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Contents
Preface
CHAPTER 1 Burn the Business Plan
CHAPTER 2 Twelve Things Every Aspiring Entrepreneur Should Know
CHAPTER 3 Why Start a Company?
CHAPTER 4 What Motivates Entrepreneurs?
CHAPTER 5 Can You Survive the Entrepreneur’s Curse?
CHAPTER 6 Big Companies Can Be Schools for Startups
CHAPTER 7 Copycat Entrepreneurs
CHAPTER 8 Preventing Failure Before It Happens
CHAPTER 9 Don’t Waste Time Doing Things That Don’t Work
CHAPTER 10 Planning for Success
CHAPTER 11 Becoming a Successful Entrepreneur
Acknowledgments
About the Author
Notes
Index
In memory of Ewing Marion Kauffman.
A great American entrepreneur who started his company without a college education, a written business plan, an incubator experience, a mentor, or a venture investor. He was committed to the idea that more people should, “Make a job, not take a job,” and devoted his fortune to helping others see that entrepreneurship is a choice that anyone can make. Like so many great Americans, Ewing Kauffman saw his life’s story as emblematic of how a regular person from humble beginnings could achieve success through personal initiative and hard work in a free nation that encouraged individuals to find, develop, and apply their creative talents in business. The Kauffman Foundation has embodied his magnificent choice to give back by lighting the path for those willing to take the personal risk of starting businesses that make life better for others. Thank you, Mr. K.
Preface
How can real, practical information help potential entrepreneurs? I have started companies, managed small and large entities, been a venture investor, consulted with big firms and governments on innovation, and spent time as a professor. For ten years, I was privileged to run the Kauffman Foundation, the “Foundation of Entrepreneurship” in Kansas City. Along the way, I was lucky enough to spend time with many successful entrepreneurs, aspiring entrepreneurs, investors, and business visionaries.
These experiences made me realize that many of the popularly held ideas about how, when, and why people start companies probably are wrong. As an economist, I began to look skeptically at the powerful underlying memes—the largely unexamined but widely accepted ideas that shape our view of the world—especially those applicable to new business success. The entrepreneurship memes seemed to be little more than an assortment of stories and case histories that had crystallized into a rubric.
So, in 2002, I embraced the opportunity to direct the Kauffman Foundation. The Foundation was a nearly $2 billion endowment established by Ewing Marion Kauffman, an innovative Kansas City pharmaceutical company founder, and was the world’s largest philanthropy dedicated to promoting entrepreneurship. This was an irresistible challenge: I knew that a continuing stream of new businesses was critical to society’s growth and advancement, and that what entrepreneurs really do is too important to be described by glittering narratives that are not based on evidence. After all, innovative businesses bring us unimagined products and services, create most of the new jobs in our economy, and are the most important force driving growth and creating expanding welfare. Entrepreneurs are too valuable a national resource to be subjected to a potpourri of unsubstantiated aphorisms dressed up as good business advice.
The Kauffman Foundation provided the platform to recruit a small army of brilliant economists to initiate serious research on how new businesses really get started and grow. The team extended the work of Joseph Schumpeter1 and William Baumol,2 two influential economists who had examined entrepreneurship well before almost anyone knew how to spell it, much less what it meant. Research inside Kauffman and scholarship supported by the Foundation produced a torrent of empirical research on entrepreneurs, which resulted in insights that people now seem to think that we’ve always known. For example, it was not previously well understood that young firms create more than eighty percent of all new jobs in our economy, or that new business formation plays such a significant role in economic expansion. Now, happily, entrepreneurship is embedded in every discussion about economic growth. The Kauffman team was the first to inject intellectual, empirical rigor into research about how new firms are created. Because of this work, we know much more about how businesses are formed and how they grow and what is likely to improve your chances for success.
This book seeks to translate many of these findings, and the experiences of some of the thousands of entrepreneurs whom I’ve met, into practical guidance and lessons. It is intended to illustrate how businesses really start, grow, and prosper. The first four chapters speak to the “what” of business startups, the following four to lessons learned by those who have gone before you, and the last section is devoted to data-derived facts, not myths, and realistic guidance from successful entrepreneurs.
CHAPTER 1
Burn the Business Plan
One of the headwinds that challenges a fact-based discussion of successful entrepreneurship is the looming presence of a handful of wildly successful high-tech innovators. The icons are, of course, the household names: Bill Gates, Steve Jobs, and Mark Zuckerberg, the myth-making college dropouts who became billionaires before thirty. While their stories make fascinating reading, and we owe them immeasurable gratitude for their revolutionary contributions, their narratives hold very few actionable lessons for the more than ninety-five percent of entrepreneurs who want to start a construction business, manufacture innovative building materials, become a service provider, or develop a franchise. These are the people more like you and me, from all walks of life, who want to start businesses.
