The Deciding Factor E-Book

Larry E. Rosenberger

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 339

Veröffentlichungsjahr: 2009

Beschreibung

Praise for The Deciding Factor "Both companies and governments have made some poor decisionsrecently, and almost all would benefit from more fact-based andanalytical approaches. This book provides clear methods andextensive examples for organizations that want to make better,faster, and more consistent decisions."--Thomas H. Davenport,author, Competing on Analytics, and President's DistinguishedProfessor of Information Technology and Management, BabsonCollege "The secrets of the decision-making processes employed by themost successful corporations of the world are revealed in TheDeciding Factor. Both corporate decision makers as well as analystswill gain invaluable insights from this treasure trove of casestudies and expert guidelines." --Robert Heller, former president and CEO of VISA U.S.A., andformer governor, Federal Reserve Board "Information, used correctly and creatively, can be a source oftremendous customer value, competitive advantage, and companyprofitability. The Deciding Factor will help you understand if youhave this opportunity, and how you might seize it." --Nigel Morris, co-founder, Capital One Financial Services "There has never been a more important time in business historyto truly understand both the technical strengths and conceptualweaknesses of decision analytics. If you're prepared to be serious,The Deciding Factor offers the insider's insights that matter whenmanaging innovation risk." --Michael Schrage, author, Serious Play, and researchassociate, MIT Sloan School of Management

Leseprobe

Table of Contents

Title Page

Copyright Page

Introduction

Why This Book Is So Important Now

Who This Book Is For

Who We Are and Why We Wrote This Book

How This Book Can Help You

Chapter 1 - FROM INTUITION TO ALGORITHMS

THE ALGORITHM KILLED JEEVES.

Chapter 2 - HOW ANALYTICS MAKES CUSTOMER RELATIONSHIPS MORE VALUABLE

How Netflix Beat Blockbuster—By Tracking What Customers Really Want

How Tesco Made Online Grocery Shopping Profitable

Best Buy Sharpens Its Customer Focus

Harrah’s Learns How to Use Customer Reward Data

How Best Buy’s Improved “Reward Zone” Tripled the Membership

Chapter 3 - CO-CREATION AND DECISION MANAGEMENT

Always On, Consumer-Centered Marketing

How Nike Practices Always-On Marketing

How Coca-Cola Practices Precision Engagement

Capital One and Bank of Montreal: Co-Creating Credit Cards with Customers

Insurance Companies Try to Use Customer Data—But Face Privacy Issues

Startup Auto Finance Company Customizes Car Loans for Individual Customers

Credit Consumers Take Control with MyFICO

Who Controls the “Opt-In/Opt-Out” Tool?

How Companies Cope with Consumer Sensitivities About Data

Segmenting the Privacy Zealots from the Pragmatists and the Indifferent

Conclusion

Chapter 4 - THE DISCIPLINES OF DECISION LEADERS

Building a Decision Management Infrastructure

Investing in Decision Management Technology Is a Journey

Stage 1: Developing Rules-Based Systems

Stage 2: Using Predictive Analytics Models

Stage 3: Connecting Decisions Across Multiple Dimensions

What Are the Disciplines of Decision Leaders?

Discipline #1: Decision Leaders Are Systematic and Quantitative

Discipline #2: Decision Leaders Are Always Learning and Improving

Discipline #3: Decision Leaders Are Bold and Creative

Seeking (Not Maximizing) Profit Under the Weight of Real-World Constraints

Using the Adaptive Control Technique: Experimenting with Alternative Decisions ...

“The Champion versus the Challenger”: Determining Which Strategy Is Best

Charting the Efficient Frontier to Navigate Among Conflicting Objectives

Using Decision Yield to Consider All Dimensions of Business Benefit

Conclusion

Chapter 5 - THE NEW KNOWLEDGE WORKERS

Analytic Professionals Are the New Knowledge Workers

Information Architects: Where Strategy, Data, and Technology Meet

Number-Crunching Creatives Blend Analytics with the Art of Marketing

The MBAs and the Mathematicians: Where Strategists and Scientists Meet

The Rise of the Decision Natives: Mixing It All Together

How Titles Signal a Company’s Business Priorities

How Analytics Fits Into an Organization’s Structure—Or Doesn’t

Conclusion: What Are the Optimal People Strategies?

