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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Serie: SAS Institute Inc
- Sprache: Englisch
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.
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Seitenzahl: 599
Veröffentlichungsjahr: 2012
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Contents
Preface
Acknowledgments
Chapter 1: The Challenge of Business Analytics
The Challenge from Outside
The Challenge from Within
Chapter 2: Pillars of Business Analytics Success
Business Challenges Pillar
Data Foundation Pillar
Analytics Implementation Pillar
Insight Pillar
Execution and Measurement Pillar
Distributed Knowledge Pillar
Innovation Pillar
Conclusion
Chapter 3: Aligning Key Business Challenges across the Enterprise
Mission Statement
Business Challenge
Identifying Business Challenges as a Consultative Process
Identify and Prioritize Business Challenges
Analytics Solutions for Business Challenges
Chapter 4: Big and Little Data
Big Data
Little Data
Laying the Data Foundation: Data Quality
Data Sources and Locations
Data Definition and Governance
Data Dictionary and Data Key Users
Sanity Check and Data Visualization
Customer Data Integration and Data Management
Data Privacy
Chapter 5: Who Cares about Data?
The IMPACT Cycle
Curiosity Can Kill the Cat
Master the Data
A Fact in Search of Meaning
Actions Speak Louder Than Data
“Eat Like a Bird, Poop Like an Elephant”
Track Your Outcomes
The IMPACT Cycle in Action: The Monster Employment Index
Chapter 6: Data Visualization
Convey Meaning
Objectivity: Be True to Your Data
Necessity: Don’t Boil the Ocean
Visual Honesty: Size Matters
Imagine the Audience
Nimble: No Death by 1,000 Graphs
Context
Encourage Interaction
Conclusion
Chapter 7: Analytics Implementation
Analytics Implementation Model
Vision and Mandate
Strategy
Organizational Collaboration
Human Capital
Metrics and Measurement
Integrated Processes
Customer Experience
Technology and Tools
Change Management
Chapter 8: Voice-of-the-Customer Analytics and Insights
Customer Feedback Is Invaluable
The Makings of an Effective Voice-of-the-Customer Program
Strategy and Elements of the VOC System
Common VOC Program Pitfalls
Chapter 9: Leveraging Digital Analytics Effectively
Strategic and Tactical Use of Digital Analytics
Understanding Digital Analytics Concepts
Digital Analytics Team: People Are Most Important for Analytical Success
Digital Analytics Tools
Advanced Digital Analytics
Digital Analytics and Voice of the Customer
Analytics of Site and Landing Page Optimization
Call to Action: Unify Traditional and Digital Analytics
Chapter 10: Effective Predictive Analytics
What Is Predictive Analytics?
Unlocking Stage
Prediction Stage
Optimization Stage
Diverse Applications for Diverse Business Problems
Financial Service Industries as Pioneers
Chapter 11: Predictive Analytics Applied to Human Resources
Staff Roles
Assessment: Beyond People
Planning Shift
Competency versus Capability
Production
HR Process Management
HR Analysis and Predictability
Elevate HR with Analytics
Value Hierarchy
HR Reporting
HR Success through Analytics
Chapter 12: Social Media Analytics
Social Media Is Multidimensional
Understanding Social Media Analytics: Useful Concepts
Is Social Media about Brand or Direct Response?
Social Media “Brand” and “Direct Response” Analytics
Social Media Tools
Social Media Analytical Techniques
Social Media Analytics and Privacy
Chapter 13: The Competitive Intelligence Mandate
Competitive Intelligence Defined
Principles for CI Success
Chapter 14: Mobile Analytics
Understanding Mobile Analytics Concepts
How Is Mobile Analytics Different from Site Analytics?
Importance of Measuring Mobile Analytics
Mobile Analytics Tools
Business Optimization with Mobile Analytics
Chapter 15: Effective Analytics Communication Strategies
Communication: The Gap between Analysts and Executives
An Effective Analytics Communication Strategy
Analytics Communication Tips
Communicating through Mobile Business Intelligence
Chapter 16: Business Performance Tracking
Analytics’ Fundamental Questions
Analytics Execution
Business Performance Tracking
Analytics and Marketing
Chapter 17: Analytics and Innovation
What Is Innovation?
