Analytics and DynamicCustomer Strategy

Analytics andDynamic

Customer Strategy

Big Profits from Big Data

John F. Tanner Jr.

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Copyright © 2014 by John F. Tanner Jr. All rights reserved.

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Library of Congress Cataloging-in-Publication Data:

Tanner, John F.Analytics and dynamic customer strategy : big profits from big data / John F. (Jeff) Tanner, Jr.

pages cmIncludes index.ISBN 978-1-118-90573-9 (Hardcover) — ISBN 978-1-118-91978-1 (ePDF) — ISBN

978-1-118-91977-4 (ePub) — ISBN 978-1-118-91976-7 (oBook) 1. Customer relations.2. Relationship marketing. 3. Big data. I. Title.

HF5415.5.T36 2014658.8′34—dc23

2014005246

Printed in the United States of America

10 9 8 7 6 5 4 3 2 1

This book is dedicated to Tom Leigh,formerly the Charles M.

and Emily H. Tanner Chairin Sales and Professor Emeritusat the University of Georgia,

who got this all started.

Contents

Foreword xiPreface xvAcknowledgments xvii

Part One: Big Data and Dynamic Customer Strategy

Chapter 1: Big Strategy for Big Data 3Beyond the Hype 4The Value of Accelerated Learning 6Introducing Dynamic Customer Strategy 7DCS Complements Design School 19Barriers to Big Data and DCS 20Summary 24Notes 24

Chapter 2: Mapping Dynamic Customer Strategy 27Theory as Strategy 28Concepts 29Relationships 31Establishing Causality through Control 34

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viii C O N T E N T S

Conditions 39Making the Model Operational 40Target’s Behavioral Loyalty Model 40Simple versus Complex Models 42Summary 43Notes 43

Chapter 3: Operationalizing Strategy 45Conceptual to Operational 45Operational Definitions 48From Strategy to Action 53Microsoft’s DCS and Fail-Fast Mentality 53Experiments and Decisions 54Managing Decision Risk 57Using Big Data Effectively 59Summary 63Notes 64

Part Two: Big Data Strategy

Chapter 4: Creating a Big Data Strategy 69Avoiding Data Traps 70An Airline Falls into a Data Trap 71Creating the Data Strategy 73Summary 83Notes 83

Chapter 5: Big Data Acquisition 85Measurement Quality 88The Truth and Big Data 89Acquiring Big Data 90Making Good Choices 98The Special Challenge of Salespeople 99Summary 100Notes 101

Chapter 6: Streaming Insight 103The Model Cycle 103Applications of Statistical Models 108Types of Data—Types of Analytics 112

Contents ix

Matching Data to Models 113Summary 118

Chapter 7: Turning Models into Customers 119Mac’s Avoids Mindless Discounting 120Decision Mapping 121Conversations and Big Data 123Cascading Campaigns 127Cascading Campaigns Accelerate Learning 130Accelerating the Process with MultifactorialExperimental Design 131Summary 133Notes 133

Chapter 8: Big Data and Lots of Marketing Buzzwords 135Customer Experience Management 136Value and Performance 138Performance, Value, and Propensity to Relate 140Responsiveness 142Citibank MasterCard Responds at MarketLevel 143Transparency 144Community 146Cabela’s Journey to Customer Experience 147Summary 149Notes 150

Chapter 9: Big Data Metrics for Big Performance 151The Big Data of Metrics 152Variation and Performance 154Creating a Tolerance Range 156Visualization 158Creating the Right Metrics 164Summary 170Notes 170

Part Three: Big Data Culture

Chapter 10: The Near-Simultaneous Adoption of MultipleInnovations 173Building Absorptive Capacity 176

x C O N T E N T S

People, Process, and Tools 177Managing the Change 183Empowering Your Entrepreneurs 188Konica-Minolta’s Awesome Results 190One Result: Customer KnowledgeCompetence 191Global Implementation 193Summary 194Notes 195

Chapter 11: Leading (in) the Dynamic Customer Culture 197Leadership, Big Data, and Dynamic CustomerStrategy 198Leadership and Culture 203Movements 207Exploiting Strategic Experimentation 212Big Data, Big Decisions, Big Results 213Notes 213

Afterword 215Additional Readings 219About the Author 221Index 223

Foreword

The need to effectively build and manage customer relationshipsis almost universally accepted in today’s world. Why then do somany organizations get it so wrong? The rise of Big Data has

unfortunately only compounded the problem in many cases. Organiza-tions that weren’t doing a good job with customer data and analytics inthe past are falling even further behind. For those that aren’t prepared,Big Data offers plenty of ways to do things very wrong. For those thatare prepared, Big Data provides the ability to understand customers andactively manage customer relationships at a level never before possible.

