Analytics and Dynamic Customer Strategy: Big Profits from Big Data

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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 cm

Includes 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 2014

658.8′34—dc23

2014005246

This book is dedicated to Tom Leigh, formerly the Charles M. and Emily H. Tanner Chair in Sales and Professor Emeritus at the University of Georgia, who got this all started.

Foreword

The need to effectively build and manage customer relationships is almost universally accepted in today's world. Why then do so many organizations get it so wrong? The rise of Big Data has unfortunately only compounded the problem in many cases. Organizations that weren't doing a good job with customer data and analytics in the 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 that are prepared, Big Data provides the ability to understand customers and actively 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 on customer data to drive competitive advantage. While Tanner is an academic by profession, readers will not find the book to be focused on academics. The book is focused on real-world practices and examples that will be accessible to anyone who is familiar with marketing and customer analytics. At the same time, Tanner is able to reference the research he and others have done over the years to support the book's key points.

The central theme of the book is the need to learn and act in an ever-accelerating fashion. Tanner discusses the concept of a Dynamic Customer Strategy that keeps day-to-day actions linked to a central strategy. While a broad strategy is the compass, day-to-day efforts to remain on course are driven by a constant stream of analysis, learning, and actions that can be adjusted as required. Organizations that can accelerate the learning cycle will be able to act faster than the competition. One terrific example Tanner discusses is how Walmart was able to be the only store with American flags available after the 9/11 attacks. Since Walmart was analyzing sales trends and placing orders hourly, they locked up all of the available product from all flag vendors before the competition had the chance to run their nightly processes that identified the same demand. That's the advantage of learning and acting quickly.

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

A number of important themes that organizations can focus upon to help accelerate the learn-and-act cycle are discussed. Front and center is the need for controlled tests and experimental design. Such tests, when combined with statistical models to assess the results, are the gold standard for pinpointing what is working and what is not. A culture of testing and experimentation is no longer something that only innovative technology and Web companies have. The practices have moved into the mainstream and no organizations today can skip this trend. Experimenting to either find a new insight or fail fast can be done in a very purposeful and targeted manner.

Executing the wrong test and assessing it with the wrong data and analytics can be harmful. As Tanner discusses, simply having data is not having insight. A data and analytics strategy must be put in place to guide the process of finding insights. When it comes to collecting new information, approaches such as progressive profiling and avoiding data traps through erroneous assumptions are offered to guide the process. Emphasis is also placed upon the need to have cascading, multistep marketing campaigns that touch customers multiple times based upon their actions. Not all offers and touches need to be focused on sales. Often the most appropriate strategy is to simply nudge customers to the next step on a path known to lead to a purchase. I strongly agree with Tanner's directive not to equate offers with discounts. Driving action without discounts can provide more value to both an organization and its customers.

The book also spends time discussing the cultural aspects of succeeding with a strategy for Big Data and customer analytics. Without a culture that values the process, success can't happen. If marketing programs generate leads, but the sales force never provides feedback on what happened with the leads, then it isn't possible to learn how to act more effectively. Tanner emphasizes that many CRM projects have failed because organizations didn't separate the change management required within the organization from the technical project implementation. Executives must not simply sponsor projects, but sponsor the broader organizational and cultural changes needed to enable the project to succeed. One example I found particularly appropriate relates to an organization that claimed to put customer needs first. However, right next to a sign stating the need to put the customers first was a sign with the latest average customer service call time. Call center reps focused on keeping calls short can't possibly put customers first.

One aspect of the book that really jumped out at me is how much what's needed today is based upon the same principles that have historically been successful. I would be skeptical of any book that suggested tossing out everything we've learned in the past. Certainly, evolution and adaptation to the realities of Big Data and today's competitive and technology-driven landscape are necessary; however, Tanner shows how it is possible to build upon the wisdom that has accumulated over the years with respect to managing customer relationships. Readers should find comfort in this. Assimilating the principles in this book into an organization can be accomplished without totally ripping out whatever existing foundation is in place.