What Is the Promise of Advanced Analytics?
What Makes Up Innovation in Analytics?
Intersection between Analytics and Innovation
Chapter 18: Unstructured Data Analytics
What Is Unstructured Data Analytics?
The Unstructured Data Analytics Industry
Uses of Unstructured Data Analytics
How Unstructured Data Analytics Works
Why Unstructured Data Is the Next Analytical Frontier
Unstructured Analytics Success Stories
Chapter 19: The Future of Analytics
Data Become Less Valuable
Predictive Becomes the New Standard
Social Information Processing and Distributed Computing
Advances in Machine Learning
Traditional Data Models Evolve
Analytics Becomes More Accessible to the Nonanalyst
Data Science Becomes a Specialized Department
Human-Centered Computing
Analytics to Solve Social Problems
Location-Based Data Explosion
Data Privacy Backlash
About the Authors
Index
Additional praise for Win with Advanced Business Analytics
“JP Isson and Jesse Harriott are outstanding leaders in this field. If you want to succeed with analytics, you must read this book!”
—Bruno Aziza, Vice President of Marketing, SiSense, and coauthor of Drive Business Performance: Enabling a Culture of Intelligent Execution
“In today’s ultra-competitive world, leveraging analytics to help companies manage their path forward is a must. Win with Advanced Business Analytics does a great job of providing an understanding of business analytics and creating a framework to build upon. The authors bring great experience and knowledge to help explain a critical and complex topic.”
—Larry Freed, CEO, ForeSee, and author of Managing Forward: How to Move from Measuring the Past to Managing the Future
“At LifeCare, we are passionate about using analytics to drive innovation and business growth. This book provides executives and managers with useful guidance for using business analytics to drive results. I recommend reading and applying the concepts and frameworks of Win with Advanced Business Analytics.”
—Doug Klinger, CEO, LifeCare
“Win with Advanced Business Analytics provides a blueprint for how analytics can impact the bottom line of an organization and helps business leaders make the most of their analytics investments. It’s a must-read in today’s big data environment.”
—Michael Krauss, @ C Level Columnist, Marketing News, and President, Market Strategy Group
“In today’s world, an analytical, data-driven approach to business is table stakes. Win with Advanced Business Analytics has been written for those of us who need an introduction or an update on the state-of-the-art/science. It is comprehensive and practical. It suggests approaches and concrete real-life solutions to complex business challenges while remaining nontechnical and fun to read. A great reference for today’s decision makers!”
—Louis Gagnon, Chief Product and Marketing Officer, Yodle
“A great overview of analytics and best practices for the business leader. It will be a critical resource for anyone looking to get the most out of their analytics initiatives and is a must-read for both newcomers and seasoned vets.”
—Raj Aggarwal, CEO, Localytics
“Finally, a book that provides straightforward, accessible, and proven approaches to dealing with the massive amount of information collected by companies. Isson and Harriott give managers the analytical tool not only to see the forest from the trees but also to capitalize on the picture.”
—Stéphane Brutus, PhD, Professor, Department of Management, John Molson School of Business, Concordia University
“Business analytics focuses on developing new insights and understanding of business performance based on concrete data and statistical methods. This book is a good guide to help you compete and win with advanced analytics.”
—Jean-Marc Leger, President and CEO, Leger Marketing
“Win with Advanced Business Analytics offers the business leader a clear path of how analytics can impact the organization.”
—Joe Carvelli, CEO, Retail Ingenuity
“Whether you work in marketing research, customer relationship management, social media, customer experience management, web analytics, or have to use all of these inputs in developing your organization’s strategic planning efforts, this book will open your eyes. The authors present an integrated approach to customer and competitor information analysis. They define the domain, explain the disparate functions, give great and timely company examples, and tie it all together. This book is certain to cause more than one company to reorganize how it approaches analytics.”
—Roger Baran, Professor of Marketing, DePaul University
“Retail is a highly competitive industry, and part of our success is due to how we leverage big data and analytics. Win with Advanced Business Analytics is a key resource for leaders looking to gain insight and direction regarding how data assets can be used to impact the bottom line of their organization.”
—Scott Bracale, President, Tween Brands Agency Inc., d/b/a Justice
“This book is great for managers and students who would like to learn how to apply advanced business analytics. The conceptual framework and case studies presented here are a must-read.”