Chapter 6 - DEMYSTIFYING DECISION MANAGEMENT

Decision Management Unlocks Value in Data

A Primer on Analytic Techniques

Decision Management Is Not Business Intelligence

Competing on Decision Management

Conclusion: Putting All the Pieces Together

Chapter 7 - THE FUTURE OF DECISION MANAGEMENT

How Decision Management Delivers Differentiation

How Decision Management Makes Knowledge Technologists More Productive

Societal Trends That Are Changing the Way We Do Business

How Decision Management Is Changing in Different Industries

How Decision Management Is Improving in the Financial Services Industry

How Decision Management Is Improving in the Health Care Industry

How Decision Management Is Improving in Retail Businesses

Concluding Thoughts

Notes

Appendix A - FAIR ISAAC’S DECISION MANAGEMENT METHODOLOGY

Appendix B: Glossary

Acknowledgements

The Authors

Index

Copyright © 2009 by Larry Rosenberger and John Nash. All rights reserved.

Portions of this material, including Appendix A, are patent pending.

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Library of Congress Cataloging-in-Publication Data

Rosenberger, Larry, 1946-

The deciding factor : the power of analytics to make every decision a winner / Larry Rosenberger and John Nash with Ann Graham.

p. cm.

Includes bibliographical references and index.

eISBN : 978-0-470-45946-1

1. Decision making. 2. Management. I. Nash, John, 1962- II. Graham, Ann, 1956 July 22- III. Title.

HD30.23.R672 2009

658.4’03—dc22 2008051600

HB Printing

Introduction

Whatever the circumstances—dire or trivial, clear-cut or complex—every decision is a choice between two or more actions that, for better or worse, lead to an outcome. The Deciding Factor is a book about how companies use decision management—the discipline and the technologies—to improve the outcomes of millions of operational decisions that affect a company’s relationship with its customers and prospects.

In 2007 researchers predicted that by 2010 businesses and consumers worldwide would be spending $1.5 trillion annually on information technology hardware, software, and services.1 At work, at home, at school, in our cars, at the beach—indeed, physically and virtually everywhere—our lives operate on continual streams of digital data. Displacing the paper trail is a digital trail, residing in computer databases, both private and public. There isn’t a business or a consumer anywhere today that isn’t touched by the trillions upon trillions of revealing bytes moving through wired and wireless, stationary and mobile information technologies.

All twenty-first-century corporations, no matter the industry or where or how they operate, therefore face the same challenge and opportunity: How can we create financial and customer value from unprecedented access to such huge amounts of digital data?

And the next big question is, what will companies do with this data to stimulate the next wave of business innovation? Can the executives of large corporations who focus on the future even envision what it might look like? As a leader of your corporation, can you?

These are the questions we thought about when we decided to write this book.

Data masters of the Internet, such as Amazon and Netflix, have made analytics and algorithms the hottest buzzwords in business, but how many C-level executives and line managers (unless they have an advanced degree in math or computer science) understand what analytics does? We have written The Deciding Factor not only to show executives why they should care but also to offer a different path to success in a digital world:

• It’s not “all about the data.”

• It’s not “all about the math.”

• It’s not “all about the analytics.”

• It’s not “all about software technology.”

Although all of those elements are important, successfully using all the data you mine and analyze with powerful software—data about your customers, your market, your industry, and your competition—really depends on the decisions you make. At the end of the day, what drives the results your company achieves are the millions of decisions your company makes that are informed by and in turn shape customer interactions and transactions every day. This is what we call decision management. As we will explain, analytics is part of decision management; the two are not synonymous.

For more than fifty years, the mathematicians, operations researchers, and computer scientists at our company, the Fair Isaac Corporation, have helped companies create business value by making better operational decisions. Our company is perhaps best known for the credit score product known as the FICO® score, which is based on our own proprietary algorithms to measure an individual’s credit risk. FICO scores have become a standard tool used by consumer banks around the world to measure and evaluate consumer credit risk, for credit cards, bank lines of credit, mortgages, and insurance.

The Deciding Factor shows how leading companies in financial services industries and many other consumer industries—insurance, retail, health care, and consumer-packaged goods, among others—are using analytic techniques to predict customer behavior and then use that insight to make better operational decisions. When executed well, the actions from those analytically based decisions create more value for customers and more profit for companies.

