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More About This Title Analytics and Dynamic Customer Strategy: Big Profits from Big Data
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Dynamic Customer Strategy: Big Profits from Big Data is a comprehensive guide to exploiting big data for both business-to-consumer and business-to-business marketing. This complete guide provides a process for rigorous decision making in navigating the data-driven industry shift, informing marketing practice, and aiding businesses in early adoption. Using data from a five-year study to illustrate important concepts and scenarios along the way, the author speaks directly to marketing and operations professionals who may not necessarily be big data savvy. With expert insight and clear analysis, the book helps eliminate paralysis-by-analysis and optimize decision making for marketing performance.
Nearly seventy-five percent of marketers plan to adopt a big data analytics solution within two years, but many are likely to fail. Despite intensive planning, generous spending, and the best intentions, these initiatives will not succeed without a manager at the helm who is capable of handling the nuances of big data projects. This requires a new way of marketing, and a new approach to data. It means applying new models and metrics to brand new consumer behaviors. Dynamic Customer Strategy clarifies the situation, and highlights the key decisions that have the greatest impact on a company's big data plan. Topics include:
- Applying the elements of Dynamic Customer Strategy
- Acquiring, mining, and analyzing data
- Metrics and models for big data utilization
- Shifting perspective from model to customer
Big data is a tremendous opportunity for marketers and may just be the only factor that will allow marketers to keep pace with the changing consumer and thus keep brands relevant at a time of unprecedented choice. But like any tool, it must be wielded with skill and precision. Dynamic Customer Strategy: Big Profits from Big Data helps marketers shape a strategy that works.
English
JOHN F. “JEFF” TANNER, JR., PHD, is Professor of Marketing and the Executive Director of Baylor University’s Business Collaboratory. His work has appeared in the Journal of Marketing, Journal of the Academy of Marketing Science, and the Journal of Business Research. He has authored and coauthored 15 books. Tanner is a consultant to companies such as IBM, Pearson–Prentice Hall, Cabela’s, and the federal government. He serves on the board of directors of several companies and nonprofits.
English
Foreword xi
Preface xv
Acknowledgments xvii
Part One: Big Data and Dynamic Customer Strategy
Chapter 1: Big Strategy for Big Data 3
Beyond the Hype 4
The Value of Accelerated Learning 6
Introducing Dynamic Customer Strategy 7
DCS Complements Design School 19
Barriers to Big Data and DCS 20
Summary 24
Notes 24
Chapter 2: Mapping Dynamic Customer Strategy 27
Theory as Strategy 28
Concepts 29
Relationships 31
Establishing Causality through Control 34
Conditions 39
Making the Model Operational 40
Target’s Behavioral Loyalty Model 40
Simple versus Complex Models 42
Summary 43
Notes 43
Chapter 3: Operationalizing Strategy 45
Conceptual to Operational 45
Operational Definitions 48
From Strategy to Action 53
Microsoft’s DCS and Fail-Fast Mentality 53
Experiments and Decisions 54
Managing Decision Risk 57
Using Big Data Effectively 59
Summary 63
Notes 64
Part Two: Big Data Strategy
Chapter 4: Creating a Big Data Strategy 69
Avoiding Data Traps 70
An Airline Falls into a Data Trap 71
Creating the Data Strategy 73
Summary 83
Notes 83
Chapter 5: Big Data Acquisition 85
Measurement Quality 88
The Truth and Big Data 89
Acquiring Big Data 90
Making Good Choices 98
The Special Challenge of Salespeople 99
Summary 100
Notes 101
Chapter 6: Streaming Insight 103
The Model Cycle 103
Applications of Statistical Models 108
Types of Data—Types of Analytics 112
Matching Data to Models 113
Summary 118
Chapter 7: Turning Models into Customers 119
Mac’s Avoids Mindless Discounting 120
Decision Mapping 121
Conversations and Big Data 123
Cascading Campaigns 127
Cascading Campaigns Accelerate Learning 130
Accelerating the Process with Multifactorial
Experimental Design 131
Summary 133
Notes 133
Chapter 8: Big Data and Lots of Marketing Buzzwords 135
Customer Experience Management 136
Value and Performance 138
Performance, Value, and Propensity to Relate 140
Responsiveness 142
Citibank MasterCard Responds at Market
Level 143
Transparency 144
Community 146
Cabela’s Journey to Customer Experience 147
Summary 149
Notes 150
Chapter 9: Big Data Metrics for Big Performance 151
The Big Data of Metrics 152
Variation and Performance 154
Creating a Tolerance Range 156
Visualization 158
Creating the Right Metrics 164
Summary 170
Notes 170
Part Three: Big Data Culture
Chapter 10: The Near-Simultaneous Adoption of Multiple
Innovations 173
Building Absorptive Capacity 176
People, Process, and Tools 177
Managing the Change 183
Empowering Your Entrepreneurs 188
Konica-Minolta’s Awesome Results 190
One Result: Customer Knowledge
Competence 191
Global Implementation 193
Summary 194
Notes 195
Chapter 11: Leading (in) the Dynamic Customer Culture 197
Leadership, Big Data, and Dynamic Customer
Strategy 198
Leadership and Culture 203
Movements 207
Exploiting Strategic Experimentation 212
Big Data, Big Decisions, Big Results 213
Notes 213
Afterword 215
Additional Readings 219
About the Author 221
Index 223