—Minha Hwang, PhD, Assistant Professor of Marketing, McGill University
WILEY & SAS BUSINESS SERIES
The Wiley & SAS Business Series presents books that help senior-level managers with their critical management decisions.
Titles in the Wiley and SAS Business Series include:
Cover images: Architecture image, © Maciej Noskowski/iStockphoto; Pie chart image, © Anton Balazh/iStockphoto
Cover design: John Wiley & Sons, Inc.
Copyright © 2013 by Jean Paul Isson and Jesse Harriott, PhD. All rights reserved.
Published by John Wiley & Sons, Inc., Hoboken, New Jersey.
Published simultaneously in Canada.
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Wiley publishes in a variety of print and electronic formats and by print-on-demand. Some material included with standard print versions of this book may not be included in e-books or in print-on-demand. If this book refers to media such as a CD or DVD that is not included in the version you purchased, you may download this material at http://booksupport.wiley.com. For more information about Wiley products, visit www.wiley.com.
Library of Congress Cataloging-in-Publication Data:
Isson, Jean Paul, 1971-
Win with advanced business analytics : creating business value from your data/Jean Paul Isson, Jesse Harriott.
p. cm.
Includes index.
ISBN 978-1-118-37060-5 (cloth); ISBN 978-1-118-41708-9 (ebk.); ISBN 978-1-118-42051-5 (ebk.); ISBN 978-1-118-43428-4 (ebk.)
1. Business planning. 2. Industrial management—Statistical methods. I. Harriott, Jesse. II. Title.
HD30.28.I83 2013
658.4′038—dc23
2012026662
I dedicate this book to Roxane, my daughter, who, despite her young age, was old enough to understand that I was not available as usual and managed to keep busy with her own stuff, giving me time to write. Daddy is finished writing and hopes when you will read this book you will be proud of your patience. A special thanks to Nathalie, who is always supporting me 100 percent in all I do. Thanks, Nat, for being there with our lovely daughter.
—JP Isson
I dedicate this book to my wife, Evelyn, for all her love, support, and great feedback during the writing of each chapter and to my wonderful young children, Jesse and Eva, for their patience and understanding while Daddy sat in front of a computer for days at a time.
—Jesse Harriott
Preface
Many people have an interest in analytics right now; it’s an undeniably “hot” area. After having practiced analytics for 20 years, we are seeing the technology finally begin to catch up to the analytical techniques. We are also seeing a wide variety of organizations and non–analytically trained business people gaining an interest in analytics. Analytics is definitely going mainstream. We are also seeing a lot of books written on analytics, from technical manuals on how to do analytics to works written to help you kick-start your organization’s efforts in a particular analytical area, such as Web analytics.
We wrote this book to be different from the available books on analytics, and we are glad you have chosen to read it. The motivation for writing this book comes, in part, from the lack of resources we found that can help a business leader create value from, and make the most of, his or her organization’s analytical assets. In other words, this book will help you think about analytics across your organization, will help you evaluate whether you are doing analytics well, and will provide you with frameworks to take your analytics to the next level, creating economic value for your organization in the process. It is not a technical book and is written to be relevant to someone with no analytical experience, as well as to the person with a great deal of analytical experience. Also, unlike many of the analytics books out there, we each have about 20 years of practical analytical leadership experience in more than 50 countries. As such, this is not a book written in an ivory tower. We have been through what we write about in the book, have overcome the pitfalls we outline, and have created successful analytical solutions for a wide variety of global business situations.
The focus of this book is on advanced analytics and how companies can create business value from their data assets. By advanced analytics, we mean analytics that starts with a business goal or question, integrate disparate data sources together, create a prediction for the future, and lead to business actions with measurable results. We provide numerous advanced analytics examples throughout the book, with an eye toward real-world examples that will be of interest to a business leader, as well as to practicing analytical professionals.
