it, a lot of the blame for the capitalist’s dilemma rests with our great schools of business, including our own. In mapping the terrain of business and management, we have routinely separated disciplines that can only properly be understood in terms of their interactions with one another, and we’ve advanced success metrics that are at best superficial and at worst harmful.

Finance is taught independently in most business schools. Strategy is taught independently, too—as if strategy could be conceived and implemented without finance. The reality is that finance will eat strategy for breakfast any day—financial logic will overwhelm strategic imperatives—unless we can develop approaches and models that allow each discipline to bring its best attributes to cooperative investment decision making. As long as we continue this siloed approach to the MBA curriculum and experience, our leading business schools run the risk of falling farther and farther behind the needs of sectors our graduates aspire to lead.

The intricate workings of the resource allocation process often are not studied at all in business schools. As a result, MBAs graduate with little sense of how decisions in one part of the enterprise relate to or reflect priorities in other parts. One of our alumni noted, “The only way we learned what projects to invest in was in FIN I [the introductory finance course at HBS].” A whole host of questions goes unasked—and unanswered: How do I identify conditions that signal opportunity for long-term, growth-creating investment? What proxies for estimated future cash flows can I use in evaluating an investment that is pointed toward a new market? How do we identify and build innovations that will help noncustomers perform jobs they need to get done? When are the traditional metrics of IRR and NPV most appropriate, and when are they likely to lead us astray? Since the functions of the enterprise are interdependent, we should mirror this in our teaching.

And lest one think that this is an academic question, removed from reality, Charlie Munger, Vice Chairman of Berkshire Hathaway, articulated a similar idea at the 2011 annual meeting of Berkshire Hathaway:

Costco of course is a business that became the best in the world in its category. And it did it with an extreme meritocracy, and an extreme ethical duty—self-imposed to take all its cost advantages as fast as it could accumulate them and pass them on to the customers. And of course they’ve created ferocious customer loyalty. It’s been a wonderful business to watch—and of course strange things happen when you do that and when you do that long enough. Costco has one store in Korea that will do over $400 million in sales this year. These are figures that can’t exist in retail, but of course they do. So that’s an example of somebody having the right managerial system, the right personnel solution, the right ethics, the right diligence, etcetera, etcetera. And that is quite rare. If once or twice in your lifetime you’re associated with such a business you’re a very lucky person.

The more normal business is a business like, say, General Motors, which became the most successful business of its kind in the world and wiped out its common shareholders… what, last year? That is a very interesting story—and if I were teaching business school I would have Value-Line-type figures that took me through the entire history of General Motors and I would try to relate the changes in the graph and data to what happened in the business. To some extent, they faced a really difficult problem—heavily unionized business, combined with great success, and very tough competitors that came up from Asia and elsewhere in Europe. That is a real problem which of course… to prevent wealth from killing people—your success turning into a disadvantage—is a big problem in business.

And so there are all these wonderful lessons in those graphs. I don’t know why people don’t do it. The graphs don’t even exist that I would use to teach. I can’t imagine anybody being dumb enough not to have the kind of graphs I yearn for. [Laughter] But so far as I know there’s no business school in the country that’s yearning for these graphs. Partly the reason they don’t want it is if you taught a history of business this way, you’d be trampling on the territories of all the professors and sub-disciplines—you’d be stealing some of their best cases. And in bureaucracies, even academic bureaucracies, people protect their own turf. And of course a lot of that happened at General Motors. [Applause]

I really think the world … that’s the way it should be taught. Harvard Business School once taught it much that way—and they stopped. And I’d like to make a case study as to why they stopped. [Laughter] I think I can successfully guess. It’s that the course of history of business trampled on the territory of barons of other disciplines like the baron of marketing, the baron of finance, the baron of whatever.

IBM is an interesting case. There’s just one after another that are just utterly fascinating. I don’t think they’re properly taught at all because nobody wants to do the full sweep.

— h/t Farnam Street Blog

And that's just part of it: combining business lenses into a holistic view of what it means to build a profitable, sustainable business. An idea I've been toying with is that the act of creating truly unique and valuable businesses draws from the arts and humanities as well, that all great businesses, which are essentially collections of people creating things to sell to other groups of people, "make our hearts sing." 