But before we get caught up in analytics—and the “analytic techniques” and “analytically based decisions” mentioned in the previous paragraph—keep in mind that, in plain English, analytics is simply “a method of logical analysis.” And data is “factual information (as measurements or statistics) used as a basis for reasoning, discussion, or calculation,” according to Webster’s. That’s exactly what we’re advocating in this book: how to use logical analysis based on data to make better decisions that will benefit both your business and your customers. Add digital— “information in numerical form that can be digitally transmitted or processed”—into the mix, and all together they form decision management.

The Deciding Factor features companies from around the world: Fair Isaac clients, such as Coca-Cola, Best Buy (the electronics retailer), Capital One (the financial services company that introduced the mass-customized credit card that is now an industry standard), ICICI (India’s top retail bank that is going global), and Akbank (Turkey’s leading consumer bank). We also look at companies we admire, such as Tesco (the third largest grocer and general merchandise retailer in the world), Netflix (the start-up company that blew away Blockbuster in DVD rentals), and Harrah’s Entertainment (the world’s largest gaming company). These are companies that are well known for their analytical prowess, but we also look at them as customer-driven decision makers.

Why This Book Is So Important Now

It’s no accident that the information technology industry long ago chose the word mining to describe what companies aspire to do with their data. They knew data contained gold, and now that we are well into the digital age, companies are scrambling to figure out how they can get their piece of its value.

This book is long overdue. The data rush has been going on for some time now, though it seems only recently that executives and the strategy consultants who advise them have started to pay more attention to the importance of excellence in operations and the value of decision management technology. For example, Ian Davis, the chairman and CEO of McKinsey & Company, declared, “Long-gone is the day of gut instinct management styles . . . Today’s business leaders are adopting algorithmic decision-making techniques and using highly sophisticated software to run their organizations. Scientific management is moving from a skill that creates competitive advantage to an ante that gives companies the right to play the game.”2

Simon Kennedy and Dave Matheson, both senior partners with Boston Consulting Group, confirm the importance of using data:

Given that this is the information age, why don’t those who can benefit from existing data? Often the problem is that companies do have access, but don’t recognize or appreciate what is in their grasp. With so many facts at hand about so many things—products, customers, sales, and more—it’s easy to fail to connect the dots. Simply put, many companies have a competency issue in this area: very few have an advanced analytics capability or even a home for one. The operations side produces streams of data, and IT ships it around but no one has the time, tools, or ability to take the data apart to find opportunities for advantage.3

Finally, C. K. Prahalad, one of the world’s most influential management thinkers, believes that “As digitization permeates every aspect of business, every business is in effect, an e-business . . . While the hardware and connectivity part of this architecture can be delegated to IT departments and vendors, CEOs and line managers cannot delegate strategic decisions on the business applications, analytics capabilities, and data warehousing.”4

In addition to the increasing attention analytics is getting from management thought leaders, media buzz is building: stories about data mining, business intelligence software, and analytics have always been standard fare in IT trade journals, but in the last three years more and more general business reporters in newspapers and magazines have picked up on stories about the way mathematics and computers are transforming how businesses operate and how we live. For example:

Consumers and companies increasingly depend on a hidden mathematical world. Algorithms sound scary, of interest only to dome-headed mathematicians. In fact, they have become instruction manuals for a host of routine consumer transactions . . . from analyzing credit card transactions to deciding how to stack supermarket shelves, algorithms now underpin a large amount of everyday life.5

and this:

Rising flows of data give companies the intelligence to home in on the individual customer. Internet marketers are the natural leaders, but traditional businesses are following suit. Gary W. Loveman, CEO of casino giant Harrah’s Entertainment Inc. and a former Harvard B-school professor, has led the company to build individual profiles of millions of Harrah’s customers. The models include gamblers’ ages, gender, and ZIP codes, as well as the amount of time they spent gambling and how much they won or lost. These data enable Harrah’s to study gambling through a host of variables and to target individuals with offers, from getaway weekends to gourmet dining, calculated to maximize returns. In the last five years, Harrah’s has averaged 22% annual growth, and its stock has nearly tripled.6

and this:

The desire to exploit computing and mathematical analytics is by no means new. In the 1960s and ’70s, operations research combined computing and math mainly to make factory production work more efficient . . . most companies now have the tools to do the kind of competitive analytics that only a relative handful of elite companies could do in the past. “It’s really starting to become mainstream,’ says [Thomas H.] Davenport,” co-author of a book on the topic: Competing on Analytics: The New Science of Winning. The entry barrier, he says, “is no longer technology, but whether you have executives who understand this.”7

Who This Book Is For

Tom Davenport’s comment echoes our sentiments exactly. Until recently, books about analytics (and books about decision management, although there aren’t many of those) have been technical tomes, written for the mathematicians who dream up the equations, the IT professionals who embed the math in the software, and the operations experts who manage and try to improve business processes. Unlike them, The Deciding Factor is a strategy book that brings to life technical ideas about analytics for a broad management and strategy audience—both senior executives and rising executives.

The Deciding Factor can help managers in every industry, though the book does slightly emphasize financial services companies, because for two decades, many of the leading companies using analytics and decision management technologies have come from the consumer lending and banking sector. We have also chosen to focus on the demand side of business-to-consumer companies, rather than the supply-side applications for which analytics is also used. Analytics and decision management are spreading fast among consumer companies, in insurance, health care, and grocery and general merchandise retailing, because companies in these industries are flooded with transaction data that can be used to improve the customer experience, operating efficiency, and financial performance. So whatever industry you’re working in, we’re confident you have lots of untapped data that you can use to better your decisions and thus your competitive advantage. The book also offers a global perspective by featuring companies from the United States, Canada, Great Britain, Turkey, Brazil, Romania, and India.

Who We Are and Why We Wrote This Book

Fair Isaac is a global corporation serving clients in more than eighty countries, with revenues in 2008 of $745 million; it is one of the world’s leading purveyors of mathematically based software used by corporations to automate operational decisions and to optimize profit and mitigate risk. Our market-leading software is used by many of the largest corporations in the world to improve their decisions in marketing, customer management, collections, and fraud management, with products such as the TRIAD® adaptive control system and Falcon® Fraud Manager.

We were motivated to write a book about the relationship between strategy and operations—and its link to customer value—because we worked as both executive leaders and technical implementers throughout our careers. Thinking about strategy and operations is a natural for both of us. Larry joined Fair Isaac in 1974 “as a kid out of school,” as he likes to say, with degrees in physics from the MIT and in operations research from UC Berkeley. In 1991, he was chosen to succeed Bill Fair (one of Fair Isaac’s cofounders) as CEO, a position he held until 1999, after which he went on to lead the R&D group, returning to his first passion: finding solutions to complex business process problems. In 2007 he became Fair Isaac’s first research fellow.

John has an MBA from the University of Minnesota with a focus on information and decision sciences. Starting his career as a parts buyer for Rosemount Aerospace, a company that made precision flight sensors for the Space Shuttle and military aircraft, John moved on to Accenture, where he spent fourteen years working across retail, high-tech, manufacturing, and other industries, to help companies gain competitive advantage from technology. He was one of three founders of Accenture’s global customer insight and CRM practices. An executive at Fair Isaac since 2002, he is currently the VP of strategy.

Together, we’ve worked with clients as diverse as retailers Best Buy, Lands’ End, Lucky Stores (the grocery chain, not Lucky brand jeans), and SUPERVALU (the third largest food retailer in the United States); in the financial world, J. P. Morgan Chase, U.S. Bank, Wells Fargo Bank, GE Capital, HSBC, Banco Santander, ICICI, Sumitomo, Citi, Bank of America, Visa, MasterCard, and E*TRADE; and a range of other companies from Paul Revere insurance to Gateway computers.

We wrote this book to help executives understand the relationship between math and improving business processes, and to help managers and modelers use data, analytics, and software to make those decisions not incrementally better but orders of magnitude better.