One of the key trends we are seeing emerge and that we have incorporated into this book is the integration of previously unrelated data assets together in the organization. For example, it used to be that marketing research was siloed in the marketing department, user-experience research was in the product development part of the organization, Web analytics in the technology group, and customer analytics in yet another part of the organization. We are slowly starting to see these barriers break down within companies. Organizations are realizing that key business questions often cut across departmental silos and that richer and deeper analytics will help an organization compete more effectively. In some companies, this means the formation of a centralized analytics organization; in others, it may not mean a formal organization but instead an informal center of excellence. To reflect this trend, we cover areas of analytics not often seen together in one book. This is an effort to expose you to areas of analytics that may be new, as well as show you how they relate to one another and outline the information areas you need to think about as you create your own strategic analytics endeavor.
This book is written such a way that each chapter builds on the last, but each chapter can be read by itself as well. You will get more out of the book if you read it from beginning to end, but if you are interested in quickly learning about mobile analytics, for example, you can jump right to that chapter. Regardless, we encourage you to start with Chapters 1 and 2, which provide the foundation for the book, as well as outline one of our key frameworks, the Business Analytics Success Pillars.
We are confident that if you follow the principles contained in this book, you will develop a high-impact analytics function and will generate economic value from data for your organization. Many of the practices we outline are not easy to accomplish, but whether you are in a large company or a small one, you can apply your vision for advanced analytics and create business value from your data.
Acknowledgments
We engaged hundreds of analytical business leaders to help in the writing of this book. Whether through interviews, formal contributions, or informal collaboration, we are indebted to many for helping us complete this book. You will see many of their contributions throughout the book, in the form of useful insights in their quotes and concrete examples of how they make analytics work. A few even wrote an entire chapter in their area of expertise. We are thankful to have such esteemed colleagues in the field of analytics; the impact of this book would not be as great were it not for their input. There are too many to list, but we would like to especially acknowledge Dr. Abby Mehta, Dr. Jac Fitz-enz, and Judah Phillips for contributing excellent chapters in their respective areas of expertise. We would also like to thank Sims Hulings, Stephen Kaufer, Elise Amyot, Avinash Kaushik, Steve Krichmar, Alex Yoder, Jon Lehto, Steve Pogorzelski, Sudeep Haldar, Joseph Arsenault, Jim Tincher, Mark McKenna, Tim Ruth, Amy Quigley, Jonathan Mendex, Amel Arhab, Justin Cutroni, Chris Krohn, Karem Tomak, Raj Aggarwal, Josh Chasin, Dr. Latha Palaniappan, Eric Wong, Seth Grimes, and Chris Musto for writing about some of the helpful examples and providing insightful quotes that can be found throughout this book.
JP Isson is especially grateful to Marjorie Bayard for her insightful discussions and support during some major keynote speeches he delivered in Chicago and Las Vegas, keynotes that helped lead to the idea of writing this book. Their early morning discussions and inspiring late-night talks have finally been brought to life.
We also want to thank the reviewers and the friends who supported us during this endeavor and provided valuable feedback during the writing of this book. Pat Turgeon, despite his busy schedule at Figures, was instrumental in reviewing some chapters and generously gave us great feedback and input. Kim Lascelles graciously reviewed the original book proposal, as well as some chapters of the manuscript, offering excellent input along the way. We appreciate Elise Amyot not only for contributing a case study from CMPA but also for reviewing some chapters of the book. We are very grateful to Shelley Sessoms from SAS for quickly seeing great potential from the proposal and fast-tracking the project to a book. In addition, we appreciate Stacey Hamilton from SAS and Sheck Cho and Kimberly Monroe-Hill from John Wiley & Sons, for helping to manage the publication of this book within a very aggressive time frame. We thank Doug Hardy as well, for his continued insight into, and support of, the writing process.
This book would have never been completed without the support and love of our families. A special thanks to them all.
We would like to thank all of our friends and colleagues who helped inspire many of the concepts in this book: Louis Gagnon, Mario Bottone, Mike Nethdercotts Martijn Mengerink, Fedel Chbihna, Karim Salabi, Alfonso Troisi, Caroline Apollon, Raymonde Beaudoin, Oumar Mbaye, Ellen Julian, Deanna Hampton, Karima Arhab, Mark Bienstock, Ezana Razwork, Diawo Diallo, Francois To, Jeff Quinn, Sean Dalton, Sunday Eboala, Paul Jamieson, Eugene Robitaille, Keke Wu, John McLaughlin, Peter Anastacio, Kim Vu, J. W. Milon, Marjolaine Boisvert, and Stephane Britus, just to name a few, because the list is endless. We would also like to thank all of our analytics colleagues at Monster Worldwide for their ongoing implementation of effective advanced business analytics in more than 50 countries around the globe and for embracing the business challenges of analytics. We want to thank all of you for your input, as well as for helping us implement the analytics solutions contained in this book.
