Forecasting Business Quotes

Many people object to “wasting money in space” yet have no idea how much is actually spent on space exploration. The CSA’s budget, for instance, is less than the amount Canadians spend on Halloween candy every year, and most of it goes toward things like developing telecommunications satellites and radar systems to provide data for weather and air quality forecasts, environmental monitoring and climate change studies. Similarly, NASA’s budget is not spent in space but right here on Earth, where it’s invested in American businesses and universities, and where it also pays dividends, creating new jobs, new technologies and even whole new industries.

In fact, the business plans of the next 10,000 startups are easy to forecast: Take X and add AI. Find something that can be made better by adding online smartness to it. An

Like the weather or bonds between lovers, transformations can never be predicted. All energy transmutes one day or another, in one way or another. Either in its form or composition, or in its position or disposition.

You have to include inflation in your annual revenue and expense forecasts. You have to treat inflation as an annual fee your business pays into the economy. If inflation is 2% for example, that means the economy is charging your business a 2% annual fee and so you gotta make sure your income and total assets grow at minimum 2% annually just to keep up.

The recipe for success in business is this: increase profits and forecasts by reducing expenses through labor elimination and restructure.

When forecasting the outcomes of risky projects, executives too easily fall victim to the planning fallacy. In its grip, they make decisions based on delusional optimism rather than on a rational weighting of gains, losses, and probabilities. They overestimate benefits and underestimate costs. They spin scenarios of success while overlooking the potential for mistakes and miscalculations. As a result, they pursue initiatives that are unlikely to come in on budget or on time or to deliver the expected returns—or even to be completed. In this view, people often (but not always) take on risky projects because they are overly optimistic about the odds they face. I will return to this idea several times in this book—it probably contributes to an explanation of why people litigate, why they start wars, and why they open small businesses.

But do you know what happened during this period? Where do we begin ... 1.3 million Americans died while fighting nine major wars. Roughly 99.9% of all companies that were created went out of business. Four U.S. presidents were assassinated. 675,000 Americans died in a single year from a flu pandemic. 30 separate natural disasters killed at least 400 Americans each. 33 recessions lasted a cumulative 48 years. The number of forecasters who predicted any of those recessions rounds to zero. The stock market fell more than 10% from a recent high at least 102 times. Stocks lost a third of their value at least 12 times. Annual inflation exceeded 7% in 20 separate years. The words “economic pessimism” appeared in newspapers at least 29,000 times, according to Google.

This should not come as a surprise: overly optimistic forecasts of the outcome of projects are found everywhere. Amos and I coined the term planning fallacy to describe plans and forecasts that are unrealistically close to best-case scenarios could be improved by consulting the statistics of similar cases Examples of the planning fallacy abound in the experiences of individuals, governments, and businesses.

As Paul Saffo, a forecaster of large-scale change at Discern Analytics, observes wisely, 'Change is never linear. Our expectations are linear, but new technologies come in S curves, so we routinely overestimate short-term change and underestimate long-term change.' Never mistake a clear view for a short distance, he adds.

As CEO, you should have an opinion on absolutely everything. You should have an opinion on every forecast, every product plan, every presentation, and even every comment. Let people know what you think. If you like someone’s comment, give her the feedback. If you disagree, give her the feedback. Say what you think. Express yourself. This will have two critically important positive effects:   Feedback won’t be personal in your company. If the CEO constantly gives feedback, then everyone she interacts with will just get used to it. Nobody will think, “Gee, what did she really mean by that comment? Does she not like me?” Everybody will naturally focus on the issues, not an implicit random performance evaluation.   People will become comfortable discussing bad news. If people get comfortable talking about what each other are doing wrong, then it will be very easy to talk about what the company is doing wrong. High-quality company cultures get their cue from data networking routing protocols: Bad news travels fast and good news travels slowly. Low-quality company cultures take on the personality of the Wicked Witch of the West in The Wiz: “Don’t nobody bring me no bad news.

you can’t have an effective bonus program if you don’t have a forecasting system that’s taking people forward to where they want

Those who need leaders are not qualified to choose them. In a sense, asking people to make political decisions is like asking them to forecast the weather. They’re not in a position to do so, and it is silly to expect them to. Democracy entails people who run their businesses well being forced to run their businesses poorly by people who can’t run businesses at all.

Over time, managers and executives began using statistics and analysis to forecast the future, relying on databases and spreadsheets in much the same way ancient seers relied on tea leaves and goat entrails.

Over time, managers and executives began using statistics and analysis to forecast the future, relying on databases and spreadsheets in much the same way ancient seers relied on tea leaves and goat entrails. The

Without action, forecasts and decisions about the future are not worth the paper they are written on. A decision that does not result in action is a poor one. The pace of business demands timely as well as informed decision making.

But the portion of the forecasting I care the most about is the direction given on future gross margins, because that can be a true indicator of what the business can earn in the future. The gross margin guidance is what will be used to try to figure out next quarter’s earnings estimates. That will set the benchmark that has to be beaten next time.

Over the past 30 years, approximately 300 million people have moved into China’s middle class. And according to the OECD Development Centre, the forecast is for another 200 million people to move into the middle class by 2026. This means the Asia Pacific region, which in 2009 represented 18% of the world’s middle class, will reach 66 percent by 2030. Let’s repeat that. Over the next 15 years, Asia will go from 20 percent to 66 percent of the world’s middle class. At the same time, the developed markets of North America and Europe, which held a combined 54 percent of the global middle class in 2009, are forecast to drop to only 21 percent by 2030. Basically, follow the money. Asia’s middle class consumers are the future. Learn Mandarin.

My businessman friend Dudley Wright saw the drawing and I told him the story about it. He said, “You oughta triple its price. With art, nobody is really sure of its value, so people often think, ‘If the price is higher, it must be more valuable!’” I said, “You’re crazy!” but, just for fun, I bought a twenty-dollar frame and mounted the drawing so it would be ready for the next customer. Some guy from the weather forecasting business saw the drawing I had given Gianonni and asked if I had others. I invited him and his wife to my “studio” downstairs in my home, and they asked about the newly framed drawing. “That one is two hundred dollars.” (I had multiplied sixty by three and added twenty for the frame.) The next day they came back and bought it. So the massage parlor drawing ended up in the office of a weather forecaster.

while we professors, lawyers, physicians, agronomists, artisans, instead of busying ourselves with books, legal papers, diagnoses, weather forecasts, machine parts, were making piles of bricks that we would be ordered to unpile the day following, that verse from Exodus came into my mind, the verse in which the children of Israel are forced to bake bricks for Pharaoh of Egypt to build the treasure cities of Pithom and Raamses.

More often forecasts are made and then…nothing. Accuracy is seldom determined after the fact and is almost never done with sufficient regularity and rigor that conclusions can be drawn. The reason? Mostly it’s a demand-side problem: The consumers of forecasting—governments, business, and the public—don’t demand evidence of accuracy. So there is no measurement. Which means no revision. And without revision, there can be no improvement.

I have found it frustrating at times that so few people know what the space program does and, as a result, are unaware that they benefit from it. Many people object to “wasting money in space” yet have no idea how much is actually spent on space exploration. The CSA’s budget, for instance, is less than the amount Canadians spend on Halloween candy every year, and most of it goes toward things like developing telecommunications satellites and radar systems to provide data for weather and air quality forecasts, environmental monitoring and climate change studies. Similarly, NASA’s budget is not spent in space but right here on Earth, where it’s invested in American businesses and universities, and where it also pays dividends, creating new jobs, new technologies and even whole new industries. The

Basically, Graham breaks the art of investing down into two simple variables – price and value. Value is what a business is worth. Price is what you have to pay to get it. Given the stock market’s manic-depressive behavior, numerous occasions arise where a business’ market price is distinctly out of line with its true business value. In such instances, an investor may be able to purchase a dollar of value for just 50 cents. Note that there is no mention here of interest rates, economic forecasts, technical charts, market cycles, etc. The only issues are price and value. I should also note that Graham emphasizes a large margin of safety. The strategy is not to buy a dollar of value for 97 cents. Rather, the gap should be dramatic so as to absorb the effects of miscalculation and worse-than-average luck.