In Analytics and Dynamic Customer Strategy: Big Profits from Big Data,Jeff Tanner tackles the challenge of capturing, analyzing, and acting oncustomer data to drive competitive advantage. While Tanner is an aca-demic by profession, readers will not find the book to be focused onacademics. The book is focused on real-world practices and examplesthat will be accessible to anyone who is familiar with marketing andcustomer analytics. At the same time, Tanner is able to reference theresearch he and others have done over the years to support the book’skey points.

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xii F O R E W O R D

The central theme of the book is the need to learn and act in anever-accelerating fashion. Tanner discusses the concept of a DynamicCustomer Strategy that keeps day-to-day actions linked to a central strat-egy. While a broad strategy is the compass, day-to-day efforts to remainon course are driven by a constant stream of analysis, learning, andactions that can be adjusted as required. Organizations that can accel-erate the learning cycle will be able to act faster than the competition.One terrific example Tanner discusses is how Walmart was able to bethe only store with American flags available after the 9/11 attacks. SinceWalmart was analyzing sales trends and placing orders hourly, they lockedup all of the available product from all flag vendors before the compe-tition had the chance to run their nightly processes that identified thesame demand. That’s the advantage of learning and acting quickly.

In order to act quickly, an organization must be aligned. Tanner alsofocuses on the importance of having a common language. For example,what exactly is a customer? Just like Tanner, I have seen this seeminglyobvious question lead to heated debate and disagreement at organiza-tions that should have had a quick and consistent answer. Is the customera household or an individual? Is the customer the main corporate entityor each office location? There can be ambiguity about the best answer.However, it is critical that organizations reach agreement on a single def-inition and stick to it. Otherwise it is impossible to develop the consistentanalytics to drive consistent actions.

A number of important themes that organizations can focus uponto help accelerate the learn-and-act cycle are discussed. Front and cen-ter is the need for controlled tests and experimental design. Such tests,when combined with statistical models to assess the results, are the goldstandard for pinpointing what is working and what is not. A culture oftesting and experimentation is no longer something that only innovativetechnology and Web companies have. The practices have moved intothe mainstream and no organizations today can skip this trend. Exper-imenting to either find a new insight or fail fast can be done in a verypurposeful and targeted manner.

Executing the wrong test and assessing it with the wrong data andanalytics can be harmful. As Tanner discusses, simply having data is nothaving insight. A data and analytics strategy must be put in place to guide

Foreword xiii

the process of finding insights. When it comes to collecting new infor-mation, approaches such as progressive profiling and avoiding data trapsthrough erroneous assumptions are offered to guide the process. Empha-sis is also placed upon the need to have cascading, multistep marketingcampaigns that touch customers multiple times based upon their actions.Not all offers and touches need to be focused on sales. Often the mostappropriate strategy is to simply nudge customers to the next step on apath known to lead to a purchase. I strongly agree with Tanner’s directivenot to equate offers with discounts. Driving action without discounts canprovide more value to both an organization and its customers.

The book also spends time discussing the cultural aspects of succeed-ing with a strategy for Big Data and customer analytics. Without a culturethat values the process, success can’t happen. If marketing programs gen-erate leads, but the sales force never provides feedback on what happenedwith the leads, then it isn’t possible to learn how to act more effectively.Tanner emphasizes that many CRM projects have failed because orga-nizations didn’t separate the change management required within theorganization from the technical project implementation. Executives mustnot simply sponsor projects, but sponsor the broader organizational andcultural changes needed to enable the project to succeed. One exampleI found particularly appropriate relates to an organization that claimed toput customer needs first. However, right next to a sign stating the needto put the customers first was a sign with the latest average customerservice call time. Call center reps focused on keeping calls short can’tpossibly put customers first.