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 this sales and marketing onslaught pervades our daily lives, the customer’s attention span is shrinking, with customers seeking to avoid marketing messages through the use of digital video recorders that can skip ads, e-mail spam blockers, do not call lists, do not mail lists, and other techniques to avoid being exposed to your message. Given these challenges, the world of multichannel customer acquisition requires the effective use of business analytics to untangle the complex patterns of brand and product perception that arise from being exposed to so many marketing messages from so many channels.
At this same time, the promise of new media to help businesses grow and ensure success has reached somewhat hysterical proportions and is another business challenge leading to the importance of business analytics. Mobile usage continues to increase dramatically on a global basis, as does the use of social media and other online content, such as micro blogs. You can even call mobile and social media mainstream media at this point. At the end of 2011, there were roughly 6 billion mobile phone subscriptions worldwide, with some users having service on more than one device.11 According to the Direct Marketing Association, 36% of consumers now follow brands on social media platforms.12 Also, the number of social media users age 65 and older grew 100% during 2010, so now one in four people in that age group is part of a social networking site.13 As an example, one of the authors of this book, Jesse Harriott, has a 94-year-old grandmother who recently purchased a cell phone and started searching Facebook to find people she knows.
This new media is taking a lot of the friction out of learning about a product and about choosing a company brand. Yet with the increase of new media and the multitude of ways to interact online comes a flood of new data into the organization. Every interaction someone has with your brand or product in an electronic medium, such as an Internet search engine, a website, a social media platform, an electronic coupon provider, a blog post, or a mobile device, generates a data trail. Other interaction points are also growing and generating massive amounts of data in their wake. For example, there are unknown quantities of digital tracking sensors in shipping crates, electric meters, automobiles, industrial equipment, and various other devices. In addition, GPS, wifi, and Bluetooth position tracking by mobile devices is widespread and generates massive streams of location data that companies are beginning to harness.
Given that economic pressures remain, that business competition is more intense than ever, that customer loyalty is all but gone, and that new media usage is on the rise, it is no surprise that the use of business analytics is gaining new prominence. These are the challenges for the business analytics discipline, the challenge to help organizations thrive and prosper. It’s clear that using effective business analytics is seen as a way to address these key outside business challenges and that business analytics holds great promise to help you understand what your customers want from you, figure out how to acquire new ones, and learn what will lead to a repeat purchase. Yet most organizations we speak with are struggling to make sense of what these data can tell them or how they can use it. Therefore, we have designed this book to help businesses think about, organize, and make the most of the data assets available to them. Throughout this book, we provide examples of companies that are doing it well, along with some that are not.
THE CHALLENGE FROM WITHIN
Whatever your specific outside challenges driving you toward business analytics, there are also challenges for analytics inside the organization. In other words, how do you unleash the power of analytics to address the business challenges that are most critical to your organization, while overcoming typical pitfalls inside your company? If you could only find that brilliant data scientist and woo him or her into your organization, then everything would be all right, and your company could do brilliant things with its data. That one genius could help you segment your market effectively, increase your number of customers, reduce the customer attrition rate, predict what will make new customers buy, predict online customer behavior, and increase your company market cap by 30%, right?
Wrong. Certainly, smart and knowledgeable staff is important in helping you make good use of your data—but that is nowhere near enough. Several other challenges from within your organization need to be addressed before you can reach data nirvana using brilliant data scientists. This book is designed to help you address those internal challenges, but first, let’s outline a few of them.