Short-termism also dominates in the marketplace. The market uses a discount rate of 10% per year (or more) when comparing costs now with benefits in the future. This means that a benefit that lies twenty years ahead will be valued at one-tenth of its real value. In other words, a problem twenty years in the future will be worth solving only if the cost of the solution is less than one-tenth of the value saved. It comes as no surprise to those who know economics that it is “cost efficient” to allow the world to collapse from climate damage, as long as the collapse is more than forty years into the future. The net present value of reducing emissions and saving the world is lower than the net present value of business as usual. It is cheaper to push the world over the cliff than to try to save it. The political world is not much better, given the short tenure of political appointments. Politicians can rarely spend time on agendas that yield a positive result only after the next election—which is normally less than four years away.

For years the financial services have been making stock-market forecasts without anyone taking this activity very seriously. Like everyone else in the field they are sometimes right and sometimes wrong. Wherever possible they hedge their opinions so as to avoid the risk of being proved completely wrong. (There is a well-developed art of Delphic phrasing that adjusts itself successfully to whatever the future brings.) In our view—perhaps a prejudiced one—this segment of their work has no real significance except for the light it throws on human nature in the securities markets. Nearly everyone interested in common stocks wants to be told by someone else what he thinks the market is going to do. The demand being there, it must be supplied. Their interpretations and forecasts of business conditions, of course, are much more authoritative and informing. These are an important part of the great body of economic intelligence which is spread continuously among buyers and sellers of securities and tends to create fairly rational prices for stocks and bonds under most circumstances. Undoubtedly the material published by the financial services adds to the store of information available and fortifies the investment judgment of their clients.

The cheerleaders of the new data regime rarely acknowledge the impacts of digital decision-making on poor and working-class people. This myopia is not shared by those lower on the economic hierarchy, who often see themselves as targets rather than beneficiaries of these systems. For example, one day in early 2000, I sat talking to a young mother on welfare about her experiences with technology. When our conversation turned to EBT cards, Dorothy Allen said, “They’re great. Except [Social Services] uses them as a tracking device.” I must have looked shocked, because she explained that her caseworker routinely looked at her purchase records. Poor women are the test subjects for surveillance technology, Dorothy told me. Then she added, “You should pay attention to what happens to us. You’re next.” Dorothy’s insight was prescient. The kind of invasive electronic scrutiny she described has become commonplace across the class spectrum today. Digital tracking and decision-making systems have become routine in policing, political forecasting, marketing, credit reporting, criminal sentencing, business management, finance, and the administration of public programs. As these systems developed in sophistication and reach, I started to hear them described as forces for control, manipulation, and punishment

A few years ago my friend Jon Brooks supplied this great illustration of skewed interpretation at work. Here’s how investors react to events when they’re feeling good about life (which usually means the market has been rising): Strong data: economy strengthening—stocks rally Weak data: Fed likely to ease—stocks rally Data as expected: low volatility—stocks rally Banks make $4 billion: business conditions favorable—stocks rally Banks lose $4 billion: bad news out of the way—stocks rally Oil spikes: growing global economy contributing to demand—stocks rally Oil drops: more purchasing power for the consumer—stocks rally Dollar plunges: great for exporters—stocks rally Dollar strengthens: great for companies that buy from abroad—stocks rally Inflation spikes: will cause assets to appreciate—stocks rally Inflation drops: improves quality of earnings—stocks rally Of course, the same behavior also applies in the opposite direction. When psychology is negative and markets have been falling for a while, everything is capable of being interpreted negatively. Strong economic data is seen as likely to make the Fed withdraw stimulus by raising interest rates, and weak data is taken to mean companies will have trouble meeting earnings forecasts. In other words, it’s not the data or events; it’s the interpretation. And that fluctuates with swings in psychology.

People like Darlene who are particularly good at managing their attention tend to share certain characteristics. One is a propensity to create pictures in their minds of what they expect to see. These people tell themselves stories about what’s going on as it occurs. They narrate their own experiences within their heads. They are more likely to answer questions with anecdotes rather than simple responses. They say when they daydream, they’re often imagining future conversations. They visualize their days with more specificity than the rest of us do. Psychologists have a phrase for this kind of habitual forecasting: “creating mental models.” Understanding how people build mental models has become one of the most important topics in cognitive psychology. All people rely on mental models to some degree. We all tell ourselves stories about how the world works, whether we realize we’re doing it or not. But some of us build more robust models than others. We envision the conversations we’re going to have with more specificity, and imagine what we are going to do later that day in greater detail. As a result, we’re better at choosing where to focus and what to ignore. The secret of people like Darlene is that they are in the habit of telling themselves stories all the time. They engage in constant forecasting. They daydream about the future and then, when life clashes with their imagination, their attention gets snagged. That helps explain why Darlene noticed the sick baby. She was in the habit of imagining what the babies in her unit ought to look like. Then, when she glanced over and the bloody Band-Aid, distended belly, and mottled skin didn’t match the image in her mind, the spotlight in her head swung toward the child’s bassinet. Cognitive tunneling and reactive thinking occur when our mental spotlights go from dim to bright in a split second. But if we are constantly telling ourselves stories and creating mental pictures, that beam never fully powers down. It’s always jumping around inside our heads. And, as a result, when it has to flare to life in the real world, we’re not blinded by its glare.

Was this luck, or was it more than that? Proving skill is difficult in venture investing because, as we have seen, it hinges on subjective judgment calls rather than objective or quantifiable metrics. If a distressed-debt hedge fund hires analysts and lawyers to scrutinize a bankrupt firm, it can learn precisely which bond is backed by which piece of collateral, and it can foresee how the bankruptcy judge is likely to rule; its profits are not lucky. Likewise, if an algorithmic hedge fund hires astrophysicists to look for patterns in markets, it may discover statistical signals that are reliably profitable. But when Perkins backed Tandem and Genentech, or when Valentine backed Atari, they could not muster the same certainty. They were investing in human founders with human combinations of brilliance and weakness. They were dealing with products and manufacturing processes that were untested and complex; they faced competitors whose behaviors could not be forecast; they were investing over long horizons. In consequence, quantifiable risks were multiplied by unquantifiable uncertainties; there were known unknowns and unknown unknowns; the bracing unpredictability of life could not be masked by neat financial models. Of course, in this environment, luck played its part. Kleiner Perkins lost money on six of the fourteen investments in its first fund. Its methods were not as fail-safe as Tandem’s computers. But Perkins and Valentine were not merely lucky. Just as Arthur Rock embraced methods and attitudes that put him ahead of ARD and the Small Business Investment Companies in the 1960s, so the leading figures of the 1970s had an edge over their competitors. Perkins and Valentine had been managers at leading Valley companies; they knew how to be hands-on; and their contributions to the success of their portfolio companies were obvious. It was Perkins who brought in the early consultants to eliminate the white-hot risks at Tandem, and Perkins who pressed Swanson to contract Genentech’s research out to existing laboratories. Similarly, it was Valentine who drove Atari to focus on Home Pong and to ally itself with Sears, and Valentine who arranged for Warner Communications to buy the company. Early risk elimination plus stage-by-stage financing worked wonders for all three companies. Skeptical observers have sometimes asked whether venture capitalists create innovation or whether they merely show up for it. In the case of Don Valentine and Tom Perkins, there was not much passive showing up. By force of character and intellect, they stamped their will on their portfolio companies.

