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INTRODUCTION

1.1 WHAT IS BUSINESS ANALYTICS?

Business analytics (BA) is the practice and art of bringing quantitative data to bear on decision‐making. The term means different things to different organizations.

Consider the role of analytics in helping newspapers survive the transition to a digital world. One tabloid newspaper with a working‐class readership in Britain had launched a web version of the paper, and did tests on its home page to determine which images produced more hits: cats, dogs, or monkeys. This simple application, for this company, was considered analytics. By contrast, the Washington Post has a highly influential audience that is of interest to big defense contractors: it is perhaps the only newspaper where you routinely see advertisements for aircraft carriers. In the digital environment, the Post can track readers by time of day, location, and user subscription information. In this fashion the display of the aircraft carrier advertisement in the online paper may be focused on a very small group of individuals—say, the members of the House and Senate Armed Services Committees who will be voting on the Pentagon's budget.

Business analytics, or more generically, analytics, includes a range of data analysis methods.

Many powerful applications involve little more than counting, rule checking, and basic arithmetic. For some organizations, this is what is meant by analytics.

The next level of business analytics, now termed business intelligence (BI), refers to the use of data visualization and reporting for becoming aware and understanding “what happened and what is happening.” This is done by use of charts, tables, and dashboards to display, examine, and explore data. Business intelligence, which earlier consisted mainly of generating static reports, has evolved into more user‐friendly and effective tools and practices, such as creating interactive dashboards that allow the user not only to access real‐time data, but also to directly interact with it. Effective dashboards are those that tie directly to company data, and give managers a tool to see quickly what might not readily be apparent in a large complex database. One such tool for industrial operations managers displays customer orders in one two‐dimensional display using color and bubble size as added variables. The resulting 2 by 2 matrix shows customer name, type of product, size of order, and length of time to produce.

Business analytics now typically includes BI as well as sophisticated data analysis methods, such as statistical models and machine learning algorithms used for exploring data, quantifying and explaining relationships between measurements, and predicting new records. Methods like regression models are used to describe and quantify “on average” relationships (e.g., between advertising and sales), to predict new records (e.g., whether a new patient will react positively to a medication), and to forecast future values (e.g., next week's web traffic).

Readers familiar with the earlier edition of this book might have noticed that the book title changed from Data Mining for Business Analytics to Machine Learning for Business Analytics. The change reflects the more recent term BA, which overtook the earlier term BI to denote advanced analytics. Today, BI is used to refer to data visualization and reporting. The change from data mining to machine learning reflects today's common use of machine learning to refer to algorithms that learn from data. This book uses primarily the term machine learning.

WHO USES PREDICTIVE ANALYTICS?

The widespread adoption of predictive analytics, coupled with the accelerating availability of data, has increased organizations' capabilities throughout the economy. A few examples:

Credit scoring: One long‐established use of predictive modeling techniques for business prediction is credit scoring. A credit score is not some arbitrary judgement of creditworthiness; it is based mainly on a predictive model that uses prior data to predict repayment behavior.

Future purchases: A more recent (and controversial) example is Target's use of predictive modeling to classify sales prospects as “pregnant” or “not‐pregnant.” Those classified as pregnant could then be sent sales promotions at an early stage of pregnancy, giving Target a head start on a significant purchase stream.

Tax evasion: The US Internal Revenue Service found it was 25 times more likely to find tax evasion when enforcement activity was based on predictive models, allowing agents to focus on the most likely tax cheats (Siegel, 2013).

The business analytics toolkit also includes statistical experiments, the most common of which is known to marketers as A/B testing. These are often used for pricing decisions:

• Orbitz, the travel site, has found that it could price hotel options higher for Mac users than Windows users.
• Staples online store found that it could charge more for staplers if a customer lived far from a Staples store.

Beware the organizational setting where analytics is a solution in search of a problem: a manager, knowing that business analytics and machine learning are hot areas, decides that her organization must deploy them too, to capture that hidden value that must be lurking somewhere. Successful use of analytics and machine learning requires both an understanding of the business context where value is to be captured and an understanding of exactly what the machine learning methods do.

1.2 WHAT IS MACHINE LEARNING?

In this book, machine learning or data mining refers to business analytics methods that go beyond counts, descriptive techniques, reporting, and methods based on business rules. While we do introduce data visualization, which is commonly the first step into more advanced analytics, the book focuses mostly on the more advanced data analytics tools. Specifically, it includes statistical and machine learning methods that inform decision‐making, often in automated fashion. Prediction is typically an important component, often at the individual level. Rather than “what is the relationship between advertising and sales?” we might be interested in “what specific advertisement, or recommended product, should be shown to a given online shopper at this moment?” Or we might be interested in clustering customers into different “personas” that receive different marketing treatment, then assigning each new prospect to one of these personas.

The era of Big Data has accelerated the use of machine learning. Machine learning methods, with their power and automaticity, have the ability to cope with huge amounts of data and extract value.

1.3 MACHINE LEARNING, AI, AND RELATED TERMS

The field of analytics is growing rapidly, both in terms of the breadth of applications, and in terms of the number of organizations using advanced analytics. As a result, there is considerable overlap and inconsistency in terms of definitions. Terms have also changed over time.

The older term data mining means different things to different people. To the general public, it may have a general, somewhat hazy and pejorative meaning of digging through vast stores of (often personal) data in search of something interesting. Data mining, as it refers to analytic techniques, has largely been superseded by the term machine learning.

Other terms that organizations use are predictive analytics, predictive modeling, and most recently machine learning and artificial intelligence (AI).

Many practitioners, particularly those from the IT and computer science communities, use the term AI to refer to all the methods discussed in this book. AI originally referred to the general capability of a machine to act like a human, and, in its earlier days, existed mainly in the realm of science fiction and the unrealized ambitions of computer scientists. More recently, it has come to encompass the methods of statistical and machine learning discussed in this book, as the primary enablers of that grand vision, and sometimes the term is used loosely to mean the same thing as machine learning. More broadly, it includes generative capabilities such as the creation of images, audio, and video.

Statistical Modeling vs. Machine Learning

A variety of techniques for exploring data and building models have been around for a long time in the world of statistics: linear regression, logistic regression, discriminant analysis, and principal components analysis, for example. But the core tenets of classical statistics—computing is difficult and data are scarce—do not apply in machine learning applications where both data and computing power are plentiful.

This is what gives rise to Daryl Pregibon's description of “data mining” (in the sense of machine learning) as “statistics at scale and speed” (Pregibon, 1999). Another major difference between the fields of statistics and machine learning is the focus in statistics on inference from a sample to the population regarding an “average effect”—for example, “a $1 price increase will reduce average demand by 2 boxes.” In contrast, the focus in machine learning is on predicting individual records—“the predicted demand for person...