To illustrate some of the internal challenges to business analytics success, let’s take the case of executives we spoke with at a company as part of the background research for this book. Out of respect for the company, we won’t name it; however, let’s just say it is a fairly well-known media company. Executives at this media company expressed some analytical angst to us during our interview. They said they realized a few years ago that their unstructured data were an untapped resource to help their business strategy, as well as help their customers. So they went searching for someone with the requisite degrees and experience who could lead the work with their data to help them unleash the data’s potential. They searched for seven months (these people are in demand) and finally found someone with a statistics degree, computer science experience, great references, and a solid track record of helping well-known brands analyze their data. They hired him and put the existing seven analysts already at the company under his management. They were very optimistic with their new key hire and set him immediately to work on analyzing customer segments with a large average order size and a long tenure, versus those without, in order to understand how to better target prospective sales and marketing that would yield profitable relationships with a solid customer lifetime value. They said everything started off well at first—the team was optimistic and energized with its new team member. However, problems gradually started to develop. First, the analytics team went away for weeks at a time, with little data analysis completed, and then when something was delivered, it was usually lots of raw data and a graph or two, all of which were difficult for the businesspeople to understand. Second, the new team occasionally provided stats that were in conflict with other analytics teams in the company or what had been common company wisdom in the past—setting off ill will between departments and spates of dueling data that often took weeks to untangle. Next, it seems as if the analysts would occasionally come out with numbers that were different from the analysis they had provided just a few months earlier, which frustrated the business to no end.
The executives at the company attributed these challenges to the difficulty of doing analytics and tended to blame the analytics team for the problems. As a result of our interview, however, they gained an expanded view that it was very likely that the overall organizational dynamics within the company may have been the cause of their analytical team’s difficulties.
First, we asked what company leadership sponsored the hiring and formation of this analytics team. It was explained to us that a long-tenured VP of marketing commissioned this initiative, and everyone had great faith that she could make the best use of these analytical resources. When we followed up regarding whether the most senior corporate or functional leaders were also in favor of forming this team, we were told that they were not completely sure, because no one beyond the SVP whom the marketing VP reported to was consulted. This illustrates our first internal challenge that business analytics must overcome—weak executive sponsorship. Unless a senior leader within the organization is a driving force behind business analytics and is aware, supports, and believes in the mission of the business analytics discipline over the long term, then it will likely have difficulties thriving and may fail eventually, due to shifting corporate priorities, company politics, and lack of corporate accountability.
Second, we asked what process the company had undergone to make sure its corporate business objectives were in line with the objectives of this new analytics team. We uncovered that the executives didn’t really communicate corporate objectives to the new analytics leader or his team, because they thought the team simply needed to analyze data and not worry too much about corporate priorities. This illustrates the second internal challenge that a business analytics function must overcome: failure to communicate and align business analytics priorities against corporate priorities.
Third, we noted that surely technology systems and resources were required to help the analytics function do its work, so we asked how the analytics team worked with the technology team that supported these analytics initiatives. For example, did the technology resources report in to the new analytics team? Was there a direct line of accountability in some other way? We were told that the company did not set up any formal arrangement but relied on the new analytics manager to build a bridge and work across the departments. This illustrates our third internal challenge that the practice of business analytics must overcome: weak alignment and lack of accountability from the technology support function.
Next, we asked whether there was any data quality or governance function within the company to ensure that definitions were standardized and data were accurate. We were told no, but that it was the analytics team’s responsibility to make sure that whatever data and analysis were distributed were accurate and reliable. This leads us to the fourth internal challenge: lack of formal data governance. It takes dedicated and diligent effort from business and technology to ensure that data being published from various systems is accurate and reliable, and this cannot be merely an afterthought by a few analysts simply because they happen to be last in the chain of data distribution.
Then we asked how the new analytics team’s activities were rationalized against the activities of other analytics departments, such as product, service, finance, or strategy teams. We were told that they did not really communicate with one another formally and didn’t initially think it was necessary because those teams were working on different analytical tasks. This illustrates the fifth internal challenge: weak alignment of existing analytical resources within an organization. We explained that in order to reduce the likelihood of a duplication of efforts and of dueling data, as well as to ensure that the company is leveraging the collective knowledge of the analytical resource most effectively, there must be some type of formal alignment across analytical teams. That can take the form of a reporting relationship to a single manager or simply a formal communication and management cadence across different analytical teams throughout the enterprise. The right solution depends on corporate culture and maturity and is definitely open for debate, as we have seen both work well under different circumstances. There are several ways to overcome this challenge, and we will outline each later in the book.