To do this well, we believe executives need to think about the company’s IT infrastructure not as technology to process data, as they have in the past, but as strategic resource for making better decisions. Furthermore, as IT infrastructures are streamlined and integrated, there is more opportunity to bring people and technology together to create a decision advantage. As MIT’s Michael Schrage writes:

“The big-box retailers, Wal-Mart and Best Buy, are widely regarded as having superior analytic infrastructures. But they don’t just hire the smartest ‘quants.’ They push them to make their mathematical tools accessible to others. They’re constantly rethinking when mathematics should automate a decision, and when it should assist a decision maker.”8

How This Book Can Help You

There are many management books for senior executives that focus on the periodic big-picture decisions that set the direction and objectives for the entire firm. In contrast, The Deciding Factor emphasizes how you can manage more effectively the quality of the millions of operational decisions your company makes every day—each of which can determine whether or not you achieve your corporate objectives. Even successful companies don’t appreciate how much value can be created or lost in a single transaction, and how the cumulative effects of bad or careless operational decisions harm performance. For example, in the credit card industry it is well documented that companies have accepted losses from defaults of between 6 and 7 percent on annual receivables, adding up to billions of dollars.

Decision management is most applicable to high-volume, repeatable operational decisions. However, these decision management principles are not applicable to certain types of decision problems, namely:

• Highly collaborative, interactive team decisions, such as those that a team of engineers need to make regarding the design of a bridge.

• Corporate decisions that are singular or rarely repeated, such as deciding whether to expand in certain countries or to acquire another company.

Instead, The Deciding Factor focuses on how business-to-consumer companies—in consumer lending and banking, retailing, consumer packaged goods, insurance, health care, and mobile telecom industries—are making significant progress in applying decision management to:

• Use predictions of consumer behavior to grow the value of customer relationships (in other words, to attract, grow, and retain profitable customers)

• Make operational decision making more profitable, through effective use of voluminous customer data—and despite the increasing complexity and shifting dynamics of the business environment

• Increase the efficiency of decisions through automation, while simultaneously lowering risks and raising revenues

In other words, this book can help you serve your customers better and make more money. And who doesn’t want that? As you begin this book, we ask you to keep in mind these key ideas:

• Many managers responsible for “strategy” underestimate the effect that operational decisions have on strategy outcomes. Indeed, salespeople, customer service reps, logistics managers, and marketing managers make decisions all the time. The quality, precision, and strategic forethought of decisions made throughout an organization have vital consequences for the overall success of corporate strategy.

• Companies are not profit maximizers; they are constrained profit seekers. The process of making business decisions almost always involves trade-offs between multiple competing objectives. We all know that what’s good for shareholders may not be good for customers or employees, but we don’t always acknowledge this. When a bank’s marketing department sends credit card solicitations to people who are behind on their payments on an existing card, they are undermining the collection department’s efforts to reduce delinquencies. When cost cutting degrades the customer experience, how much business value is lost even if short-term profitability goes up? Decision management makes the trade-offs visible.

• You don’t have to do the math to use it anymore. Through automation and the distributed computing infrastructures, now operations research and analytic engineers, mathematicians, and computer specialists are not the only ones who can create business value. From the boardroom to the front line, executives and managers are using analytical insights to guide their decisions.

• Companies get more business value from customer data when they don’t try to squeeze the most profit they can out of every customer transaction. They manage data to boost profitability by adding value to and strengthening the relationship over time.

We hope that you enjoy this book as much as we enjoyed writing it, as it truly is a wonderful story, given that there is both social and economic value in what we do. The social benefit comes when decisions are made on a much more objective and fair basis; for example, in responsible lending and responsible borrowing. Given the increasing complexities of financial systems and the global economy, there has never been a better time to apply mathematics and technology to ensure that decisions are fact-based, systematic, and transparent. The economic benefits will become more obvious as you read the case examples in the chapters to come, and we believe there are endless additional benefits to be gained when these decision management principles are put into practice by leaders with the right insight and creativity.

1

FROM INTUITION TO ALGORITHMS

A Brief History of Using Analytics to Make Better Decisions

Late on a November night in 2006, along New York City’s Bruckner Expressway in the South Bronx, a solid azure blue, brightly lit new billboard declared, in a single line of bold white block text:

THE ALGORITHM KILLED JEEVES.

The billboard stood out among the others hawking car dealers, reality TV shows, and sex clubs. Although it wasn’t hard to get the “whodunit?” message, the billboard’s sponsor was a mystery. A quick search, though, revealed that it was Ask.com, the search engine owned by the website conglomerate IAC Search and Media, Inc. Apparently its marketers had decided that a billboard along the Bruckner—the roadway home to the suburbs for the search engine’s target user—would be a good place to announce that the dapper info-butler Jeeves had been dismissed for a better and faster model: the algorithm. The billboard was meant to draw attention to Ask.com’s new and improved website-ranking algorithm called ExpertRank, and to contrast it with archrival Google’s search algorithm, PageRank.