One aspect of the book that really jumped out at me is how muchwhat’s needed today is based upon the same principles that have histor-ically been successful. I would be skeptical of any book that suggestedtossing out everything we’ve learned in the past. Certainly, evolutionand adaptation to the realities of Big Data and today’s competitive andtechnology-driven landscape are necessary; however, Tanner shows howit is possible to build upon the wisdom that has accumulated over theyears with respect to managing customer relationships. Readers shouldfind comfort in this. Assimilating the principles in this book into anorganization can be accomplished without totally ripping out whateverexisting foundation is in place.

xiv F O R E W O R D

Don’t get distracted with market hype. Simply focus on collectingthe right data on your customers to plan and measure the effort to builda relationship with them through analytics. As Tanner points out, today’stechnologies make it possible to test new ideas more quickly and withmore precision than in the past. Failures experienced for the purposeof learning are not bad. Rather, they focus attention where it is mostdeserved. And as Tanner concludes, the Big Data movement isn’t aboutbuying more technology or hiring more data scientists. It is about achiev-ing a company’s mission through better use of data and analytics. Thetechnologies and people are simply the mechanisms to get there.

Analytics and Dynamic Customer Strategy: Big Profits from Big Data offerssolid advice backed by research and case studies. It will help readers assessthe current state of their organization’s practices and identify opportuni-ties for improvement. Improving how your organization learns and actsto develop customer relationships can only lead to more success. Enjoy!

Bill FranksChief Analytics Officer, Teradata, and author of

Taming the Big Data Tidal Wave ( John Wiley & Sons, 2012) andThe Analytics Revolution: How to Improve Your Business by Making

Analytics Operational in the Big Data Era ( John Wiley & Sons, 2014)

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Preface

Why didn’t you teach us this when I was at Baylor? Shewasn’t one of my students while she was at Baylor, butit wouldn’t have mattered. I still felt the heat of her com-

plaint. We were both at a conference to learn the latest in data-drivenmarketing but truth be told, no university was teaching anything aboutmarketing automation and Big Data at the time—in fact, Big Data, as aterm, didn’t even exist yet. I couldn’t have taught it, simply because therewas no real body of knowledge from which to teach, only anecdotes.

So I did what professors do. I began to study and work on the prob-lem. I began to test my ideas with executives and marketing professionalsaround the world in workshops, seminars, and conferences.

Quickly, I realized that there was a hunger for solutions, but becauseof the proliferation of marketing channels and the rapid development ofBig Data, many marketers simply weren’t ready. Some progress in BigData and marketing technology had to be made first. But my researchteam and I soldiered on. With the aid of good people like Mary Grosat Teradata, Bruce Culbert at the Pedowitz Group, and Paul Greenbergof the 56 Group, I got opportunities to work alongside people like PhilKaus at Cabela’s, James MacEngvale at Gallery Furniture, and others who

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xvi P R E F A C E

were willing to let me test ideas and see what worked. But the responsefrom the marketing world was, well, lukewarm to put it mildly.

Then in 2013, everything changed—and I mean changed quickly.Suddenly, my presentations at conferences were jammed. Organizationswere calling, asking for presentations on customer strategy and Big Dataand attendees were lined up to talk after. I was getting calls from jour-nalists and asked to comment on Big Data and marketing strategy. So itwas time—time to get what we had learned into one place: this book.

Are you struggling with a strategy for Big Data? Have you boughtmarketing applications that you fear are underutilized or overpowered forwhat you can do? Do you wonder what the next thing is you should bedoing—or the last thing you should have done and now it feels too late?

Big Data is no longer the future—Big Data is here. So is the mar-keting infrastructure. What’s needed is the new set of strategic planningand execution skills needed to make the most of what Big Data and mar-keting technology have to offer. What’s needed is Dynamic CustomerStrategy.

Marketers, whether B2B or B2C, will find the Dynamic CustomerStrategy approach works in making the most of Big Data. The goal withBig Data is to accelerate insight so that you can identify opportunitiesfaster and respond to the data more quickly and automate to drive costsout of the value chain.

The book is organized into three parts. The first part describes thetools comprising the Dynamic Customer Strategy approach to Big Data.The second part details the Big Data strategy process of acquire, analyze,apply, and assess. The final part then identifies strategies for embeddingBig Data and the Dynamic Customer Strategy into your organizationalculture so that you can truly accelerate the insight and the benefits to begained from them.

