Win with Advanced Business Analytics

Creating Business Value from Your Data

Jean-Paul Isson, Jesse Harriott

• Publisher: Wiley
• Year: 2012
• ISBN:
  9781118417089
• Edition: 1
• Topic: Business
• Subtopic: Decision Making
• Index: Business

Plain English guidance for strategic business analytics and big data implementation

In today's challenging economy, business analytics and big data have become more and more ubiquitous. While some businesses don't even know where to start, others are struggling to move from beyond basic reporting. In some instances management and executives do not see the value of analytics or have a clear understanding of business analytics vision mandate and benefits. Win with Advanced Analytics focuses on integrating multiple types of intelligence, such as web analytics, customer feedback, competitive intelligence, customer behavior, and industry intelligence into your business practice.

• Provides the essential concept and framework to implement business analytics
• Written clearly for a nontechnical audience
• Filled with case studies across a variety of industries
• Uniquely focuses on integrating multiple types of big data intelligence into your business

Companies now operate on a global scale and are inundated with a large volume of data from multiple locations and sources: B2B data, B2C data, traffic data, transactional data, third party vendor data, macroeconomic data, etc. Packed with case studies from multiple countries across a variety of industries, Win with Advanced Analytics provides a comprehensive framework and applications of how to leverage business analytics/big data to outpace the competition.

Chapter 1

The Challenge of Business Analytics

“In God we trust, all others bring data.”

—Edward Deming

Those of you who have teenagers in high school living under your roof understand what a transitional life stage this is for your kids. It is a time of many ups and downs, with great memories being created and, in some cases, momentous life struggles beginning. If you don’t have teenagers in your home, imagine for a moment that you have a 17-year-old daughter in high school. She’s a wonderful kid, very personable and outgoing, and excels at most things she attempts. You’re very proud of her—she is on the honor roll, has a lot of nice friends, has the responsibility of an after-school job, has visions of college, and even has a long-term boyfriend of whom you approve. Being a good parent, you also occasionally monitor her computer use and e-mail activity. You notice that she is getting a lot of e-mails from a retailer to encourage her to buy baby and pregnancy-related items and are concerned that the retailer is glamorizing the notion of teen pregnancy and encouraging her to get pregnant. Furious, you storm into the retailer in person, read the manager the riot act, and demand that these e-mails stop. The retailer humbly apologizes and vows to stop the e-mails. Satisfied, you head home and relate entire experience to your teenage daughter. To your surprise, she reveals to you that she is indeed pregnant and is expecting a baby in five months.

According to a New York Times story, this is exactly what happened to a customer of the large retailer Target. Practically speaking, Target’s business analytics activities informed the father that his daughter was pregnant. Specifically, Target statistician Andrew Pole used data-mining techniques to create a “pregnancy predictor” based on online shopping activity. If a customer scored high enough on the pregnancy predictor, Target would send e-mails with offers for pregnancy-related products:

As Pole’s computers crawled through the data, he was able to identify about 25 products that, when analyzed together, allowed him to assign each shopper a “pregnancy prediction” score. More important, he could also estimate her due date to within a small window, so Target could send coupons timed to very specific stages of her pregnancy.

[Pole] ran test after test, analyzing the data, and before long some useful patterns emerged. Lotions, for example. Lots of people buy lotion, but one of Pole’s colleagues noticed that women on the baby registry were buying larger quantities of unscented lotion around the beginning of their second trimester. Another analyst noted that sometime in the first 20 weeks, pregnant women loaded up on supplements like calcium, magnesium and zinc. Many shoppers purchase soap and cotton balls, but when someone suddenly starts buying lots of scent-free soap and extra-big bags of cotton balls, in addition to hand sanitizers and washcloths, it signals they could be getting close to their delivery date.

Take a fictional Target shopper named Jenny Ward, who is 23, lives in Atlanta and in March bought cocoa-butter lotion, a purse large enough to double as a diaper bag, zinc and magnesium supplements and a bright blue rug. There’s, say, an 87 percent chance that she’s pregnant and that her delivery date is sometime in late August.1

Data privacy debate aside, the Target example is a brief illustration of the insights that can be gained through leveraging big data in an effective business analytics practice. If you are reading this book, we assume you see the importance, as we do, of using business analytics to positively affect your organization. You may be a business leader who wants to learn more about how companies use data effectively. You may be an analytics manager who wants to understand pitfalls to avoid that can lead to failure. You may be motivated to learn some of the latest techniques and best practices of how to use different types of information across the enterprise. You may be an analytical professional and want to learn how to take your organization’s analytics to the next level. You may be an HR leader who wants to learn about data across the enterprise so you can decide how best to use it to make strategic human capital decisions. Whatever your motivation for reading this book, we assume your organization has business challenges that you hope data and the practice of business analytics will help you overcome.