Geeky highway billboards, sponsored by cheeky web search engine marketers, are certainly signs of the times. Mathematical moguls are making vast fortunes by differentiating models that compute complex equations with extraordinary speed and precision. “Once upon a time, the most valuable secret formula in American business was Coca-Cola’s. Today it’s Google’s master algorithm,” wrote Randall Stross, author of multiple books on internet-era moguls, in his New York Times column “Digital Domain.”1 An algorithm is a set of mathematically derived instructions to accomplish a defined task. Algorithms running on powerful computer networks are not just a part of the digital revolution; they are spawning a revolution in how business decisions are managed and made.

Of course, the seeds for this revolution—and for the digital technology that enables companies to apply such mathematical rigor to operational decision making—were planted long ago.

“Predicting short-term changes or shocks is often a fool’s errand. But forecasting long-term directional change is possible by identifying trends through an analysis of deep history rather than of the shallow past. Even the Internet took more than 30 years to become an overnight phenomenon,”2 writes Ian Davis, chairman and CEO of McKinsey & Company.

Today’s digital data management discipline known as analytics began with the first mainframe computers in the 1950s. In this chapter, we look back over the past sixty years, not because the history of analytics and decision management is so fascinating (though much of it is, as you’ll see), but to show you how far companies have come in using computers and analytics to achieve all of the following goals:

• To sort through the enormous amount of data they have about their businesses

• Which helps them make better decisions about serving their customers

• Which in turn improves the value they offer their customers as well as their overall profitability

If you share these goals, read on.

The Pioneers of Decision Management

Long before marketers were posting arcane mathematical terms on highway billboards, business pioneers were using math and computers to make better decisions. These business visionaries promoted a union of computing power, powerful equations, and brainpower to achieve business insights from deep and diverse analysis of operational data.

The First Use of Computers to Improve Decision Making

Back in the 1950s, at MIT’s Sloan School of Management, computer scientist Jay Forrester argued that a large corporation is a complex social system far too abstract for human beings to manage effectively without the aid of computers. He asserted that we literally need technology to understand the relationships and interactions among people in big organizations. In 1961, Forrester published Industrial Dynamics, his seminal book on systems dynamics—an analytical, problem-solving methodology he developed that employs computer-based simulations to help managers visualize and understand cause-and-effect relationships in decision making and business processes that would otherwise be invisible and inestimable.

Management decisions based only on mental models are inferior to decisions derived from computer models that can represent complex relationships and predict outcomes that the human mind can’t.

Forrester also used the term mental models to describe how people tend to make decisions based on instinct and interpretation rather than on fact. Forrester believed that management decisions based only on mental models and human judgments are inferior to decisions derived from computer models that can represent complex relationships and predict outcomes that the human mind can’t. In the 1970s, Donnella Meadows, a protégé of Forrester’s from MIT, applied his theories of systems dynamics to produce a global model for the Club of Rome that was the basis for the controversial book Limits to Growth, which predicted all of the long-term trends in population growth, economics, and the state of the earth’s environment that have since come to pass. Another Forrester protégé, Peter Senge, popularized systems dynamics in a management context with his book The Fifth Discipline. Decision management arises from the same notions of systems complexity.

Whereas Forrester advocated for more computer-guided management of business systems in the 1950s and early ’60s in Cambridge, the International Business Machine Corporation—now known simply as IBM—was making its transition from punch card processors to electronic computers. Thomas J. Watson, Jr., bet the company’s future on the thinking machines his father had dismissed as too expensive and unreliable. Taking charge in 1952, the younger Watson hired hundreds of electrical engineers to start designing the first mainframe computers. Little did he know that this decision to commit IBM’s business machine vision to computers would kick-start the information technology revolution in business and the beginnings of decision management in large corporations.

Fair Isaac’s Formative Days with Decisions Management

At about the same time, in California, two young process management scientists—William R. Fair, an engineer, and Earl J. Isaac, a mathematician—were starting their careers in the new field of operations research. Then, as now, operations research involved applying advanced mathematics and statistics using computers to analyze complex operational business processes to improve the process through better decisions. Bill and Earl met in 1953 at the Stanford Research Institute (SRI), a think tank that primarily did operations research for the military. Bill and Earl spent their days as operations research scientists at SRI, helping the U.S. Defense Department figure out how to contain the destruction of an atomic bomb. They created elaborate mathematical models to run on SRI’s behemoth computer in order to answer basic questions. Their concern was not how to build missiles and atomic weapons, but how to operate them. How do you carry them? Where do you aim? How close to the target?