Many internal challenges will crop up on the way. These are just some of the internal challenges a business analytics function must rise to meet in order to become business relevant, fast, insightful, and predictive; have a bias toward action; and become part of the corporate culture. We don’t claim this book will solve all of these issues for everyone. Yet we know that the best practices, lessons learned, and assessment tools within will go a long way toward helping you make sure your business analytics is world-class.
This book is organized in such a way as to help you build on your knowledge as you read from chapter to chapter. We have also attempted to define and organize the chapters so that they can stand on their own. For example, if you are primarily interested in learning about how companies effectively use Web analytics across the enterprise, you can jump to Chapter 9, “Leveraging Digital Analytics Effectively.” However, if you want to learn about how to successfully evolve an analytics function, then we suggest you read the chapters in order and ask yourself hard questions about whether your company is doing everything it can to win with advanced business analytics.
NOTES
1. Charles Duhigg, “How Companies Learn Your Secrets,” New York Times, February 16, 2012.
2. James Manyika, Michael Chui, Brad Brown, Jacques Bughin, Richard Dobbs, Charles Roxburgh, and Angela Hun, Big Data: The Next Frontier for Innovation, Competition, and Productivity (McKinsey Global Institute, 2011).
3. Ginni Rometty, keynote address speech, IBM Partner World Presentation, New Orleans, February 27–March 1.
4. IDC (January 2012), Worldwide Business Analytics Software Tracker.
5. Ben Bernanke, testimony to U.S. Congress, regarding the economic outlook for the remainder of 2012, Washington, DC., February 29, 2012.
6. Mark A. Ratekin, “What Is the Current State of Customer Loyalty?” Walker Information Group Blog, March 2011.
7. 2011 Statistical Fact Book (New York: Direct Marketing Association, 2011).
8. Radicati Group, April 2010.
9. eMarketer, January 2012.
10. “Our Rising Ad Dosage,” Media Matters, February 15, 2007.
11. International Telecommunication Union, November 2011.
12. 2011 Statistical Fact Book.
13. “Boomers Joining Social Media at Record Rate,” CBS News, November 15, 2010.
Chapter 2
Pillars of Business Analytics Success
The BASP Framework
“Great things are not done by impulse, but by a series of small things brought together.”
—Vincent Van Gogh
Chapter 1 introduced some of the challenges business analytics faces, from both outside and inside the organization. We are sure that by now you have gained a new appreciation for why some analytics initiatives are difficult to get off the ground or why others languish within the organization. In this chapter, we propose and outline a conceptual framework for successfully implementing business analytics in any organization so that your analytics initiatives will flourish and bring positive return on investment to your firm.
Our emphasis in this book is on practical solutions that have shown themselves to work in successful analytically focused organizations. The framework we developed is based on research we conducted with analytics leaders, as well as on our own practical experience, with each of us having been in this field for 20 years. That being said, any framework is merely a starting point for your organization’s unique circumstances. Therefore, we encourage you to think about how this framework may be uniquely applied to your company, and we welcome your feedback or stories regarding how the framework has affected your organization.
We created the framework with the intention that it be simple and straightforward, yet have deep complexity beneath the surface, such that it applies to a broad range of organization challenges and situations. We hope the framework will inspire business analytics creativity, as well as heated debate. We believe the framework is responsive to the needs of business analytics customers, as well as business analytics creators. We know it’s a framework a CEO can get behind, yet the individual analyst or manager can also use it as a blueprint to take analytics to the next level at her organization.
This chapter is merely an introduction to the elements of our framework. We have provided key practical examples throughout the book, as well as some dedicated chapters on important aspects in order to illustrate the concepts in the framework fully.
Before reviewing our framework, we think it is important to cover the Five Stages of Analytical Maturity that are required in order to move toward being an analytical competitor. The Analytical Maturity model was developed by Tom Davenport, a pioneer in the use of information and analytics effectively across the enterprise, and his coauthor Jeanne Harris in their 2007 work, Competing on Analytics.1 Their outline to becoming an analytical competitor involves five stages, with Stage 5 being the most advanced.
Stage 1 is labeled “analytically impaired” and is reflective of a company that has some data and management interest in analytics, yet no real center of excellence or organized capability.
Stage 2 is labeled “localized analytics” and reflects an organization where some isolated managers may support leveraging analytics, but there is no formal enterprise-wide effort or recognition at the senior-most level regarding the importance of analytics.