Effective business analytics is a focus for business leaders across the globe in ever-increasing numbers. A 2011 report by the McKinsey Global Institute projects that the United States needs 1.5 million more data-literate managers to meet the demands of the data-driven enterprise.2 In addition, during IBM’s 2012 IBM PartnerWorld Conference, its CEO predicted that analytics will be the thread that weaves together front- and back-office systems in order to give companies that harness huge volumes of unstructured data a competitive business advantage.3 Also, a recent International Data Corporation (IDC) report predicts that the business analytics market will grow 8.2% in 2012 to $33.9 billion.4 It is gradually becoming clear that in today’s cut-throat business climate, failing to leverage business analytics effectively in your organization can be the difference between thriving or slow death.

Because business analytics is rapidly evolving and often indicates different things to different people, we think it is important to outline what we mean by “business analytics” for the purpose of this book. We define business analytics as the integration of disparate data sources from inside and outside the enterprise that are required to answer and act on forward-looking business questions tied to key business objectives. We realize this is a fairly broad definition; however, our experience in practicing business analytics, as well as the hundreds of companies that have provided input, indicates to us that business analytics is moving away from an isolated reporting and dashboard mentality and toward an integration of various types of information across the organization in tighter alignment with the business goals of C-level executives.

Even though business analytics is a relatively new field, we see it as having the potential for great organizational impact and importance, much beyond that of the more traditional and isolated reporting function, research department, or “business intelligence”–related activities. Actually, the practice of business analytics is beginning to have a meaningful impact in many companies, some of which we profile in this book.

There are several key components worth noting in our definition that may differ from more traditional definitions of business intelligence, research, Web analytics, information retrieval, data mining, or other related disciplines. First, in our view, effective business analytics must be grounded in key business questions. The amount of data available to businesses is overwhelming and is growing at an exponential rate, and it’s easy to enter analysis paralysis or drift into intellectual curiosities. Therefore, organizations must articulate and prioritize the key questions they want business analytics to answer.

Second, we believe that business analytics has the most impact on the organization when it is forward looking—not backward looking. In other words, business analytics is most useful when it is predictive and provides a lens into the future regarding likely business outcomes.

Third, to us, the new age of business analytics requires the integration and synthesis of various information disciplines across the organization, such as marketing research, Web analytics, business reporting, competitive intelligence, customer data, and outside data sources, among others, in order to be effective. If you recall, from our definition, all effective business analytics should be grounded in key business questions and objectives. Those business questions and objectives do not care about your organization’s structure—that some of the data are in finance, some are in marketing, and some are in product. Those business questions simply demand an answer, and whichever organization can answer them consistently, with speed and accuracy, will win. Will that be you or your competition?

THE CHALLENGE FROM OUTSIDE

We see several business challenges that led up to the newfound focus on business analytics, as well as several challenges that business analytics must rise to meet.

We all know that the economic environment has been more intense and challenging than ever before. At the time of this book’s writing, the global economy is still on unsure footing, consumers are still being conservative about their spending, the real estate market has not fully recovered, and businesses are struggling to understand how to grow effectively, yet profitably. In the first quarter of 2012, the chairman of the Federal Reserve, Ben Bernanke, was still predicting only modest growth during 2012, expecting economic and job growth to remain somewhat muted through the remainder of 2012.5 Those companies that identify with the Fed’s cautious outlook see the economic glass as half-empty and are trying to hold market share, stem losses, and keep their current customers happy.

Yet business and consumer confidence is showed signs of improvement during 2012, and the long-term payroll data trend from the Bureau of Labor Statistics indicates that companies have started to create new jobs. Therefore, optimistically minded companies are eagerly trying to be smart about staying ahead of business trends, as well as about how to capture some of the impending economic growth. Regardless of whether your future business outlook is optimistic or pessimistic, effective business analytics is becoming a required component of business success.

Another business challenge driving the increased importance of business analytics is that business competition has become more intense. It’s easier to start a business with little capital and, in some cases, gradually disrupt an entire industry or invent a new one. Take the case of Amazon, the well-known online retailer based in Washington State. Started in 1994, it spurred the rise in the online purchase of books and music and was, in part, responsible for the relatively rapid decline of bricks-and-mortar stores in the book and music industries. These types of examples should motivate most organizations to acquire as much data about competitors and their industries as possible.