I have an-odd ability-to read very quickly.” “Oh,” Elizabeth replied, “how lucky you are. I never heard of a talent like that.” A lazy glamorous smile swept across his face, and he squeezed her hand. “It’s not nearly as uncommon as your eyes,” he said. Elizabeth thought it must be a great deal more uncommon, but she wasn’t completely certain and she let it pass. The following day, that discovery was completely eclipsed by another one. At Ian’s insistence, she’d spread the books from Havenhurst across his desk in order to go over the quarter’s accounts, and as the morning wore on, the long columns of figures she’d been adding and multiplying began to blur together and transpose themselves in her mind-due in part, she thought with a weary smile, to the fact that her husband had kept her awake half the night making love to her. For the third time, she added the same long columns of expenditures, and for the third time, she came up with a different sum. So frustrated was she that she didn’t realize Ian had come into the room, until he leaned over her from behind and put his hands on the desk on either side of her own. “Problems?” he asked, kissing the top of her head. “Yes,” she said, glancing at the clock and realizing that the business acquaintances he was expecting would be there momentarily. As she explained her problem to him, she started shoving loose papers into the books, hurriedly trying to reassemble everything and clear his desk. “For the last forty-five minutes, I’ve been adding the same four columns, so that I could divide them by eighteen servants, multiply that by forty servants which we now have there, times four quarters. Once I know that, I can forecast the real cost of food and supplies with the increased staff. I’ve gotten three different answers to those miserable columns, and I haven’t even tried the rest of the calculations. Tomorrow I’ll have to start all over again,” she finished irritably, “and it takes forever just to get all this laid out and organized.” She reached out to close the book and shove her calculations into it, but Ian stopped her. “Which columns are they?” he asked calmly, his surprised gaze studying the genuine ire on her face. “Those long ones down the left-hand side. It doesn’t matter, I’ll fight it out tomorrow,” she said. She shoved the chair back, dropped two sheets of paper, and bent over to pick them up. They’d slid beneath the kneehole of the desk, and in growing disgust Elizabeth crawled underneath to get them. Above her, Ian said, “$364.” “Pardon?” she asked when she reemerged, clutching the errant sheets of paper. He was writing it down on a scrap of paper. “$364.” “Do not make light of my wanting to know the figures,” she warned him with an exasperated smile. “Besides,” she continued, leaning up and pressing an apologetic kiss on his cheek, loving the tangy scent of his cologne, “I usually enjoy the bookwork. I’m simply a little short of sleep today, because,” she whispered, “my husband kept me awake half the night.” “Elizabeth,” he began hesitantly, “there’s something I-“ Then he shook his head and changed his mind, and since Shipley was already standing in the doorway to announce the arrival of his business acquaintances, Elizabeth thought no more of it. Until the next morning.

SELF-MANAGEMENT Trust We relate to one another with an assumption of positive intent. Until we are proven wrong, trusting co-workers is our default means of engagement. Freedom and accountability are two sides of the same coin. Information and decision-making All business information is open to all. Every one of us is able to handle difficult and sensitive news. We believe in collective intelligence. Nobody is as smart as everybody. Therefore all decisions will be made with the advice process. Responsibility and accountability We each have full responsibility for the organization. If we sense that something needs to happen, we have a duty to address it. It’s not acceptable to limit our concern to the remit of our roles. Everyone must be comfortable with holding others accountable to their commitments through feedback and respectful confrontation. WHOLENESS Equal worth We are all of fundamental equal worth. At the same time, our community will be richest if we let all members contribute in their distinctive way, appreciating the differences in roles, education, backgrounds, interests, skills, characters, points of view, and so on. Safe and caring workplace Any situation can be approached from fear and separation, or from love and connection. We choose love and connection. We strive to create emotionally and spiritually safe environments, where each of us can behave authentically. We honor the moods of … [love, care, recognition, gratitude, curiosity, fun, playfulness …]. We are comfortable with vocabulary like care, love, service, purpose, soul … in the workplace. Overcoming separation We aim to have a workplace where we can honor all parts of us: the cognitive, physical, emotional, and spiritual; the rational and the intuitive; the feminine and the masculine. We recognize that we are all deeply interconnected, part of a bigger whole that includes nature and all forms of life. Learning Every problem is an invitation to learn and grow. We will always be learners. We have never arrived. Failure is always a possibility if we strive boldly for our purpose. We discuss our failures openly and learn from them. Hiding or neglecting to learn from failure is unacceptable. Feedback and respectful confrontation are gifts we share to help one another grow. We focus on strengths more than weaknesses, on opportunities more than problems. Relationships and conflict It’s impossible to change other people. We can only change ourselves. We take ownership for our thoughts, beliefs, words, and actions. We don’t spread rumors. We don’t talk behind someone’s back. We resolve disagreements one-on-one and don’t drag other people into the problem. We don’t blame problems on others. When we feel like blaming, we take it as an invitation to reflect on how we might be part of the problem (and the solution). PURPOSE Collective purpose We view the organization as having a soul and purpose of its own. We try to listen in to where the organization wants to go and beware of forcing a direction onto it. Individual purpose We have a duty to ourselves and to the organization to inquire into our personal sense of calling to see if and how it resonates with the organization’s purpose. We try to imbue our roles with our souls, not our egos. Planning the future Trying to predict and control the future is futile. We make forecasts only when a specific decision requires us to do so. Everything will unfold with more grace if we stop trying to control and instead choose to simply sense and respond. Profit In the long run, there are no trade-offs between purpose and profits. If we focus on purpose, profits will follow.

EARNINGS McDonald's Plans Marketing Push as Profit Slides By Julie Jargon | 436 words Associated Press The burger giant has been struggling to maintain relevance among younger consumers and fill orders quickly in kitchens that have grown overwhelmed with menu items. McDonald's Corp. plans a marketing push to emphasize its fresh-cooked breakfasts as it battles growing competition for the morning meal. Competition at breakfast has heated up recently as Yum Brands Inc.'s Taco Bell entered the business with its new Waffle Taco last month and other rivals have added or discounted breakfast items. McDonald's Chief Executive Don Thompson said it hasn't yet noticed an impact from Taco Bell's breakfast debut, but that the overall increased competition "forces us to focus even more on being aggressive in breakfast." Mr. Thompson's comments came after McDonald's on Tuesday reported that its profit for the first three months of 2014 dropped 5.2% from a year earlier, weaker than analysts' expectations. Comparable sales at U.S. restaurants open more than a year declined 1.7% for the quarter and 0.6% for March, the fifth straight month of declines in the company's biggest market. Global same-store sales rose 0.5% for both the quarter and month. Mr. Thompson acknowledged again that the company has lost relevance with some customers and needs to strengthen its menu offerings. He emphasized Tuesday that McDonald's is focused on stabilizing key markets, including the U.S., Germany, Australia and Japan. The CEO said McDonald's has dominated the fast-food breakfast business for 35 years, and "we don't plan on giving that up." The company plans in upcoming ads to inform customers that it cooks its breakfast, unlike some rivals. "We crack fresh eggs, grill sausage and bacon," Mr. Thompson said. "This is not a microwave deal." Beyond breakfast, McDonald's also plans to boost marketing of core menu items such as Big Macs and french fries, since those core products make up 40% of total sales. To serve customers more quickly, the chain is working to optimize staffing, and is adding new prep tables that let workers more efficiently add new toppings when guests want to customize orders. McDonald's also said it aims to sell more company-owned restaurants outside the U.S. to franchisees. Currently, 81% of its restaurants around the world are franchised. Collecting royalties from franchisees provides a stable source of income for a restaurant company and removes the cost of operating them. McDonald's reported a first-quarter profit of $1.2 billion, or $1.21 a share, down from $1.27 billion, or $1.26 a share, a year earlier. The company partly attributed the decline to the effect of income-tax benefits in the prior year. Total revenue for the quarter edged up 1.4% to $6.7 billion, though costs rose faster, at 2.3%. Analysts polled by Thomson Reuters forecast earnings of $1.24 a share on revenue of $6.72 billion.

computer programs to forecast listeners’ habits. A company named Polyphonic HMI—a collection of artificial intelligence experts and statisticians based in Spain—had created a program called Hit Song Science that analyzed the mathematical characteristics of a tune and predicted its popularity. By comparing the tempo, pitch, melody, chord progression, and other factors of a particular song against the thousands of hits stored in Polyphonic HMI’s database, Hit Song Science could deliver a score that forecasted if a tune was likely to succeed.7.14

Lucrezia Reichlin, co-founder of Now-Casting, a forecaster that tracks economies, and a professor at the London Business School, said the recovery had weakened since the spring. “There is geopolitical risk. But China is also slowing down and the fiscal and monetary stance in the eurozone has been very restrictive,” she said.