Stage 3 is labeled “analytical aspiration” and has some executive level sponsorship regarding the importance of analytics, and some organizational structure and effort have been put in place to leverage analytics within the enterprise. However, analytics is typically siloed to a few areas of the organization and lacks standards, support, and consistency.
Stage 4 is called the “analytical company” and involves a company-wide analytics priority that is actively under development, has the support of top executives, and has some standards and systems consistency.
In Stage 5, an “analytics competitor,” the organization has consistent standards and practices, has thorough data integrity, and routinely capitalizes on all of the business benefits of its enterprise-wide analytics focus and capability.
The framework we developed for this book is designed to move your organization rapidly through the five stages and into becoming a world-class center of business analytics. The conceptual framework is especially helpful to organizations that have expressed interest in making analytics a priority, have made some organized analytics efforts where senior leaders understand the benefits of analytics, and are attempting to push themselves further into Stage 5.
Based on our experience and research and through interviewing other analytics leaders, we have noticed several common themes regarding companies that are successful with business analytics initiatives versus those that are not successful. From this knowledge, we created the framework that we call the Business Analytics Success Pillars (BASP). The BASP captures the key activities and similarities that thriving and successful business analytics functions share. The BASP can be used by the analytics leader as a self-check on what is being done well versus what is not done well. The BASP can also be used by the senior business leader to assess what is working with the analytics functions and what is not.
The BASP framework contains seven pillars that we believe are critical to successful business analytics implementation (see Exhibit 2.1). The pillars are not necessarily going to be followed in a particular order, because some organizations may be strong in one pillar but weak in others. For example, your organization may have a great culture of internal communication and have very little organizational friction when it comes to communicating information across business units or geographies—so the pillar related to communication may not need as much work. Conversely, your company may be very weak in the data foundation, with little integration or standardization of data sources across the enterprise—so a lot of your effort may need to be spent there. Regardless of your specific situation, the pillar framework can be thought of as being similar to the foundation of a house—you need all of the areas of support in order to make the house stand strong and not collapse. Therefore, the goal of the BASP framework is to focus your organization’s attention on those areas that are key to business analytics success and will lead to the greatest return on investment.
Exhibit 2.1 BASP Framework
BUSINESS CHALLENGES PILLAR
In today’s challenging business environment, professionals across all industries are being tasked with doing more with less, with limited time and resources to allocate toward individual business analytics initiatives. This makes prioritization imperative and means that a crucial step of any business analytics implementation requires clear understanding of organizational objectives, or “business challenges,” to ensure that any solution is aligned with, and addresses, the company’s biggest or most pressing needs. This is why we have the first pillar of the BASP framework as “business challenges.” This concept may sound obvious to some, but it is a deceptively simple concept that is often difficult to follow consistently. Any business analytics initiative must be grounded in “critical” business challenges. When we say critical, we mean challenges or questions for which the answers will lead to the company increasing its revenues or reducing its cost. It’s very easy for the analytics effort within an organization to drift gradually into issues of intellectual curiosity or merely be a support function that answers questions at the whim of senior business leaders. This is how an analytics function can gradually turn into a cost center, rather than a function that adds economic value to the organization.
We cover the best practices for how to establish the critical business questions in detail in Chapter 3: “Aligning Key Business Challenges across the Enterprise.” However, some of the most common business challenges that analytics may work to address include:
How can I increase customer acquisition and retention?
What prospects do I need to target in order to increase market share/customer spending?
What are the emergent competitive threats, and how can my organization manage them?
Who are my most profitable customers, and how do I bring in more like them?
What types of our customers are most loyal, and what can we do to increase loyalty among the others?
How are customer prospects using our online environment, and how can we increase conversion to a customer in our online experience?
What are customers saying about us in the marketplace?
What new products do customers want from us?
Although these challenges are broad, they are common to all industries and businesses. Understanding their priority within your organization and their alignment with your business’s mission should allow you to focus your expertise on the most important needs for which to develop a business analytics strategy or solution.
DATA FOUNDATION PILLAR
As with architecture, the strength of any design is incumbent on the strength of its foundation. And similar to architects, business analytics practitioners must engineer an analytics framework that’s built to last. With your business challenges outlined, you can start constructing the foundation that’s built on data.
