Data Mining for Business Analytics

Concepts, Techniques, and Applications with JMP Pro

Galit Shmueli, Peter C. Bruce, Mia L. Stephens, Nitin R. Patel

• Publisher: Wiley
• Year: 2016
• ISBN:
  9781118877524
• Edition: 1
• Topic: Mathematics
• Subtopic: Probability & Statistics
• Index: Mathematics

Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® presents an applied and interactive approach to data mining.

Featuring hands-on applications with JMP Pro®, a statistical package from the SAS Institute, the book
uses engaging, real-world examples to build a theoretical and practical understanding of key data mining methods, especially predictive models for classification and prediction. Topics include data visualization, dimension reduction techniques, clustering, linear and logistic regression, classification and regression trees, discriminant analysis, naive Bayes, neural networks, uplift modeling, ensemble models, and time series forecasting.

Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® also includes:

• Detailed summaries that supply an outline of key topics at the beginning of each chapter
• End-of-chapter examples and exercises that allow readers to expand their comprehension of the presented material
• Data-rich case studies to illustrate various applications of data mining techniques
• A companion website with over two dozen data sets, exercises and case study solutions, and slides for instructors www.dataminingbook.com

Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® is an excellent textbook for advanced undergraduate and graduate-level courses on data mining, predictive analytics, and business analytics. The book is also a one-of-a-kind resource for data scientists, analysts, researchers, and practitioners working with analytics in the fields of management, finance, marketing, information technology, healthcare, education, and any other data-rich field.

Part I
Preliminaries

1
Introduction

1.1 What is Business Analytics

Business analytics 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, 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 includes more sophisticated data analysis methods, such as statistical models and data mining 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).

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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:

1. 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 credit-worthiness; it is based mainly on a predictive model that uses prior data to predict repayment behavior.
2. 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.
3. 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).

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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 ...