Don't get distracted with market hype. Simply focus on collecting the right data on your customers to plan and measure the effort to build a relationship with them through analytics. As Tanner points out, today's technologies make it possible to test new ideas more quickly and with more precision than in the past. Failures experienced for the purpose of learning are not bad. Rather, they focus attention where it is most deserved. And as Tanner concludes, the Big Data movement isn't about buying more technology or hiring more data scientists. It is about achieving a company's mission through better use of data and analytics. The technologies and people are simply the mechanisms to get there.

Analytics and Dynamic Customer Strategy: Big Profits from Big Data offers solid advice backed by research and case studies. It will help readers assess the current state of their organization's practices and identify opportunities for improvement. Improving how your organization learns and acts to develop customer relationships can only lead to more success. Enjoy!

Bill Franks

Chief Analytics Officer, Teradata, and author of

Taming the Big Data Tidal Wave (John Wiley & Sons, 2012) and The Analytics Revolution: How to Improve Your Business by Making Analytics Operational in the Big Data Era (John Wiley & Sons, 2014)

Preface

Why didn't you teach us this when I was at Baylor? She wasn't one of my students while she was at Baylor, but it wouldn't have mattered. I still felt the heat of her complaint. We were both at a conference to learn the latest in data-driven marketing but truth be told, no university was teaching anything about marketing automation and Big Data at the time—in fact, Big Data, as a term, didn't even exist yet. I couldn't have taught it, simply because there was 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 problem. I began to test my ideas with executives and marketing professionals around the world in workshops, seminars, and conferences.

Quickly, I realized that there was a hunger for solutions, but because of the proliferation of marketing channels and the rapid development of Big Data, many marketers simply weren't ready. Some progress in Big Data and marketing technology had to be made first. But my research team and I soldiered on. With the aid of good people like Mary Gros at Teradata, Bruce Culbert at the Pedowitz Group, and Paul Greenberg of the 56 Group, I got opportunities to work alongside people like Phil Kaus at Cabela's, James MacEngvale at Gallery Furniture, and others who were willing to let me test ideas and see what worked. But the response from 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. Organizations were calling, asking for presentations on customer strategy and Big Data and attendees were lined up to talk after. I was getting calls from journalists and asked to comment on Big Data and marketing strategy. So it was time—time to get what we had learned into one place: this book.

Are you struggling with a strategy for Big Data? Have you bought marketing applications that you fear are underutilized or overpowered for what you can do? Do you wonder what the next thing is you should be doing—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 marketing infrastructure. What's needed is the new set of strategic planning and execution skills needed to make the most of what Big Data and marketing technology have to offer. What's needed is Dynamic Customer Strategy.

Marketers, whether B2B or B2C, will find the Dynamic Customer Strategy approach works in making the most of Big Data. The goal with Big Data is to accelerate insight so that you can identify opportunities faster and respond to the data more quickly and automate to drive costs out of the value chain.

The book is organized into three parts. The first part describes the tools 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 embedding Big Data and the Dynamic Customer Strategy into your organizational culture so that you can truly accelerate the insight and the benefits to be gained from them.

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

Acknowledgments

This book would not have been possible without the work of a large group of people. First, I'd like to acknowledge my research team, which included Brooke Borgias, Emily Buratkowski, Carlos Gieseken, Anna Hoglund, Peter Klingman, Shantanu Moghe, LeighAnn Pearson, and John Vrbanac. These former students conducted interviews, gathered data, wrote case studies, and were vital members of the team. Similarly, faculty such as Cindy Riemenschneider (who introduced me to absorptive capacity), Bill Rand (who contributed in data 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 great deal to Mary Gros with Teradata, Bruce Culbert at Pedowitz, and Paul Greenberg of the 56 Group, and I would be remiss if I didn't acknowledge their support here. These folks, who are tremendous thought leaders in their own right, were so kind to open up their networks so we could collect data, work alongside with their clients to solve business challenges, and more. Because of them, I got know other thought leaders who contributed to the development of Dynamic Customer Strategy, folks like Cathy Burrows at RBC, Phil Kaus and Corey Bergstrom at Cabela's, and others.