Big profits from Big Data? Absolutely. Data-capable marketing lead-ers? Positively. I’ve never wanted to teach the history of business—I’vealways wanted to be in the lead. So join me—let’s accelerate the profitsfrom Big Data.

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Acknowledgments

This book would not have been possible without the work of alarge group of people. First, I’d like to acknowledge my researchteam, which included Brooke Borgias, Emily Buratkowski,

Carlos Gieseken, Anna Hoglund, Peter Klingman, Shantanu Moghe,LeighAnn Pearson, and John Vrbanac. These former students conductedinterviews, gathered data, wrote case studies, and were vital membersof the team. Similarly, faculty such as Cindy Riemenschneider (whointroduced me to absorptive capacity), Bill Rand (who contributed indata visualization), and Morris George (who worked on client data)were important in shaping the direction and content of the book.

While I mentioned them in the Preface, this project owes a greatdeal to Mary Gros with Teradata, Bruce Culbert at Pedowitz, and PaulGreenberg of the 56 Group, and I would be remiss if I didn’t acknowl-edge their support here. These folks, who are tremendous thought lead-ers in their own right, were so kind to open up their networks so wecould collect data, work alongside with their clients to solve businesschallenges, and more. Because of them, I got know other thought lead-ers who contributed to the development of Dynamic Customer Strategy,

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xviii A C K N O W L E D G M E N T S

folks like Cathy Burrows at RBC, Phil Kaus and Corey Bergstrom atCabela’s, and others.

Mary, Bruce, and Paul, along with Emily Tanner and John Tan-ner, also carefully read the manuscript and offered terrific insight forstrengthening the book. I also value the input of Bill Franks, Chief DataOfficer for Teradata, and Lisa Arthur, Chief Marketing Officer for Ter-adata Applications—both authors of terrific Big Data books. I owe agreat deal to all of them and any shortcomings in the book are due tomy own faults.

I’d like to thank the editorial team at Wiley, Sheck Cho and StaceyRivera. They are terrific professionals and great to work with.

And to my wife, Karen, I owe a special debt of gratitude. Thankyou for putting up with the travel, the early mornings and late nightsspent writing and editing, the evening and weekend business calls, andall the rest.

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Part One

BIG DATA ANDDYNAMIC CUSTOMER

STRATEGY

1

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Chapter 1

Big Strategy for Big Data

My grandfather was a partner in a small furniture company inPalatka, Florida. The company manufactured Florida-stylepatio furniture for a limited market. His first major contri-

bution (other than capital) was to shift the company from patio furnitureto bedroom furniture because every house has bedrooms and not all havepatios. He and his partners grew the company to three plants and retailcustomers across the lower 48 states.

I was on the board of directors when the Chinese began manufactur-ing bedroom furniture. As you can imagine, there was a lot of discussionabout the threat of Chinese manufacturing. We thought it was all hype.No way could they send furniture to the United States and compete!By the time we recognized the threat, our sales and profits were suffer-ing. Despite an effort to pivot (a bit too late), we could not recover, and,without overstating it, all we were left with were empty factories and afew memories.

I think Big Data is like that. You hear a lot of hype! And toughdecisions to make while filtering your own industry noise and market

3

4 A N A L Y T I C S A N D D Y N A M I C C U S T O M E R S T R A T E G Y

distractions. Do you pivot now or hope your organization can managea defensive ramp-up when the deluge hits?

I suggest pivot!

Beyond the Hype

About the time we were going through the painful process of closingdown the business, I came across this quote: “The only sustainable com-petitive advantage is to learn faster than your competition, and to be ableto act on that learning.” I don’t recall where I first came across this quotefrom former GE CEO Jack Welch, but it has stuck with me for morethan a decade. Learn and act. Those two simple things, repeated, drivea firm’s agility and financial performance the most.1

But as I pondered the simple truth of learn and act, I realized thatsustainability only comes if you are able to continue to learn faster thanyour competition. Figure something out once and someone will comealong and jump ahead of you. Therefore the real sustainable advantageis having accelerated learning processes.