Dear KDP Author, Just ahead of World War II, there was a radical invention that shook the foundations of book publishing. It was the paperback book. This was a time when movie tickets cost 10 or 20 cents, and books cost $2.50. The new paperback cost 25 cents – it was ten times cheaper. Readers loved the paperback and millions of copies were sold in just the first year. With it being so inexpensive and with so many more people able to afford to buy and read books, you would think the literary establishment of the day would have celebrated the invention of the paperback, yes? Nope. Instead, they dug in and circled the wagons. They believed low cost paperbacks would destroy literary culture and harm the industry (not to mention their own bank accounts). Many bookstores refused to stock them, and the early paperback publishers had to use unconventional methods of distribution – places like newsstands and drugstores. The famous author George Orwell came out publicly and said about the new paperback format, if “publishers had any sense, they would combine against them and suppress them.” Yes, George Orwell was suggesting collusion. Well… history doesn’t repeat itself, but it does rhyme. Fast forward to today, and it’s the e-book’s turn to be opposed by the literary establishment. Amazon and Hachette – a big US publisher and part of a $10 billion media conglomerate – are in the middle of a business dispute about e-books. We want lower e-book prices. Hachette does not. Many e-books are being released at $14.99 and even $19.99. That is unjustifiably high for an e-book. With an e-book, there’s no printing, no over-printing, no need to forecast, no returns, no lost sales due to out of stock, no warehousing costs, no transportation costs, and there is no secondary market – e-books cannot be resold as used books. E-books can and should be less expensive. Perhaps channeling Orwell’s decades old suggestion, Hachette has already been caught illegally colluding with its competitors to raise e-book prices. So far those parties have paid $166 million in penalties and restitution. Colluding with its competitors to raise prices wasn’t only illegal, it was also highly disrespectful to Hachette’s readers. The fact is many established incumbents in the industry have taken the position that lower e-book prices will “devalue books” and hurt “Arts and Letters.” They’re wrong. Just as paperbacks did not destroy book culture despite being ten times cheaper, neither will e-books. On the contrary, paperbacks ended up rejuvenating the book industry and making it stronger. The same will happen with e-books. Many inside the echo-chamber of the industry often draw the box too small. They think books only compete against books. But in reality, books compete against mobile games, television, movies, Facebook, blogs, free news sites and more. If we want a healthy reading culture, we have to work hard to be sure books actually are competitive against these other media types, and a big part of that is working hard to make books less expensive. Moreover, e-books are highly price elastic. This means that when the price goes down, customers buy much more. We've quantified the price elasticity of e-books from repeated measurements across many titles. For every copy an e-book would sell at $14.99, it would sell 1.74 copies if priced at $9.99. So, for example, if customers would buy 100,000 copies of a particular e-book at $14.99, then customers would buy 174,000 copies of that same e-book at $9.99. Total revenue at $14.99 would be $1,499,000. Total revenue at $9.99 is $1,738,000. The important thing to note here is that the lower price is good for all parties involved: the customer is paying 33% less and the author is getting a royalty check 16% larger and being read by an audience that’s 74% larger. The pie is simply bigger.

Establishment of a quarterly rolling planning regime, wherein management both sets out its revenue and expenditure requirements for the next 18 months and seeks approval for expenditure planned for the next 3 months, is a key requirement for beyond budgeting management. Each quarter, before approving these estimates, management sees the bigger picture six quarters out. While firming up the short-term numbers for the next three months, all subsequent forecasts also update the annual forecast. Budget holders are encouraged to spend half the time on getting the details of the next three months right, as these will become targets, on agreement, and the rest of the time on the next five quarters. Each quarter

Fourth, even these aggregate targets are mainly labeled as “forecasts” (预期性) rather than “mandatory” (约束性).

Proformas rarely perform; missed projections are more often the norm. Still, we skew them up high, we miss but we try, for proformas which rarely perform.

do is make the revenue and profit projections rise 5 to 10 percent per year. If this seems simplistic or silly, just look around for a company that forecasts it will grow 7 percent, then drop 4 percent, then merge with a competitor, then rise 8 percent, and then fall another 11 percent. I’ve never seen a business forecast like that, even though that’s how most end up. They all show their numbers getting bigger every year, rendering the exercise useless.

After our IPO in January 1997, we had to get better at predicting our numbers. … The market penalized us when we missed one quarter in ‘99 after we adopted a new manufacturing system. We said, “Look, we can’t predict what’s going on in the economy, and we have no idea what our orders will look like a year from now. … We don’t run this business by the numbers. The numbers will be doing what the numbers will be doing; we can just give you a good picture of what the next quarter will bring. So, we got away from making annual projections and started just doing quarterly forecasts. … We know our performance in the long run will be a result of just doing the right things every day.115

As CEO, you should have an opinion on absolutely everything. You should have an opinion on every forecast, every product plan, every presentation, and even every comment. Let people know what you think. If you like someone’s comment, give her the feedback. If you disagree, give her the feedback. Say what you think. Express yourself.

Perhaps an analogy will clarify the point. Let us suppose that a sect should arise promulgating the view that J. R. R. Tolkien’s The Lord of the Rings was actually an inspired prophecy forecasting political events that must occur before the year 2000. The members of the sect would then busy themselves in attempting to work out the symbolic correlations between the story’s characters and the dramatis personae of the world’s political stage in the 1990s. Some of the proposals might be enormously detailed and ingenious, but surely we would have to say to such interpreters, “No, you’ve got it all wrong; The Lord of the Rings is not that sort of text at all.” Something similar must be said to those who read Revelation as predictive in this way.

Larry Kudlow hosted a business talk show on CNBC and is a widely published pundit, but he got his start as an economist in the Reagan administration and later worked with Art Laffer, the economist whose theories were the cornerstone of Ronald Reagan’s economic policies. Kudlow’s one Big Idea is supply-side economics. When President George W. Bush followed the supply-side prescription by enacting substantial tax cuts, Kudlow was certain an economic boom of equal magnitude would follow. He dubbed it “the Bush boom.” Reality fell short: growth and job creation were positive but somewhat disappointing relative to the long-term average and particularly in comparison to that of the Clinton era, which began with a substantial tax hike. But Kudlow stuck to his guns and insisted, year after year, that the “Bush boom” was happening as forecast, even if commentators hadn’t noticed. He called it “the biggest story never told.” In December 2007, months after the first rumblings of the financial crisis had been felt, the economy looked shaky, and many observers worried a recession was coming, or had even arrived, Kudlow was optimistic. “There is no recession,” he wrote. “In fact, we are about to enter the seventh consecutive year of the Bush boom.”19 The National Bureau of Economic Research later designated December 2007 as the official start of the Great Recession of 2007–9. As the months passed, the economy weakened and worries grew, but Kudlow did not budge. There is no recession and there will be no recession, he insisted. When the White House said the same in April 2008, Kudlow wrote, “President George W. Bush may turn out to be the top economic forecaster in the country.”20 Through the spring and into summer, the economy worsened but Kudlow denied it. “We are in a mental recession, not an actual recession,”21 he wrote, a theme he kept repeating until September 15, when Lehman Brothers filed for bankruptcy, Wall Street was thrown into chaos, the global financial system froze, and people the world over felt like passengers in a plunging jet, eyes wide, fingers digging into armrests. How could Kudlow be so consistently wrong? Like all of us, hedgehog forecasters first see things from the tip-of-your-nose perspective. That’s natural enough. But the hedgehog also “knows one big thing,” the Big Idea he uses over and over when trying to figure out what will happen next. Think of that Big Idea like a pair of glasses that the hedgehog never takes off. The hedgehog sees everything through those glasses. And they aren’t ordinary glasses. They’re green-tinted glasses—like the glasses that visitors to the Emerald City were required to wear in L. Frank Baum’s The Wonderful Wizard of Oz. Now, wearing green-tinted glasses may sometimes be helpful, in that they accentuate something real that might otherwise be overlooked. Maybe there is just a trace of green in a tablecloth that a naked eye might miss, or a subtle shade of green in running water. But far more often, green-tinted glasses distort reality. Everywhere you look, you see green, whether it’s there or not. And very often, it’s not. The Emerald City wasn’t even emerald in the fable. People only thought it was because they were forced to wear green-tinted glasses! So the hedgehog’s one Big Idea doesn’t improve his foresight. It distorts it. And more information doesn’t help because it’s all seen through the same tinted glasses. It may increase the hedgehog’s confidence, but not his accuracy. That’s a bad combination.

Set specific goals. 2. Define activities, resources needed, responsibilities. 3. Set a timetable for action. 4. Forecast outcomes, develop contingencies. 5. Formulate a detailed plan of action in time sequence. 6. Implement, supervise execution, and evaluate based on goals in step one.