Bill had studied engineering at the California Institute of Technology in the 1940s. During World War II, he had been a radar technical representative for Sperry Gyroscope and had served in the Pacific with the Marine Corps. As a civilian, he also applied his engineering skills repairing night fighter radars on aircraft carriers. After the War, Fair finished his schooling at Berkeley and Stanford. Isaac, who studied mathematics at the U.S. Naval Academy and UCLA, had been part of the team that developed the initial programming for one of the first electrical computers—the U.S. Bureau of Standards Electronic Eastern Automatic Computer, otherwise known as SEAC.

As Bill progressed in his career at SRI and his analysis of operational processes for missile systems and atomic weapons, he became convinced the research they were performing for the military could be just as valuable to businesses. Why couldn’t the operational analysis performed for the Defense Department be applied in other contexts, like corporations serving consumer product and service markets? He visualized the corporation as a sensitive machine similar to the radar systems he had repaired during the war. Like Jay Forrester, he believed that managers needed computers and mathematics to solve tough operational problems and to make consistently better managerial choices.

Bill founded SRI’s first nonmilitary operations research practice, and he asked Earl to join his group. It wasn’t long before the independent and ambitious duo decided to leave SRI to form their own consulting business for the private sector. In an interesting turn of fate, Bill had taken half his courses at the business school while working toward a master’s in engineering at Stanford. Combining Bill’s head for business with Earl’s rare computer talents and passion for mathematics, in 1956 they each chipped in $400 to start Fair, Isaac and Company, Inc. According to Fair Isaac lore, Bill and Earl decided to combine their own last names to come up with a name for the company, but they were concerned that “Isaac Fair” sounded like one person and “Fair Isaac” sounded like a used car salesman. As Fair tells the story, they “settled on the lesser of two evils.” Bill Fair was among the attendees at the First International Conference on Operations Research in 1957, just after they named their new company.

Bill Fair and Earl Isaac founded Fair Isaac because they believed, as did their business-minded engineering and mathematician peers, that the operational processes of corporations conceal a treasure trove of information to help managers run better companies. For an organization to be the best, its operational management decisions must be methodical and data-driven—not just guided by gut feelings and consensus. Their vision was to create computer-based mathematical tools for use by corporations to sharpen operational decision making and make process management the foundation for achieving consistently better business results. Fair and Isaac knew they could do the math and the analysis. The only glitch was that computing technology was still too primitive, too scarce, and too expensive.

In the 1950s—when men wore hats, not headphones, and computers were the size of a tank—few companies even used computers or would have known what to do with it if they had one. Even Bill and Earl didn’t have their own a computer, so they worked out a time-share deal with the Standard Oil Company of California (today’s Chevron) to use its mainframe during nonpeak evening hours to conduct their research. The SEAC machine Earl had worked with had been a physical monster with a grand total of only 512 words of high-speed memory. Earl contributed to the development of many of the early computer languages, but his thorough grounding in machine language and even in bit programming, along with his natural talent for the subject, gave him an understanding of the nature of the computer that was equaled by few people in the world at that time.

Fair Isaac Takes Off with the U.S. Credit Card Industry

It took the fledgling company almost three years, but in 1958, Fair and Isaac and Earl Follett—a fellow mathematician, alumni of SRI, and Fair Isaac’s first employee—identified consumer credit as a process in which they could put their ideas to work. By the 1960s, as more business operations started to be computerized, and credit cards became an accepted alternative to cash, suddenly it was possible for companies to capture data on customers’ behavior. When people pay cash for goods and services, it is an “anonymous” transaction. The only record of the transaction is the receipt. For the first time, companies could capture transaction-level data on masses of people.

Credit Scoring Drives Better Decisions and Growth in Consumer Lending. Credit card issuers were interested in seeing trends (that is, what people were buying or not buying). They were even more interested in knowing more about how to manage the risks of mass market lending. Fair Isaac invented the credit score to help lenders analyze each applicant’s credit risk while handling many more applications than they ever had before. The credit score was the first big application of analytics for Fair Isaac, and the beginning of what the company today calls decision management.