Arguments

I found the following advice in Daniel Dennett’s book Intuition Pumps and Other Tools for Thinking very effective:

How to compose a successful critical commentary:

  • You should attempt to re-express your target’s position so clearly, vividly, and fairly that your target says, “Thanks, I wish I’d thought of putting it that way.
  • You should list any points of agreement (especially if they are not matters of general or widespread agreement).
  • You should mention anything you have learned from your target.
  • Only then are you permitted to say so much as a word of rebuttal or criticism.

The goal is to get yourself and the other party into a frame of mind where each is open to the other’s viewpoint. 

Full stack startups

I’m excited to see the increased discussion around “full stack” startups—startups that take on the entire challenge of offering a service, rather than just offering a layer of technology that other service providers use. 

Think Tesla, Uber, Warby Parker, Redfin, and increasingly, as it gets into original content, Netflix. 

In Tesla’s case, rather than sell technology to incumbent automobile manufacturers, Elon Musk took on the thornier challenge of creating an automobile company built from the ground up on new technology. The challenges were far greater, but he didn’t have to deal with incredibly long sales cycles, revenue concentration, or customizing the technology to fit existing, and often outdated, technologies and practices, among other benefits. 

By going full stack, he was also able to address many of the other inconveniences in the auto purchasing and ownership experience, namely the dealership-based purchasing and servicing process, which he detailed in a blog post related to New Jersey’s decision to ban Tesla sales in company-owned showrooms. 

I first heard the idea of “full stack” startups from Glenn Kelman, CEO of the real estate company Redfin, and I wrote about it in an earlier post. His mental model for the idea was “change the game”. His idea was that, rather than walk into the existing game with proverbial hat in hand, offering to fit a disruptive idea into the confines of the existing industry, one should consider changing the game entirely. Abstract the discussion one level higher to ask: What is the fundamental service being provided by the industry and how might it be improved dramatically with new technology and by being re-thought completely?

Chris Dixon of Andreessen Horowitz wrote about the idea earlier this month, coining (I believe) the phrase “full stack startups”.

And then today I read this great series of tweets by Balaji Srinivasan, also at Andreessen Horowitz, which prompted me to write this post to collect my thoughts:

Veeva is Different

I was reading Veeva's S-1 and recalled something I heard from Mike Maples. He pointed out that Microsoft raised $1 million of capital and that eBay raised $5 million (video). These huge, game-changing companies didn’t raise much capital. They created products for which, to quote Maples, “the world had a high give-a-shit factor”. The world needed their products badly, letting them fail, iterate, and improve, all the while begging them to continue to do business with them. And when these companies do get it right, their product gets pulled into the market. 

After initially glancing through it’s S-1, I tweeted how remarkable it was that it raised only $7 million and was on track to clear $200 million of revenue, having doubled revenue consistently year after year.

Since sales and marketing tend to be the most significant use of capital in SaaS companies, I focused on that, and it appeared they performed far better in that dimension. In my tweet, I estimated they were about two to three times better than other SaaS companies.

Turns out I was wrong. Veeva Systems was 3.6 times better than the average SaaS company. For every $1 they spent on sales and marketing, they generated $3.05 in annual recurring revenue. They made three times their money in just the first year. The average for all other currently public SaaS companies in their pre-IPO years was $0.85. These companies lost money in the first year, making it up in later years due to the subscription nature of the revenue.

image

Digging one layer deeper, what’s remarkable is how far beyond the typical range Veeva Metrics is in this regard, standing out even from the second best-performing company, Demandware.

image

How does this happen?

I don’t have any proprietary insight today into how this came about, but there are hints in the S-1:

We sell our solutions through our direct sales organization and had sales representatives in 13 countries as of July 31, 2013. Our sales force is managed regionally by general managers in North America, Europe and Asia Pacific who are responsible for all sales, professional services and customer success in each of their geographies. We believe this provides for an integrated view of the customer relationship as well as higher levels of local and regional focus on our customers.

Life sciences companies are typically organized by the major functions of research and development for the creation and development of new solutions, and commercial, for the sales and marketing of those solutions once they are approved for use. In large life sciences companies, research and development and commercial business lines may also have separate technology and business decision makers. Accordingly, we market and sell our solutions to align with the distinct characteristics of the research and development buyer and the commercial buyer. Within each region, we have research and development and commercial sales teams. Each of these teams is further divided to sell to the largest global pharmaceutical companies and to smaller life sciences companies.