Tough times brought on by the Gulf War were testing such assumptions, forcing us to consider our response. We needed to come up with new ideas, do more with less, make short-term gains through greater efficiency, and prepare for long-term gains. That meant cutting every dollar possible in overhead and procedures while maintaining or boosting spending in three vital competitive areas. Number one was product quality. World leadership demanded that we maintain world-class quality, and recession is generally a period when material and labor prices are lowest and room occupancies are down. So we renovated and refurbished at such normally busy properties as the Inn on the Park in London and The Pierre in New York at a time when revenue would be little affected and customers least inconvenienced. That meant we were spending when others were retrenching. We had followed that strategy in 1981-82, and the rebound from that recession had given us nine years of steady growth. I thought the odds were in our favor to score the same way again. The second area was marketing. It’s tempting during recession to cut back on consumer advertising. At the start of each of the last three recessions, the growth of spending on such advertising had slowed by an average of 27 percent. But consumer studies of those recessions had showed that companies that didn’t cut their ads had, in the recovery, captured the most market share. So we didn’t cut our ad budget. In fact, we raised it modestly to gain brand recognition, which continued advertising sustains. As studies show, it’s much easier to sustain momentum than restart it. Third, we eased the workload and reduced costs by simplifying reporting methods. We set up a new system that allowed each hotel to recalculate its forecast, with minimal input, to year’s end, then send it in electronically along with a brief monthly commentary.

Threadless is a T-shirt company founded by people with expertise in information technology services, web design, and consulting. Their business model involves holding weekly design contests open to outside participants, printing only T-shirts with the most popular designs, and selling them to their large and growing customer base. Threadless doesn’t need to hire artistic talent, since skilled designers compete for prizes and prestige. It doesn’t need to do marketing, since eager designers contact their friends to solicit votes and sales. It doesn’t need to forecast sales, since voting customers have already announced what numbers they will buy. By outsourcing production, Threadless can also minimize its handling and inventory costs. Thanks to this almost frictionless model, Threadless can scale rapidly and easily, with minimal structural restrictions.

The largest, longest study of experts’ economic forecasts was performed by Philip Tetlock, a professor at the Haas Business School of the University of California–Berkeley. He studied 82,000 predictions over 25 years by 300 selected experts. Tetlock concludes that expert predictions barely beat random guesses. Ironically, the more famous the expert, the less accurate his or her predictions tended to be.

Futurists who are thinking about the businesses of the future forecast that many more of us will become entrepreneurs. They see employee healthcare and financial benefits, pension plans and retirement packages, all disappearing in the future for most employees of most companies. Everybody’s going to be a free agent, and everybody’s going to be an entrepreneur. You’re going to broker your skills and negotiate your own contracts for everything. Now it may not reach 100% of companies, but it certainly is an interesting future to think about, and it’s an interesting concept to be aware of on the path to becoming an entrepreneur.

old management methods are not up to the task. Planning and forecasting

The only thing that we know about financial predictions of startups is that 100 percent of them are wrong. If you can predict the future accurately, we have a few suggestions for other things you could be doing besides starting a risky early stage company. Furthermore, the earlier stage the startup, the less accurate any predications will be. While we know you can't predict your revenue with any degree of accuracy (although we are always very pleased in that rare case where revenue starts earlier and grows faster than expected), the expense side of your financial plan is very instructive as to how you think about the business. You can't predict your revenue with any level of precision, but you should be able to manage your expenses exactly to plan. Your financials will mean different things to different investors. In our case, we focus on two things: (1) the assumptions underlying the revenue forecast (which we don't need a spreadsheet for—we'd rather just talk about them) and (2) the monthly burn rate or cash consumption of the business. Since your revenue forecast will be wrong, your cash flow forecast will be wrong. However, if you are an effective manager, you'll know how to budget for this by focusing on lagging your increase in cash spend behind your expected growth in revenue.

KEY POINT: If you don’t stabilize a sales forecast, you can’t control your company. If you control a forecast, you control the world.

Planning and forecasting are only accurate when based on a long, stable operating history and a relatively static environment. Startups have neither.

You must be able to forecast the Flow. You must have a Flow Plan that helps you gain a clear vision of what’s out there next month and the month after that.

This chart contrasts predictive and prospective thinking: Predictive Thinking Prospective Thinking Mindset Forecasting, “We expect …” Preparing, “But what if …” Goal Reduce or even discard uncertainty, fight ambiguity Live with uncertainty, embrace ambiguity, plan for set of contingencies Level of uncertainty Average High Method Extrapolating from present and past Open, imaginative Approach Categorical, assumes continuity Global, systemic, anticipates disruptive events Information inputs Quantitative, objective, known Qualitative (whether quantifiable or not), subjective, known or unknown Relationships Static, stable structures Dynamic, evolving structures Technique Established quantitative models (economics, mathematics, data) Developing scenarios using qualitative approaches (often building on megatrends) Evaluation method Numbers Criteria

This chart contrasts predictive and prospective thinking: Predictive Thinking Prospective Thinking Mindset Forecasting, “We expect …” Preparing, “But what if …” Goal Reduce or even discard uncertainty, fight ambiguity Live with uncertainty, embrace ambiguity, plan for set of contingencies Level of uncertainty Average High Method Extrapolating from present and past Open, imaginative Approach Categorical, assumes continuity Global, systemic, anticipates disruptive events Information inputs Quantitative, objective, known Qualitative (whether quantifiable or not), subjective, known or unknown Relationships Static, stable structures Dynamic, evolving structures Technique Established quantitative models (economics, mathematics, data) Developing scenarios using qualitative approaches (often building on megatrends) Evaluation method Numbers Criteria Attitude toward the future Passive or reactive (the future will be) Proactive and creative (we create or shape the future) Way of thinking Generally deduction Greater use of induction

nowhere has the stagger chart been more productive than in forecasting economic trends. The way it works is shown in the figure below, which gives us forecasted rates of incoming orders for an Intel division. The stagger chart then provides the same forecast prepared in the following month, in the month after that, and so on. Such a chart shows not only your outlook for business month by month but also how your outlook varied from one month to the next. This way of looking at incoming business, of course, makes whoever does the forecasting take his task very seriously, because he knows that his forecast for any given month will be routinely compared with future forecasts and eventually with the actual result. But even more important, the improvement or deterioration of the forecasted outlook from one month to the next provides the most valuable indicator of business trends that I have ever seen.

1. Find the Fringe: Cast a wide enough net to harness information from the fringe. This involves creating a map showing nodes and the relationships between them, and rounding up what you will later refer to as “the unusual suspects.” 2. Use CIPHER: Uncover hidden patterns by categorizing data from the fringe. Patterns indicate a trend, so you’ll do an exhaustive search for Contradictions, Inflections, Practices, Hacks, Extremes, and Rarities. 3. Ask the Right Questions: Determine whether a pattern really is a trend. You will be tempted to stop looking once you’ve spotted a pattern, but you will soon learn that creating counterarguments is an essential part of the forecasting process, even though most forecasters never force themselves to poke holes into every single assumption and assertion they make. 4. Calculate the ETA: Interpret the trend and ensure that the timing is right. This isn’t just about finding a typical S-curve and the point of inflection. As technology trends move along their trajectory, there are two forces in play—internal developments within tech companies, and external developments within the government, adjacent businesses, and the like—and both must be calculated. 5. Create Scenarios and Strategies: Build scenarios to create probable, plausible, and possible futures and accompanying strategies. This step requires thinking about both the timeline of a technology’s development and your emotional reactions to all of the outcomes. You’ll give each scenario a score, and based on your analysis, you will create a corresponding strategy for taking action. 6. Pressure-Test Your Action: But what if the action you choose to take on a trend is the wrong one? In this final step, you must make sure the strategy you take on a trend will deliver the desired outcome, and that requires asking difficult questions about both the present and the future.

Many of our most important decisions are, in fact, attempts to forecast the future.

Predictions - that is, forecasts of what will happen - are usually wrong. Projections - estimates of future possibilities - help us imagine the future and think out what actions to take if it came about.