Here’s a simple, and true, example of the value of accelerated infor-mation. On September 11, 2001, everything stopped when three planesslammed into crowded buildings and a fourth into a Pennsylvania field.By the middle of the afternoon, pretty much all of America had closed.But on September 12, the stores across America opened again. Peoplebought lots of things they might need in a disaster: flashlights and batter-ies, canned foods, jugs of water. And they bought flags—American flags.So many that by the end of the day all were gone. On September 13, onlyWalmart had flags. Why? Because their inventory management systemwas updating inventory every five minutes and generating an order, whilecompetitors’ systems waited until the end of the day. By the time com-petitors could generate an order, Walmart had already sewn up all avail-able inventory. Walmart developed a system that could generate updatedorders more frequently because their supply chain could get flags to theshelves overnight. They were also the only one with sufficient stocksof flashlights, batteries, and so forth to handle demand, because theywere the only company that had systems that could move that quickly.How many flags did anyone else sell on September 13 or 14 or 15?

Big Strategy for Big Data 5

Next to none. Same with flashlights, batteries, and canned goods (exceptfor those like canned Brussels sprouts that few people like).

Two or three years after that awful day, speakers would use that storyto illustrate the power of data. But even that story is of limited value. Assoon as Target polled its inventory every five minutes and began order-ing at the same or faster speed, the advantage was lost. What was missing,I realized, was the process of generating new learning. And with that real-ization came a quest to discover how to accelerate organizational learning.

Then along came Big Data. By now, probably everyone is awareof the ever-increasing rates that data streams in. Data is piling up at over2.5 quintillion bytes per day. Just on Facebook, we’re seeing people shar-ing 1 billion pieces of information daily.2 And it’s not just Facebook; newmarketing channels are introduced every day, each with its own way tocapture data. That’s a lot of data.

But is that Big Data? One definition of Big Data is data that exceedsthe capacity of commonly used technology.3 On the one hand, that def-inition suggests that today’s Big Data is simply tomorrow’s data. On theother hand, companies are already capturing the power of Big Data—that is, technology is making better decision making possible with newuses for data.

So is Big Data a lot of data or just a lot of hype? Yes. In some ways,it’s both.

The hype, like all hype, has more than a nugget of truth at its core.More data, in terms of volume and variety, is available at increasing veloc-ity. Volume, variety, and velocity are in fact the three dimensions ofBig Data.4

The role of Big Data is to fuel streaming insight—to fuel continuouslearning at accelerating speeds. Yes, Big Data does represent data thatrequires new technologies, as the definition suggests. But at the sametime, sustainable competitive advantage requires action. So Big Dataand all of the attendant technology and tools are really only half of theequation—the other half is the strategy needed to act on Big Data.

For the past 10 years, my team and I have studied how companieslearn—and act. After 10 years of research, we’ve identified and tested anapproach that helps marketers master the volume, variety, and velocity ofBig Data, unleashing the power of Big Data for accelerated competitiveadvantage.

6 A N A L Y T I C S A N D D Y N A M I C C U S T O M E R S T R A T E G Y

The Value of Accelerated Learning

How big a competitive advantage? A recent study by the InsightTechnology Group claims these results, also shown in Figure 1.1, forcustomer-facing Big Data initiatives:

• Up to 40 percent revenue increase• Up to 35 percent reduction in cost to sell• Up to 25 percent reduction in sales cycle time• An average 2 percent increase in margin• An average 20 percent increase in customer satisfaction5

At the same time, consider this statistic: McKinsey Global Insti-tute estimates that we’re 150 million short of the data-capable managersneeded.6 What that means is that there is a tremendous opportunity,both personally and corporately, to develop data capability and ride thetidal wave that is Big Data. If you have the people who have Big Dataskills, you can gain a competitive advantage over those who do not.

But that’s not enough, is it? Just because there’s lots of data, of increas-ing variety, coming at us faster and faster doesn’t mean there’s value tobe gained or that it is more than hype. Where does the value reallycome from?

Look at it this way: If it takes you six days to compile all of thedata from a sales event like Black Friday, analyze it, and get interpretableresults to a merchandiser or a sales manager so that actions can be taken

+40%

–35% –25%

+20%

+2%

Rev

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Cos

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Sell

Sales

Cyc

le T

ime

Cus

tom

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atisfa

ction

Mar

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Figure 1.1 Maximum Gains Observed for Customer-Facing Big Data SolutionsSOURCE: Data cited from CBP Research, The Case for a New CRM Solution (2013).