In fact, the romanticized narrative of the young, mostly male, high-tech wizard accounts for the smallest constellation in the universe of entrepreneurs—only about five to seven percent. Their new businesses get almost all the high-profile investment by venture capital firms, most of the media coverage, and—here’s a surprise—experience the highest failure rate of business startups. About eight in ten disappear within five years.
The real story on startups and their success rates reveals something very different. Most entrepreneurs never went to college, and most did not start their companies until they were well along in their careers. The average entrepreneur is nearly forty years old when he launches, and more than eighty percent of all new companies are started by people over thirty-five. More entrepreneurs are between forty-five and fifty-five than any other cohort, and entrepreneurs over fifty-five now create more companies than those under thirty-five. And—another surprise—the chances of a new company surviving rises with the age of the entrepreneur.
Recognizing that the “mature” entrepreneur is a significant element in the startup world also leads us to acknowledge the influence of working for someone else before you start your own company. The average entrepreneur was an employee for almost fifteen years before launching a startup. Although it may seem counterintuitive, a big company can operate as a de facto school in which you can learn how to—and sometimes how not to—test, manufacture, price, and sell products; organize a workforce and deal with suppliers; finance equipment and facilities;
and comply with legal and regulatory requirements. Research also tells us that a significant number of innovative entrepreneurs have launched out of large companies that didn’t want to diversify to invest in what they saw as a tangent to their core businesses. Some established companies even have signed over ownership of innovative ideas to employees and sent them off with good wishes for success.
Planning, then Pivoting, then Pivoting, then Pivoting
Early in the history of the Internet, a raft of businesses started within months of one another, all premised on the thesis that commerce was moving to what was then known as the “World Wide Web.” Most of these startups were described in Silicon Valley shorthand as “B2C,” business to consumer, companies. Amazon, founded in July 1994, was one. Another group of companies, “B2B,” emerged to conduct bigger transactions between companies. Michael Levin was ready to play.
Levin went to the University of Wisconsin to study international relations before going to Harvard for an MBA. After graduating, he went to work for an international steel trading firm, and eventually acquired the company. Among other activities, Levin’s business involved buying the future production of steel mills in, say, China, with a plan to sell it a few months later at a higher price to factories making steel products in other countries.
As the frenzy of Internet commerce grew—now remembered as the dot-com era—Levin developed the idea of an online market for steel. After talking with customers about the feasibility of trading on the Internet, he came to believe that he might have a good idea for a B2B business. Next, he went to Silicon Valley to sell his story. Given his industry experience, past success, and readiness to invest some of his own money, venture firms were enthusiastic. They backed him on the spot, even though he presented only a few slides.
While he was organizing his new company, Levin’s investors asked him to develop a detailed business plan complete with sales forecasts and budgets. Once underway, ensconced in nice offices with a team of programmers and a state-of-the-art website, Levin’s startup came face-to-face with reality. Steelmakers who sold, and factory customers who bought, didn’t want to abandon the old ways of trading steel. They liked the face-to-face bargaining over multimillion dollar contracts, and the steak dinners and golf outings that were part of the tradition. Sales on his Internet-based trading platform were not materializing.
While the B2B steel-trading business was failing, unexpectedly, both steelmakers and steel users were expressing strong interest in using the portion of the new company’s computer models that forecast prices. In addition, they wanted to buy access to the company’s customized supply-chain software. Levin’s board was unimpressed. The vision that he had laid down in his plan had convinced his investors that the steel market eventually would see the virtues of B2B trading. Levin had written a plan that was too convincing. He found it impossible to persuade his board that the company should pivot to become a software supplier. Levin eventually bought out his investors and his startup became a software business, one of the few B2B startups that still survives.
B2C businesses, on the other hand, flourished. New ones open every day selling everything imaginable, as well as renting out everything from used dresses and fur coats to spare bedrooms.
Levin returned to his old business, trading steel, one contract at a time, on the phone, or in face-to-face meetings, with useful acquired wisdom. “Business plans are like a religious ritual. If you write one, success is supposed to follow. In the case of my startup, the investors really believed in the plan, and any adjustments responding to real customer demand, like selling software, was read as a dangerous step that put in jeopardy the future envisioned in the plan.” In Levin’s words, “Making a successful company requires an intimate tango with customers, not a tight grip on a business plan.”
My own entrepreneurial journey began when I was a junior professor at Johns Hopkins in Baltimore. Healthcare economics was my field, and one of my particular areas of interest was whether hospital “monopolies”—which was, at the time, the way that the Federal Trade Commission defined ownership by a hospital company of more than one hospital in the same market—resulted in price fixing. Obviously, by the FTC’s use of the pejorative “monopoly,” they believed that the answer to that question was “yes,” and they posited that such price fixing meant higher hospital prices for patients and their insurers in a “monopoly” market. On the other hand, hospital companies asserted that owning more than one hospital in the same market achieved cost efficiencies that saved money for patients and insurance companies.