In any case, during the same month reassurance came from still higher authority. Andrew W. Mellon said, “There is no cause for worry. The high tide of prosperity will continue.” Mr. Mellon did not know. Neither did any of the other public figures who then, as since, made similar statements. These are not forecasts; it is not to be supposed that the men who make them are privileged to look farther into the future than the rest. Mr. Mellon was participating in a ritual which, in our society, is thought to be of great value for influencing the course of the business cycle. By affirming solemnly that prosperity will continue, it is believed, one can help insure that prosperity will in fact continue. Especially among businessmen the faith in the efficiency of such incantation is very great.

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

Where does scientific knowledge come from? You know the process. A good scientist pushes to the edge of knowledge and then reaches beyond, forming a conjecture—a hypothesis—about how things work in that unknown territory. If the scientist avoids the edge, working with what is already well known and established, life will be comfortable, but there will be neither fame nor honor. In the same way, a good business strategy deals with the edge between the known and the unknown. Again, it is competition with others that pushes us to edges of knowledge. Only there are found the opportunities to keep ahead of rivals. There is no avoiding it. That uneasy sense of ambiguity you feel is real. It is the scent of opportunity. In science, you first test a new conjecture against known laws and experience. Is the new hypothesis contradicted by basic principles or by the results of past experiments? If the hypothesis survives that test, the scientist has to devise a real-world test—an experiment—to see how well the hypothesis stands up. Similarly, we test a new strategic insight against well-established principles and against our accumulated knowledge about the business. If it passes those hurdles, we are faced with trying it out and seeing what happens. Given that we are working on the edge, asking for a strategy that is guaranteed to work is like asking a scientist for a hypothesis that is guaranteed to be true—it is a dumb request. The problem of coming up with a good strategy has the same logical structure as the problem of coming up with a good scientific hypothesis. The key differences are that most scientific knowledge is broadly shared, whereas you are working with accumulated wisdom about your business and your industry that is unlike anyone else’s. A good strategy is, in the end, a hypothesis about what will work. Not a wild theory, but an educated judgment. And there isn’t anyone more educated about your businesses than the group in this room. This concept breaks the impasse. After some discussion, the group begins to work with the notion that a strategy is a hypothesis—an educated guess—about what will work. After a while, Barry starts to articulate his own judgments, saying “I think in my business we can …” When engineers use a nice clean deductive system to solve a problem, they call it winding the crank. By this they mean that it may be hard work, but that the nature and quality of the output depends on the machine (the chosen system of deduction), not on the skill of the crank winder. Later, looking back, I realize the group had expected strategy to be an exercise in crank winding. They had expected that I would give them a “logical machine” that they could use to deduce business plans—a system for generating forecasts and actions.

many on business conditions for the Saturday Evening Post.80 He developed a close working relationship with the Post’s editor, George Lorimer, and in article after article predicted the future of America’s industries:

Entrepreneurs don’t seem to believe that forecasting is worth the bother: One survey found that 60% of Inc. 500 CEOs had not even written business plans before launching their companies. To

Camden in the winter of 1954 was a bleak place. It is difficult to see it this way if you’ve only been there in the summer, but most of Maine can be dismal, especially along the coast, during the long nights and short days. Once the colorful leaves have fallen from the majestic maple trees, and the last tourist has gone home, things become grim. So it was, during that cold January day, when I was on the road hoping to get a ride to New Jersey. On the radio, the weather forecasters predicted an overnight blizzard, but here it was only late afternoon and snow was already accumulating on the road. This would be my last opportunity to get home to see my family and friends, before cruising back on down to the Caribbean. I had really hoped to get an earlier start, to get far enough south to miss the brunt of the storm. Maine is known for this kind of weather, and the snowplows and sanders were ready. In fact, I didn’t see many other vehicles on the road any longer. Schools had let out early and most businesses were closed in anticipation of the storm. My last ride dropped me off in Belfast, telling me that he was trying to get as far as Augusta, before State Road 3 became impassable. Standing alongside the two-lane coastal highway with darkness not far off, I was half thinking that I should turn back. My mind was made up for me when I stepped back off the road, making room for a big State DOT dump truck with a huge yellow snowplow. His airbrakes wheezed as he braked, coming to a stop, at the same time lifting his plow to keep from burying me. The driver couldn’t believe that I was out hitchhiking in a blizzard. This kind of weather in Maine is no joke! The driver told me that the year before a body had been found under a snow bank during the spring thaw. Never mind, I was invincible and nothing like that could happen to me, or so I thought. He got me as far as Camden and suggested that I get a room. “This storm is only going to get worse,” he cautioned as I got off. I waved as he drove off. Nevertheless, still hoping that things would improve, I was determined to continue…

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One interview technique that I’d used to sort the good from the bad was to ask a series of questions about hiring, training, and managing sales reps. Typically, it would go like this: Ben: “What do you look for in a sales rep?” Candidate: “They need to be smart, aggressive, and competitive. They need to know how to do complex deals and navigate organizations.” Ben: “How do you test for those things in an interview?” Candidate: “Umm, well, I hire everybody out of my network.” Ben: “Okay, once you get them on board, what do you expect from them?” Candidate: “I expect them to understand and follow the sales process, I expect them to master the product, I expect them to be accurate in their forecasting. . . .” Ben: “Tell me about the training program that you designed to achieve this.” Candidate: “Umm.” They would then proceed to make something up as they went along.

As I described in the “Uncorked!” chapter, the economic background in 1970 was turning grim, and sales were weakening. I was concerned. And then, once again, Scientific American came to the rescue. Each September that wonderful magazine devotes its entire issue to a single subject. In September 1970, it was the biosphere, a term I’d never seen before. It was the first time that a major scientific journal had addressed the problem of the environment. Rachel Carson’s Silent Spring, of course, had been serialized in the New Yorker in the late sixties, so the danger to the biosphere wasn’t exactly news, but it could be considered alarmist news. The prestige of Scientific American, however, carried weight. In fact, it knocked me out. I Suffered a Conversion on the Road to Damascus Within weeks, I subscribed to The Whole Earth Catalog, all the Rodale publications like Organic Gardening and Farming, Mother Earth, and a bunch I no longer remember. I was especially impressed by Francis Moore Lappé’s book Diet for a Small Planet. I joined the board of Pasadena Planned Parenthood, where I served for six years. Paul Ehrlich surfaced with his dismal, and proved utterly wrong, predictions. But hey! This guy was from Stanford! You had to believe him! And in 1972 all this was given statistical veracity by Jay Forrester of MIT, in the Club of Rome forecasts, which proved to be even further off the mark. But I bought them at the time. Bob Hanson, the manager of the new Trader Joe’s in Santa Ana, which was off to a slow start, was a health food nut. He kept bugging me to try “health foods.” After I’d read Scientific American, I was on board! Just how eating health foods would save the biosphere was never clear in my mind, or, in my opinion, in the mind of anyone else, except the 100 percent Luddites who wanted to return to some lifestyle approximating the Stone Age. After all, the motto of the Whole Earth Catalog was “access to tools,” hardly Luddite.

Unfortunately, standard accounting is not helpful in evaluating entrepreneurs. Startups are too unpredictable for forecasts and milestones to be accurate.

Having regular check-ins to align direction is super powerful, the ability to tune and adjust reduces waste and deviation and realignment.

Markets change, visions change, technologies change, teams change, settings change, relationships change… with an ever changing environment it will be naive to think that you can draw the future with a straight line.

A forecast from SpaceWorks in 2019 further predicted 2,000–2,800 nano or microsatellites will launch in over the next five years between the military, commercial, and civil sectors. This segment of satellites already increased in launch and production by 25 percent from 2017 to 2018; in 2018, 253 of the planned 262 nanosatellites actually launched, reflecting “Greater launch consistency and better execution on the part of small satellite operators.”5 Factors like the Internet of Things (IoT), demand for communications data, and increased need for Earth observation are all helping to drive this market.

United Kingdom-based growth strategy and research firm Frost and Sullivan forecasts the smallsat launch market will generate a whopping $69 billion in revenue by 2030, with new satellites, constellations, and replacement missions accounting for nearly 12,000 launches.