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

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

And to my wife, Karen, I owe a special debt of gratitude. Thank you for putting up with the travel, the early mornings and late nights spent writing and editing, the evening and weekend business calls, and all the rest.

Part One

Big Data and Dynamic Customer Strategy

Chapter 1

Big Strategy for Big Data

My grandfather was a partner in a small furniture company in Palatka, Florida. The company manufactured Florida-style patio furniture for a limited market. His first major contribution (other than capital) was to shift the company from patio furniture to bedroom furniture because every house has bedrooms and not all have patios. He and his partners grew the company to three plants and retail customers across the lower 48 states.

I was on the board of directors when the Chinese began manufacturing bedroom furniture. As you can imagine, there was a lot of discussion about 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 suffering. 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 a few memories.

I think Big Data is like that. You hear a lot of hype! And tough decisions to make while filtering your own industry noise and market distractions. Do you pivot now or hope your organization can manage a defensive ramp-up when the deluge hits?

I suggest pivot!

Beyond the Hype

About the time we were going through the painful process of closing down the business, I came across this quote: The only sustainable competitive advantage is to learn faster than your competition, and to be able to act on that learning. I don't recall where I first came across this quote from former GE CEO Jack Welch, but it has stuck with me for more than a decade. Learn and act. Those two simple things, repeated, drive a firm's agility and financial performance the most.¹

But as I pondered the simple truth of learn and act, I realized that sustainability only comes if you are able to continue to learn faster than your competition. Figure something out once and someone will come along and jump ahead of you. Therefore the real sustainable advantage is having accelerated learning processes.

Here's a simple, and true, example of the value of accelerated information. On September 11, 2001, everything stopped when three planes slammed 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. People bought lots of things they might need in a disaster: flashlights and batteries, 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, only Walmart had flags. Why? Because their inventory management system was updating inventory every five minutes and generating an order, while competitors' systems waited until the end of the day. By the time competitors could generate an order, Walmart had already sewn up all available inventory. Walmart developed a system that could generate updated orders more frequently because their supply chain could get flags to the shelves overnight. They were also the only one with sufficient stocks of flashlights, batteries, and so forth to handle demand, because they were 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? Next to none. Same with flashlights, batteries, and canned goods (except for those like canned Brussels sprouts that few people like).

Two or three years after that awful day, speakers would use that story to illustrate the power of data. But even that story is of limited value. As soon as Target polled its inventory every five minutes and began ordering 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 realization came a quest to discover how to accelerate organizational learning.

Then along came Big Data. By now, probably everyone is aware of the ever-increasing rates that data streams in. Data is piling up at over 2.5 quintillion bytes per day. Just on Facebook, we're seeing people sharing 1 billion pieces of information daily.² And it's not just Facebook; new marketing channels are introduced every day, each with its own way to capture data. That's a lot of data.

But is that Big Data? One definition of Big Data is data that exceeds the capacity of commonly used technology.³ On the one hand, that definition suggests that today's Big Data is simply tomorrow's data. On the other hand, companies are already capturing the power of Big Data—that is, technology is making better decision making possible with new uses 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 velocity. Volume, variety, and velocity are in fact the three dimensions of Big Data.⁴

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

For the past 10 years, my team and I have studied how companies learn—and act. After 10 years of research, we've identified and tested an approach that helps marketers master the volume, variety, and velocity of Big Data, unleashing the power of Big Data for accelerated competitive advantage.

The Value of Accelerated Learning

How big a competitive advantage? A recent study by the Insight Technology Group claims these results, also shown in Figure 1.1, for customer-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

Figure 1.1 Maximum Gains Observed for Customer-Facing Big Data Solutions

Source: Data cited from CBP Research, The Case for a New CRM Solution (2013).

At the same time, consider this statistic: McKinsey Global Institute estimates