Part of addressing competitive threats is to monitor and stay one step ahead of your competition—tracking, analyzing, and integrating everything you know about your competitors into the analytics of your own company. For example, do you know your market share trend over time, the strategies and tactics your competitors use to sell to customers, how your products are perceived compared to theirs, which of your customer segments are more likely to defect to the competition, or why some customers use only your competition and not you? If your organization has timely and thorough answers to these types of questions, then bravo. Many companies rely on informal feedback about the competition and do not have solid analytical systems in place to address these issues.

Another business challenge that’s leading to an increase in companies relying on business analytics to drive their strategy is that customers are becoming more fickle, and loyalty to products and services is rarer than ever before. Mark Ratekin from Walker Information Group, a respected leader in the measurement of customer loyalty, indicated, “We, too, have seen evidence of a shift in customer sentiment toward more of the High Risk category. Interestingly, there is a similar trend starting to occur among employees—more and more employees are becoming less engaged, and are planning to look for new work when the recession ends.”6 The decline in employee loyalty is also seen to be affecting the quality of the service provided to customers. Given all of this, it’s extremely crucial for businesses to understand customer issues, such as what drives purchase intent, purchase preference, and purchase behavior. Doing this without systematic analytics and voice-of-the-customer input is almost impossible—unless you have only one or two customers. In that case, you may have business challenges to address beyond just analytics.

Given intense business competition, existing companies must continually monitor their customers’ behaviors and feedback, remaining on guard for new entrants into the marketplace. Companies are under great pressure to continually and rapidly reinvent themselves and how they offer value to customers, and failing to accurately listen to customers and track their behavior often results in certain and swift demise. Take the case of Polaroid, the well-known brand of instant photographic equipment that failed to capitalize on the growing trend of digital photography. Polaroid was founded in 1937 by Edwin Land and was one of America’s early high-tech success stories. The catapult of its success was the invention of camera film in 1948 that developed a photograph in minutes—much faster than other methods at the time. This competitive strategy was successful for Polaroid through 2001, when Polaroid filed for bankruptcy due to the rapid decline in the sale of photographic film. The irony is that Polaroid had been investing heavily in digital photography technology and was actually a top seller of digital cameras into the late 1990s. Yet although Polaroid invested a lot in technology R&D, the company failed to take a business analytics approach and understand that customers were relying more on storing digital photos on their computers, rather than printing a paper copy of each picture. If Polaroid had integrated accurate voice-of-the-customer input and customer analytics into its business analytics strategy at the senior executive level, it may have been able to adapt its strategy away from photographic print film and toward a successful digital photography play.

With customer loyalty elusive, the number of sales and marketing messages seen by your customers is also ever-increasing and is another business challenge driving the importance of business analytics. In the United States, marketers send more than 90 billion pieces of direct mail each year, trying to influence the behavior of customers.7 Also, the Radicati Group estimates that nearly 90 trillion e-mails are sent each year, and certainly a large percentage of these are from businesses trying to get your customers to try their products.8 Furthermore, eMarketer expects that U.S. online advertising spending will grow 23.3% to $39.5 billion during 2012, pushing it ahead of advertising spending in print newspapers and magazines.9 In terms of traditional media, according to Media Dynamics, a media research group, the average American is exposed to a minimum combined total of 560 advertisements each day from radio, print, and television.10 At the same time that...

Table of contents

• Cover
• Contents
• Title
• Copyright
• Dedication
• Preface
• Acknowledgments
• Chapter 1: The Challenge of Business Analytics
• Chapter 2: Pillars of Business Analytics Success
• Chapter 3: Aligning Key Business Challenges across the Enterprise
• Chapter 4: Big and Little Data
• Chapter 5: Who Cares about Data?
• Chapter 6: Data Visualization
• Chapter 7: Analytics Implementation
• Chapter 8: Voice-of-the-Customer Analytics and Insights
• Chapter 9: Leveraging Digital Analytics Effectively
• Chapter 10: Effective Predictive Analytics
• Chapter 11: Predictive Analytics Applied to Human Resources
• Chapter 12: Social Media Analytics
• Chapter 13: The Competitive Intelligence Mandate
• Chapter 14: Mobile Analytics
• Chapter 15: Effective Analytics Communication Strategies
• Chapter 16: Business Performance Tracking
• Chapter 17: Analytics and Innovation
• Chapter 18: Unstructured Data Analytics
• Chapter 19: The Future of Analytics
• About the Authors
• Index