In 2013, on the auspicious date of April 1, I received an email from Tetlock inviting me to join what he described as “a major new research program funded in part by Intelligence Advanced Research Projects Activity, an agency within the U.S. intelligence community.” The core of the program, which had been running since 2011, was a collection of quantifiable forecasts much like Tetlock’s long-running study. The forecasts would be of economic and geopolitical events, “real and pressing matters of the sort that concern the intelligence community—whether Greece will default, whether there will be a military strike on Iran, etc.” These forecasts took the form of a tournament with thousands of contestants; the tournament ran for four annual seasons. “You would simply log on to a website,” Tetlock’s email continued, “give your best judgment about matters you may be following anyway, and update that judgment if and when you feel it should be. When time passes and forecasts are judged, you could compare your results with those of others.” I did not participate. I told myself I was too busy; perhaps I was too much of a coward as well. But the truth is that I did not participate because, largely thanks to Tetlock’s work, I had concluded that the forecasting task was impossible. Still, more than 20,000 people embraced the idea. Some could reasonably be described as having some professional standing, with experience in intelligence analysis, think tanks, or academia. Others were pure amateurs. Tetlock and two other psychologists, Barbara Mellers (Mellers and Tetlock are married) and Don Moore, ran experiments with the cooperation of this army of volunteers. Some were given training in some basic statistical techniques (more on this in a moment); some were assembled into teams; some were given information about other forecasts; and others operated in isolation. The entire exercise was given the name Good Judgment Project, and the aim was to find better ways to see into the future. This vast project has produced a number of insights, but the most striking is that there was a select group of people whose forecasts, while they were by no means perfect, were vastly better than the dart-throwing-chimp standard reached by the typical prognosticator. What is more, they got better over time rather than fading away as their luck changed. Tetlock, with an uncharacteristic touch of hyperbole, called this group “superforecasters.” The cynics were too hasty: it is possible to see into the future after all. What makes a superforecaster? Not subject-matter expertise: professors were no better than well-informed amateurs. Nor was it a matter of intelligence; otherwise Irving Fisher would have been just fine. But there were a few common traits among the better forecasters.

If the company doesn’t hit its forecasts, cash is tied up in inventory. Cash is like blood or oxygen; without it, you die. And growth eats cash. This is why roughly half of all bankruptcies occur after a year of record sales.

Mark Allin and Richard Burton started Capstone, their book-publishing venture, with high hopes. False modesty aside, they knew they were excellent editors, with a great track record at two publishing giants. I could vouch for Mark Allin’s profit-making abilities, since he gave me the idea for writing The 80/20 Principle, my bestselling book. Richard and Mark envisaged Capstone as a star venture, the leader in a new category of ‘funky business books’. They convinced me that this idea was plausible and I became their financial backer. I reckoned that I had an ‘each-way bet’ - either their star business would materialise, or, at worst, they would pick a few great winners, making Capstone highly profitable. The business appeared to start well. They commissioned a stream of trendy books from interesting authors. The product looked great, with distinctive trendy designs. Mark and Richard were full of ideas and enthusiasm, confidently projecting sales that would give us good profits. The only thing was, the forecasts never materialised. Whenever we looked at the numbers we were constantly disappointed. I kept injecting cash, and it kept vanishing. To this day I don’t know why their books didn’t sell in quantities we could reasonably expect.The favoured explanation was the weakness of the sales force - inevitably, it was difficult to acquire distribution muscle from scratch. Maybe they just had bad luck in not commissioning any smash hits. Whatever the reason, Capstone was a financial black hole. I remember a rather difficult meeting at my home in Richmond some three years after the start. Richard and Mark asked for a further loan to commission new books. I had to say no. We had to face facts. Capstone was not a star; the category of ‘funky business books’ had not established itself. Capstone was a rather weak follower in the business-books arena. Capstone had none of the financial attributes of a star. If it looked like a dog, behaved like a dog and barked like a dog, it probably was a dog.

The researcher who led that work went on to study thousands of businesses. She found that the most effective leaders and organizations had range; they were, in effect, paradoxical. They could be demanding and nurturing, orderly and entrepreneurial, even hierarchical and individualistic all at once. A level of ambiguity, it seemed, was not harmful. In decision making, it can broaden an organization’s toolbox in a way that is uniquely valuable. Philip Tetlock and Barbara Mellers showed that thinkers who tolerate ambiguity make the best forecasts; one of Tetlock’s former graduate students, University of Texas professor Shefali Patil, spearheaded a project with them to show that cultures can build in a form of ambiguity that forces decision makers to use more than one tool, and to become more flexible and learn more readily.

learned about our fixed and variable costs, the better I could forecast our "nut," the amount of gross profit we'd need each month to stay in business. My little dog fell

Forecasting: Predicting business outcomes •​Pattern Recognition: Identifying patterns in data •​Personalization: Personalizing experiences •​Recommendation: Making recommendations to achieve desired outcomes

The top ten individual use cases by score across all 5Ps were as follows: 1.​Recommend highly targeted content to users in real time (3.96) 2.​Adapt audience targeting based on behavior and look-alike analysis (3.92) 3.​Measure ROI by channel, campaign, and overall (3.91) 4.​Discover insights into top-performing content and campaigns (3.86) 5.​Create data-driven content (3.82) 6.​Predict winning creatives (e.g., digital ads, landing pages, calls to action) before launch without A/B testing (3.81) 7.​Forecast campaign results based on predictive analysis (3.80) 8.​Deliver individualized content experiences across channels (3.80) 9.​Choose keywords and topic clusters for content optimization (3.78) 10.​Optimize website content for search engines (3.77)

Amazon Comprehend is a natural language processing (NLP) solution that uses machine learning to find and extract insights and relationships from documents. •​Amazon Forecast combines your historical data with other variables, such as weather, to forecast outcomes. •​Amazon Kendra is an intelligent search service powered by machine learning. •​Amazon Lex is a solution for building conversational interfaces that can understand user intent and enable humanlike interactions. •​Amazon Lookout for Metrics detects and diagnoses anomalies in business and marketing data, such as unexpected drops in sales or unusual spikes in customer churn rates. •​Amazon Personalize powers personalized recommendations using the same machine-learning technology as Amazon.com. •​Amazon Polly converts text into natural-sounding speech, enabling you to create applications that talk. •​Amazon Rekognition makes it possible to identify objects, people, text, scenes, and activities in images and videos. •​Amazon Textract automatically reads and processes scanned documents to extract text, handwriting, tables, and data. •​Amazon Transcribe converts speech to text. •​Amazon Translate uses deep-learning models to deliver accurate, natural-sounding translation.

AI is forecasted to have trillions of dollars of impact on businesses and the economy, yet the majority of marketers struggle to understand what it is and how to apply it to their marketing.

at this point, it should be clear that a detailed business plan would have been a disservice to companies like GlobalGiving and Aggregate Knowledge. That’s because a detailed plan would have obfuscated the burning questions and lessened the focus on critical priorities. Any revenue projection would have been sheer folly. And without really knowing what shape the business would take, in GlobalGiving’s case, any forecast of expenses would have been pie in the sky. In each of the two cases, dashboarding allowed the teams to identify the leaps of faith that would mean life or death to their companies. As a result, they could focus their scarce time and precious resources on resolving those issues before moving on to tackle the next set of hurdles.

The official chronicler of business cycles in the United States, the National Bureau of Economic Research, a not-for-profit group founded in 1920, would declare, though many months later, that a recession had set in that August. But in September, no one was aware of it. There were the odd signs of economic slowdown, especially in some of the more interest-rate-sensitive sectors - automobile sales had peaked and construction had been down all year, but most short-term indicators, for example, steel production or railroad freight car loadings, remained exceptionally strong. By the middle of the month, the market was back at its highs and Babson's forecast of a crash had been thoroughly discredited.