A few companies had dabbled in business applications of scoring as early as World War II, but none thought of applying it to consumer lending. As operations research experts, Bill and Earl were familiar with statistical analytic techniques such as multivariate analysis and logistic regression. Earl Follet knew how to apply these concepts to managing credit risk. When Fair Isaac’s first credit scoring model was introduced in 1958, it was the first to use the historical data being captured by finance companies to predict a person’s creditworthiness based on their past behavior. The model produced a score, based on analysis of specific sets of numbers related to variables such as a person’s bank balance and payment records. The credit score was a far better predictor of a customer’s ability to pay back a loan than any decision a banker could make on his own, even if he knew the applicant personally.

Using Predictive Analytics to Make Better Decisions About Customers’ Behavior. Predictive analytics is a way to make connections between the past and the future, using historical data to predict future events. Simply put, it’s the study of how what you know at the time you make a decision relates to what you don’t know: what will happen in the future. The credit-scoring model was built based on variables such as these:

• Income

• Bank account balances

• Outstanding credit

• Payment history

• Time with present employer

These variables were vetted as highly predictive of a consumer’s creditworthiness.

Credit scoring models, and the type of predictive analytics Fair Isaac is known for generally, quantify the patterns and relationships among dozens of variables. Every credit application had all the data needed to build the model. A single score could convey the risk associated with a person’s future payment behavior and the person’s risk profile relative to the behavior of many other people. Using mathematics to predict the behavior of masses of consumers was a revolutionary concept when first proposed. Today, credit scoring is a cornerstone of lending processes, and other analytic applications using data on consumer behavior are revolutionizing mass advertising, direct marketing, and customer service—to name a few business processes that are spawning new, creative analytic applications.

Fair Isaac’s first foray into credit scoring, however, took more than a decade to take off. In fact, Fair Isaac didn’t sell a credit-scoring system to a bank’s credit card division until 1970. The first general-purpose FICO® score was not developed until 1989. It took time for business attitudes and technology to change.

In 1958, Bill and Earl sent letters to about fifty major credit grantors—mainly consumer banks and finance companies—in the United States asking for a meeting to explain credit scoring and its value. Only one institution replied. More often than not, business clients showed little interest in operational insights. All they wanted was to install their first computer and get it running. The idea of the computer as a tool for analytic computation was way ahead of what business people were thinking.

Still, the timing for scoring was right, because it coincided with growth in nonbanking businesses that were offering credit and capturing the data. Early charge cards (which were metal, not plastic) had been around since the 1930s. By the late 1950s, consumer use of cards rather than cash was growing, and metal charge cards were being replaced by the plastic credit cards we use today. Although Fair Isaac’s first credit-scoring system sale was to American Investment—a finance company based in St. Louis, Missouri—banks were initially reluctant to adopt the new credit-scoring approach.

On the other hand, national department store chains—such as JCPenney, Montgomery Ward, and Sears, Roebuck—were intrigued by the idea of a systematic way to grow their store charge card bases at a time when few people had credit cards. Montgomery Ward, one of the first U.S. national department store chains, was one of Fair Isaac’s best and most progressive clients. As credit cards took off in other consumer service industries (gasoline retail and hotels), more businesses became interested in credit scoring.

But the information management tools and processes were still basic. Before oil companies and department stores began automating their accounts receivables and billing processes, customer account records were stored on ledger cards coded from handwritten account information. Ledger cards were created and maintained using the National Cash Register (NCR) Company’s billing machines—which had one keypad for debits and another for credits—to calculate balance, finance charges, and so on. The ledger cards, posted manually, listed information about each customer’s charge purchases—the dollar amounts, what they bought, and the date.

As late as the mid-1970s, Fair Isaac staffers had to spend days in their retail store client’s backroom credit card operations so they could gather the data to build their models. They used a microfilm camera to photograph the information from the ledger cards. Larry recalls that during the summer of 1974 he drove from store to store, photographing thousands of records and praying the images would come out so that he would not have to go back and do it all again. While onsite, he also listened to the store’s collection department staff calling delinquent customers. He recalls that it was eye-opening to see the reality of the process Fair Isaac was trying to improve.