My research as an academic economist focused on quantitative healthcare issues, and I was asked both by hospital companies and the federal government to research this issue and report on my findings. The project required assembling more accounting and clinical data on hospitals than had ever before been brought together. I gathered billing records for every hospital in four states, as well as anonymous data on hundreds of thousands of their patients. This was a “big data” project before the term was even in use.1 While analyzing the statistics, I came across an unexpected and what seemed to me to be a rather astonishing fact: even standardizing for income levels, patients at some lower-cost hospitals had better healthcare outcomes than patients at higher-cost hospitals, which seemed to indicate that they were receiving better care.
With a lot more research and number crunching, the data showed that hospitals could cut costs and, at the same time, improve the quality of care. Best of all, the numbers that I’d collected clearly demonstrated how those hospitals could operate more effectively, and I was able to explain it.
This discovery upset my career plan. I could write another academic paper to explain my findings—knowing, though, that no matter how widely it might be read in scholarly or even industry circles, hospitals would not take it upon themselves to design and implement the actual mechanics and systems to improve their patient care and lower their prices.
As I saw it, my only other option was to develop, sell, and install decision-support systems so that hospitals could achieve the win-win outcomes that were possible. This posed a difficult dilemma. Hopkins frowned on faculty members becoming involved in businesses. I’d have to resign my professorship. I felt like the lone traveler in Robert Frost’s poem “The Road Not Taken”; not only would I be leaving a great university, but I’d be stepping away from a steady, if not exactly copious, financial sinecure. If I took the leap, I’d have to look to my fragile little startup not only to fund itself but to pay my salary as well. To make the decision even more difficult, just as this quandary began to unfold, my wife and I had bought a new house and become first-time parents. For the next few years, I would have very few good nights’ sleep and always thanked my lucky stars that my wife was employed and could put beans on the table if everything went to hell.
In the early years, most startups face particular financial stress. When my little company’s revenues looked like they would be insufficient to acquire data, buy computers, and hire needed staff—mostly former Hopkins students—my banker advised me to talk with one of the newly formed venture capital funds in the area. The ensuing meeting was a scene out of a movie. After hearing my story, the investor put his feet up on his desk, lit a cigar, and lectured me about how professors made bad businessmen. Presuming all investors would say the same thing, we tightened our belts. We spent no more than our revenue. I later learned that this approach to self-funding was known as “bootstrapping.”
Like many first-time entrepreneurs, I had no relevant experience. I had to feel my way along every day. No one, including my bank, ever suggested writing a business plan. If they had, it surely wouldn’t have helped.
Also like many first-time entrepreneurs, I was sure that my product was so revolutionary that customers would rush to buy it. But, much to my consternation, sales did not come easily and the price that the market was willing to pay didn’t come close to what I thought that my product was worth or even what it would cost to produce. I learne
d that hospitals didn’t much care about efficiency and, frankly, they didn’t seem to care that much about the better patient outcomes that might result. At that time, hospitals were being paid on a cost-plus basis by the federal government and insurance companies so, to them, efficiency was an interesting public policy discussion but hardly worth upsetting doctors, nurses, and administrators. Even worse, when talking with some hospitals’ lawyers, I learned that they were very uncomfortable having data on patient outcomes in their possession, which could prove embarrassing and perhaps result in liability if made public. Essentially, hospitals and their lawyers were afraid to focus on improving the quality of care because that would imply that the quality of care could be improved. My product, which could have saved hospitals lots of money and produced better care in the bargain, faced indifference and even active resistance from those who ran the enterprises and sought to protect their institutions.
My reflex was to “blame the customer” for being too dumb to know what was good for them. I realized, much more slowly than I should have, that I couldn’t convince hospitals to buy my better mousetrap. I had to find a different customer. Fortunately, in the company’s second year, it finally dawned on me that all hospital construction was financed with debt. New buildings were funded by long-maturity bonds that repaid investors’ capital in thirty years but also paid interest yearly from the hospital’s current income. Just like the first cousin of hospital bonds, the municipal bond, hospital debt had to be insured to protect investors in the event of a hospital’s bankruptcy. If the companies selling hospital bond insurance could know that Hospital A was run more efficiently than Hospital B, they could better estimate their comparative risk and set premiums accordingly.
When I appeared for my first sales call to a company that sold hospital bond insurance, the chief underwriter nearly kissed me. “I knew you’d show up one day!” he said. Of course, he didn’t mean me, personally. Rather, he knew that there had to be a quantitative way to judge the financial strength of hospitals as well as to forecast whether a given institution might be more at risk for unfavorable patient outcomes, which in his world meant the increased risk of high-dollar malpractice verdicts.
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