Predictive Business Analytics

Forward Looking Capabilities to Improve Business Performance

Lawrence Maisel, Gary Cokins

• Publisher: Wiley
• Year: 2013
• ISBN:
  9781118240151
• Edition: 1
• Topic: Business
• Subtopic: Business General
• Index: Business

Discover the breakthrough tool your company can use to make winning decisions

This forward-thinking book addresses the emergence of predictive business analytics, how it can help redefine the way your organization operates, and many of the misconceptions that impede the adoption of this new management capability. Filled with case examples, Predictive Business Analytics defines ways in which specific industries have applied these techniques and tools and how predictive business analytics can complement other financial applications such as budgeting, forecasting, and performance reporting.

• Examines how predictive business analytics can help your organization understand its various drivers of performance, their relationship to future outcomes, and improve managerial decision-making
• Looks at how to develop new insights and understand business performance based on extensive use of data, statistical and quantitative analysis, and explanatory and predictive modeling
• Written for senior financial professionals, as well as general and divisional senior management

Visionary and effective, Predictive Business Analytics reveals how you can use your business's skills, technologies, tools, and processes for continuous analysis of past business performance to gain forward-looking insight and drive business decisions and actions.

PART ONE
“Why”

CHAPTER 1
Why Analytics Will Be the Next Competitive Edge

The farther backward you can look, the farther forward you are likely to see.

—Winston Churchill

Analytics is becoming a competitive edge for organizations. Once a “nice to have,” applying analytics, especially predictive business analytics, is now becoming mission-critical.

An August 6, 2009, New York Times article titled “For Today's ­Graduate, Just One Word: Statistics”¹ refers to the famous advice to Dustin Hoffman's character in his career-breakthrough movie The Graduate. The quote occurs when a self-righteous Los Angeles businessman takes aside the baby-faced Benjamin Braddock, played by Hoffman, and declares, “I just want to say one word to you—just one word—‘plastics.'” Perhaps a remake of this movie will be made and updated with the word analytics substituted for plastics.

This spotlight on statistics is apparently relevant, because the article ranked in that week's top three e-mailed articles as tracked by the New York Times. The article cites an example of a Google employee who “uses statistical analysis of mounds of data to come up with ways to improve [Google's] search engine.” It describes the employee as “an Internet-age statistician, one of many who are changing the image of the profession as a place for dronish number nerds. They are finding themselves increasingly in demand—and even cool.”

ANALYTICS: JUST A SKILL, OR A PROFESSION?

The use of analytics that includes statistics is a skill that is gaining mainstream value due to the increasingly thinner margin for decision error. There is a requirement to gain insights, foresight, and inferences from the treasure chest of raw transactional data (both internal and external) that many organizations now store (and will continue to store) in a digital format.

Organizations are drowning in data but starving for information. The New York Times article states:

In field after field, computing and the Web are creating new realms of data to explore—sensor signals, surveillance tapes, social network chatter, public records and more. And the digital data surge only promises to accelerate, rising fivefold by 2012, according to a projection by IDC, an IT research firm. . . . Yet data is merely the raw material of knowledge. We're rapidly entering a world where everything can be monitored and measured, but the big problem is going to be the ability of humans to use, analyze and make sense of the data. . . . [Analysts] use powerful computers and sophisticated mathematical models to hunt for meaningful patterns and insights in vast troves of data. The applications are as diverse as improving Internet search and online advertising, culling gene sequencing information for cancer research and analyzing sensor and location data to optimize the handling of food shipments.

An experienced analyst is like a caddy for a professional golfer. The best ones do not limit their advice to factors such as distance, slope, and the weather but also strongly suggest which club to use.

BUSINESS INTELLIGENCE VERSUS ANALYTICS VERSUS DECISIONS

Here is a useful way to differentiate business intelligence (BI) from analytics and decisions. Analytics simplify data to amplify its value. The power of analytics is to turn huge volumes of data into a much smaller amount of information and insight. BI mainly summarizes historical data, typically in table reports and graphs, as a means for queries and drill downs. But reports do not simplify data or amplify its value. They simply package up the data so it can be consumed.