We believe the combination of our industry­ focus and commitment to customer success provides strategic advantage and allows us to more efficiently market and sell our solutions as compared to horizontal cloud­based companies. Our awareness, demand generation and sales cultivation programs are focused and designed to be cost efficient because we target only the life sciences industry buyers. We believe that we further benefit from word­ of­ mouth marketing as customers endorse our solutions to their industry peers. This allows us to focus our sales and marketing efforts without the need for a larger number of sales executives.

(Emphasis mine.)

In short, the company is sub-divided into regions with general managers seemingly running mini organizations targeting the companies in their regions. One person owns sales, professional services, and customer success. It’s a powerful alignment.

Combine that with the industry focus, where dramatic success with a few clients will spread through word-of-mouth, and it isn’t hard to imagine inbound requests flooding into the organization.

Professional services drove customer success

Professional services is something else that jumps out in the S-1. Veeva spent an incredible amount on professional services. Professional services was 50 percent of its revenue in early years, declining to 30 percent more recently. It was probably even higher in the years not detailed in the S-1. This was really low margin, difficult-to-manage and -scale business. They spent $50 million on professional services between 2009 and 2013.

Customer success drove very high renewal and upsell rates

Professional services drove customer success and had a powerful impact on future revenue from those customers. This is demonstrated in its churn metrics, what Veeva Systems measures as its renewal rates.

At the end of each year, Veeva Systems would take the total annualized revenue from the full list of customers at the end of the prior year and divide by the annualized revenue from those customers at the end of that prior year.

If some customers left and others spent about the same, this metric would be below 100 percent.

If, on the other hand, customers were not only sticking around but buying more from Veeva Systems, renewal rates would be above 100 percent.

For 2010, 2011, and 2012, renewal rates were 192, 159, and 187 percent, respectively. Specifically, on average across all customers that were customers at the end of 2011, for every $100 of subscription revenue, they had increased that to $187 by the end of 2012.

Having seen many such metrics for SaaS companies, I know these are amazing, particularly at the revenue levels to which they apply. Such an organic uplift from the established revenue base makes for very efficient sales and marketing because that revenue required almost no additional work.

Update: Subsequent to writing this post, David Skok and Pacific Crest put together a great survey of private SaaS company metrics, one of which has renewal rates as measured by Veeva here. It further illustrates how unusually high Veeva’s net renewal rates are compared the median 110 percent below:

image

Further, I suspect this is skewed higher by companies with smaller revenue bases. For other metrics, David Skok and Pacific Crest separated those, finding a skew towards the high end, driven by the lower revenue companies. I’m confident that Veeva’s performance here is mind-blowing for any company with more than, say, $10 million in annual recurring revenue. 

__

Notes:

1. The exact calculation I used to assess sales and marketing effectiveness is below. I calculated this for every quarter highlighted in the S-1 of every SaaS company that went public, whether currently public or acquired. I then averaged the quarterly metrics to come up with one metric per company to estimate pre-IPO sales and marketing effectiveness.

image

2. I’d be remiss if I didn’t mention the folks from whom I learned this. They include Rory O’ Driscoll at Scale Venture Partners in a series of insightful posts on what he calls the Magic Number here and here, Phillippe Botteri when he was with Bessemer Venture Partners writing about CAC ratio here, and David Skok at Matrix Partners in a tremendous series on SaaS metrics here.

3. One thing worth commenting on is that Veeva Systems is built on top of Salesforce’s Force.com platform. This isn’t free. Salesforce gets a cut. I don’t know the exact arrangement, and I didn’t see it outlined specifically in the S-1. But it’s embedded in the gross margins, which, while lower than other SaaS companies, aren’t dramatically lower. Veeva’s 2012 gross margin on subscription revenue was about 75 percent. SaaS companies will ideally have 90 percent subscription gross margins. So this is significant. But it’s not 50 percent, which would be eye opening. So Botteri’s CAC ratio approach above, which uses GMs instead of revenue, would be one way to refine this. Back of the envelope adjustments indicate, however, that you’d still find Veeva Systems holding a significant lead in the areas I explored so I don’t think this changes my conclusions.

Polymaths

A human being should be able to change a diaper, plan an invasion, butcher a hog, conn a ship, design a building, write a sonnet, balance accounts, build a wall, set a bone, comfort the dying, take orders, give orders, cooperate, act alone, solve equations, analyze a new problem, pitch manure, program a computer, cook a tasty meal, fight efficiently, die gallantly. Specialization is for insects.

 — Robert A. Heinlein