I had dropped more or less by chance into the only community of any size in Western Europe where political consciousness and disbelief in capitalism were more normal than their opposites. Up here in Aragon one was among tens of thousands of people, mainly though not entirely of working-class origin, all living at the same level and mingling on terms of equality. In theory it was perfect equality, and even in practice it was not far from it. There is a sense in which it would be true to say that one was experiencing a foretaste of Socialism, by which I mean that the prevailing mental atmosphere was that of Socialism. Many of the normal motives of civilized life — snobbishness, money-grubbing, fear of the boss, etc. — had simply ceased to exist. The ordinary class-division of society had disappeared to an extent that is almost unthinkable in the money — tainted air of England; there was no one there except the peasants and ourselves, and no one owned anyone else as his master. Of course such a state of affairs could not last. It was simply a temporary and local phase in an enormous game that is being played over the whole surface of the earth. But it lasted long enough to have its effect upon anyone who experienced it. However much one cursed at the time, one realized afterwards that one had been in contact with something strange and valuable. One had been in a community where hope was more normal than apathy or cynicism, where the word ‘comrade’ stood for comradeship and not, as in most countries, for humbug. One had breathed the air of equality. I am well aware that it is now the fashion to deny that Socialism has anything to do with equality. In every country in the world a huge tribe of party-hacks and sleek little professors are busy ‘proving’ that Socialism means no more than a planned state-capitalism with the grab-motive left intact. But fortunately there also exists a vision of Socialism quite different from this. The thing that attracts ordinary men to Socialism and makes them willing to risk their skins for it, the ‘mystique’ of Socialism, is the idea of equality; to the vast majority of people Socialism means a classless society, or it means nothing at all. And it was here that those few months in the militia were valuable to me. For the Spanish militias, while they lasted, were a sort of microcosm of a classless society. In that community where no one was on the make, where there was a shortage of everything but no privilege and no boot-licking, one got, perhaps, a crude forecast of what the opening stages of Socialism might be like. And, after all, instead of disillusioning me it deeply attracted me. The effect was to make my desire to see Socialism established much more actual than it had been before. Partly, perhaps, this was due to the good luck of being among Spaniards, who, with their innate decency and their ever-present Anarchist tinge, would make even the opening stages of Socialism tolerable if they had the chance.

At this point, you have set the stage for what the business is. The investors know how you arrived at the problem. They know your solution to the problem and now they know how you make money. Now it is time to show off your projected future. By answering a forecasting question, “What does your business look like over the next five years?

In an interview with Business Wire in November 2011, Buffett said, “If you understand chapters 8 and 20 of The Intelligent Investor (Benjamin Graham, 1949) and chapter 12 of The General Theory (John Maynard Keynes, 1936), you don’t need to read anything else and you can turn off your TV.”2 This advice from Buffett references two classics from the field of investing and economics. Chapter 8 of Graham’s book talks about not letting the mood swings of Mr. Market coax us into speculating, selling in panic, or trying to time the market. Chapter 20 explains that, after careful analysis of a company’s ongoing business and its prospects for future earnings, we should consider buying only if its current price implies a large margin of safety. In chapter 12 of The General Theory of Employment, Interest, and Money (“The State of Long-Term Expectation”), Keynes remarks that most professional investors and speculators were “largely concerned, not with making superior long-term forecasts of the probable yield of an investment over

Stop confusing activities with accomplishment. Stop pushing reps to rush through the sales process. Master the customer conversation with specific personas and use cases. Understand how to sell business value, using a repeatable process. Learn to qualify deal advancement issues in account situations. Coach reps on how to control an opportunity. Understand how to forecast accurately.

Weather forecasting was the beginning but hardly the end of the business of using computers to model complex systems.

Test the market with samples first, if you can, to know what is really going to sell. • If possible, don’t build inventory in large quantities and eat up cash unless the business has the orders in its hands. • Try to find strategic partners that have quick turnarounds for building inventory. • Unless you have real-time data on customer demand and have an extremely tight connection to your suppliers, you’ll never get inventory forecasting exactly right. • Err on the side of less rather than more inventory as a rule of thumb. • If you have to make a trade-off between paying more per unit in COGS to reduce the cycle time to build inventory, choose the higher COGS and reduced production time. You’ll be placing smaller orders with greater frequency, turning inventory faster and cash faster. Read this point again—it’s not very complicated (place smaller orders, more frequently), but it’s really, really important for managing your inventory.

Test before you invest, not once but at every stage. Testing is forecasting. It’s how you see your success before you achieve it.

Strategy usually begins with an assessment of your industry. Your choice of strategic style should begin there as well. Although many industry factors will play into the strategy you actually formulate, you can narrow down your options by considering just two critical factors: predictability (How far into the future and how accurately can you confidently forecast demand, corporate performance, competitive dynamics, and market expectations?) and malleability (To what extent can you or your competitors influence those factors?).

But inflation or deflation can be forecast to some extent. The real risks are those that lie hidden beneath the veneer of “business as usual.

second category, practitioners who, instead of studying future events, try to understand how things react to volatility (but practitioners are usually too busy practitioning to write books, articles, papers, speeches, equations, theories and get honored by Highly Constipated and Honorable Members of Academies). The difference between the two categories is central: as we saw, it is much easier to understand if something is harmed by volatility—hence fragile—than try to forecast harmful events, such as these oversized Black Swans. But only practitioners (or people who do things) tend to spontaneously get the point.

This, ultimately, is one of the most important secrets to learning how to make better decisions. Making good choices relies on forecasting the future. Accurate forecasting requires exposing ourselves to as many successes and disappointments as possible.

On one such call, a salesperson described an account that he’d forecast in detail: “I have buy-in from my champion, the vice president that he reports to, and the head of purchasing. My champion assures me that they’ll be able to complete the deal by the end of the fiscal quarter.” Mark quickly replied, “Have you spoken to the vice president’s peer in the networking group?” Sales rep: “Um, no I haven’t.” Mark: “Have you spoken to the vice president yourself?” Sales rep: “No.” Mark: “Okay, listen carefully. Here’s what I’d like you to do. First, reach up to your face and take off your rose-colored glasses. Then get a Q-tip and clean the wax out of your ears. Finally, take off your pink panties and call the fucking vice president right now, because you do not have a deal.

While the research and applications are in its early days, many experts see probabilistic programming as an alternative approach in areas where deep learning performs poorly, such as concept formulation using sparse or medium-sized data. Probabilistic programs have been used successfully in applications such as medical imaging, machine perception, financial predictions, and econometric and atmospheric forecasting.

Camden in the winter of 1954 was a bleak place. It is difficult to see it this way if you’ve only been there in the summer, but most of Maine can be dismal, especially along the coast, during the long nights and short days. Once the colorful leaves have fallen from the majestic maple trees, and the last tourist has gone home, things become grim. So it was, during that cold January day, when I was on the road hoping to get a ride to New Jersey. On the radio, the weather forecasters predicted an overnight blizzard, but here it was only late afternoon and snow was already accumulating on the road. This would be my last opportunity to get home to see my family and friends, before cruising back on down to the Caribbean. I had really hoped to get an earlier start, to get far enough south to miss the brunt of the storm. Maine is known for this kind of weather, and the snowplows and sanders were ready. In fact, I didn’t see many other vehicles on the road any longer. Schools had let out early and most businesses were closed in anticipation of the storm. My last ride dropped me off in Belfast, telling me that he was trying to get as far as Augusta, before State Road 3 became impassable. Standing alongside the two-lane coastal highway with darkness not far off, I was half thinking that I should turn back. My mind was made up for me when I stepped back off the road, making room for a big State DOT dump truck with a huge yellow snowplow. His airbrakes wheezed as he braked, coming to a stop, at the same time lifting his plow to keep from burying me. The driver couldn’t believe that I was out hitchhiking in a blizzard. This kind of weather in Maine is no joke! The driver told me that the year before a body had been found under a snow bank during the spring thaw. Never mind, I was invincible and nothing like that could happen to me, or so I thought. He got me as far as Camden and suggested that I get a room. “This storm is only going to get worse,” he cautioned as I got off. I waved as he drove off. Nevertheless, still hoping that things would improve, I was determined to continue.

Lukasz Gogolewski is a product manager who provides the help in marketing and forecasting currently lives in New York.

demanded. Starting in the 1950s, however, as computers became more powerful, scientists found they could use Bayesian approaches to forecast events that were previously thought unpredictable, such as the likelihood of a war, or the odds that a drug will be broadly effective even if it has only been tested on a handful of people. Even today, though, calculating a Bayesian probability curve can, in some cases, tie up a computer for hours.

But if you are hoping for a straight path to impact, innovating may appear daunting at first. You need a lot of information to trace changes at the outcome all the way back to the beginnings. That’s why the stories of innovations in hindsight reveal so little of what one needs to do. And forecasting an outcome, or a product, or a user, or an organization, or a business model, or the specific technology needed from the hunch that characterizes the genesis of an innovation requires obtaining an insurmountable amount of knowledge of the dynamics ahead.