In contrast to BI, decisions provide context for what to analyze. Work backward with the end decision in mind. Identify the decisions that matter most to your organization, and model what leads to making those decisions. If the type of decision needed is understood, then the type of analysis and its required source data can be defined.

Many believe that the use of BI software and creating cool graphs are the ultimate destination. BI is the shiny new toy of information technology. The reality is that much of what business intelligence software tools provide, as just described, has more to do with query and reporting, often by reformatting data. A common observation is: “There is no intelligence in business intelligence.” It is only when data mining and analytics are applied to BI within an organization that has the skills, competencies, and capabilities that deep insights and foresight are created to understand the solutions to problems and select actions for improving business operations and ­opportunities.

Data mining that uses statistical methods is the foundation and precursor for predictive business analytics. For example, data mining can identify similar groups and segments (e.g., customers) through cluster or correlation analysis (see Chapter 4). This allows analysts to frame their analytics to predict how their objects of interest, such as customers, new medicines, new smartphones, and so on, are likely to behave in the future—with or without interventions. This allows predictive analytics to move from being descriptive to ­being prescriptive.

To clarify, BI consumes stored information. Analytics produces new information. Predictive business analytics leverages data within an organizational function focused on analytics and possessing the ­mandate, skills, and competencies to drive better decisions faster, and to achieve targeted performance.

Queries using BI tools simply answer basic questions. Business analytics creates questions. Further, analytics then stimulates more questions, more complex questions, and more interesting questions. More importantly, business analytics also has the power to answer the questions. Finally, predictive business analytics displays the probability of outcomes based on the assumptions of variables.

The application of analytics was once the domain of quants and statistical geeks developing models in their cubicles. However, today it is becoming mainstream for organizations with the conviction that senior executives will realize and utilize its potential value.

HOW DO EXECUTIVES AND MANAGERS MATURE IN APPLYING ACCEPTED METHODS?

Here is an observation on how managers mature in applying progressive managerial methods. Roughly 50 years ago, CEOs hired accountants to do the financial analysis of a company, because this was too complex for them to fully grasp. Today, all CEOs and businesspeople know what price-earnings (P/E) ratios and cash flow statements are and that they are essential to interpreting a business's financial health. These executives would not survive or get the job without this knowledge.

Fast-forward from then to 25 years ago, when many company CEOs did not have computers on their desks. They did not have the time or skill to operate these complex machines and applications, so they had their staff do this for them. Today you will become obsolete if you do not at least personally possess multiple electronic devices such as laptops, mobile phones, tablets, and personal digital assistants (PDAs) to have the information you need at your fingertips.

FILL IN THE BLANKS: WHICH X IS MOST LIKELY TO Y?

Predictive business analytics (PBA) allows organizations to make decisions and take actions they could not do (or do well) without analytics capabilities. Consider three examples:

1. 1. Increased employee retention. Which of our employees will be the most likely next employee to resign and take a job with another company? By examining the traits and characteristics of employees who have voluntarily left (e.g., age, time period between salary raises, percent wage raise, years with the organization), predictive business analytics can layer these patterns on the existing workforce. The result is a rank-order listing of employees most likely to leave and the reasons why. This allows managements' selective intervention.
2. 2. Increased customer profitability. Which customer will generate the most profit from our least effort? By understanding various types of customers with segmentation analysis based on data about them (perhaps using activity-based costing as a foundational analysis), business analytics can answer how much can optimally be spent retaining, growing, winning back, and acquiring the attractive microsegment types of customers that are desired.
3. 3. Increased product shelf opportunity. Which product in a retail store chain can generate the most profit without carrying excess inventory but also not having periods of stock-outs? By integrating sales forecasts with actual near-real-time point-of-sale checkout register data, predictive business analytics can optimize distribution cost economics with dynamic pricing to optimize product availability with accelerated sales throughp...

Table of contents

• Cover
• Contents
• Additional praise for
• Series Page
• Title Page
• Copyright
• Dedication
• Preface
• PART ONE: “Why”
• PART TWO: Principles and Practices
• PART THREE: Case Studies
• PART FOUR: Integrating Business Methods and Techniques
• PART FIVE: Trends and Organizational Challenges
• About the Authors
• Index
• End User License Agreement