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
The only sustainable competitive advantage is the ability to learn faster than the competition and to act on what you’ve learned. Big Data is, by itself, not particularly useful. But with the right strategic tools and marketing technology, and in the right culture, your organization can learn and act faster on Big Data.
Big Data requires a new way to look at strategy, because otherwise the volume, variety, and velocity that is Big Data would overwhelm the organization. After over a decade of research, Dynamic Customer Strategy has been developed to complement what you already know about strategy but designed to take advantage of Big Data. DCS is comprised of a theoretical map, a data strategy, organizational learning, and customer knowledge competence.
Don’t let the barriers of arrogance, fear of math, or lack of CEO understanding hold you back. You can, with the help of the research on which this book is based, overcome these barriers to apply Big Data and DCS to your marketing organization.
Notes 24
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
Big Data by itself is just that, a lot of data. Without understanding what it means and how different variables relate, you really don’t have much insight. That’s why the first element in DCS is to create your theoretical foundation. Conceptmapping is a useful way to illustrate the relationship between the concepts and outcomes that are important to your organization.
Conditions are those variables that are external to your strategy but affect your outcomes.
To illustrate how a theory can also be a strategy, we examined the four causes of loyalty. But first we had to define what we meant by loyalty; conceptual definitions are an important element in effective DCS.
Causal variables, such as performance, responsiveness, transparency, and community, are the factors that drive the outcomes you desire, such as loyalty. To the extent that you can perform, be responsive, provide obvious benefits for loyalty, and build community, you can succeed in achieving your desired outcomes, stronger loyalty and greater CLV.
Causal variables do correlate with outcomes, but they also meet other requirements. They occur before the outcome, and we can eliminate, or at least control for, the effects of other variables, called counterfactuals.
Strategy, then, becomes about conducting experiments to test our theory so we can quickly determine what works and what doesn’t with a greater assurance of being right, rather than lucky. Model myopia can work against us, as it did at Best Buy. But if we can seek opportunity by building larger models, then a future like Amazon’s may be in store.
Notes 43
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
Conceptual maps help us identify opportunities in the market, but to take these strategies and turn them into action requires that we operationalize the concepts. An important aspect of operationalization is measurement; we should be able to measure our operational definitions.
These measures should meet a set of criteria, including that they reflect the concept accurately, act as intended, and are complete.
Strategy and action then becomes a series of experiments that test the conceptual model. We manage risk in the process, typically controlling both investment and opportunity risk in how we design the experiments and set up decision rules.
To use Big Data effectively, we have to overcome some of the natural human responses to data. A bias for action, for example, can cause us to act before gathering sufficient data, while an availability bias leads us to make decisions on data that is easily gathered. These and other potential biases can limit our ability to effectively consume data as part of our decision processes.
Notes 64
Avoiding Data Traps 70
An Airline Falls into a Data Trap 71
Creating the Data Strategy 73
Summary 83
From Big Data to streaming insight—that’s the goal. But all data is not created equal. Further, having a lot of data for only a few variables may make us feel like we know a lot, but actually can lead us to fall into the trap of thinking we know more than we do.
Figuring out what data creates the greatest business value, acquiring it, and applying it to achieve business objectives are the motives for a data strategy. The first step in the strategy is acquisition, followed by analysis, application, and assessment. Over the next few chapters, we’ll explore how to create a data strategy in greater detail, particularly how your data strategy integrates with the DCS approach to customer strategy.
Notes 83
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
In this chapter, we’ve explored the challenges associated with acquiring data. Big Data is supposed to be about volume, velocity, and variety, so how could acquisition be a challenge? The answer is that not all data is created equal. We need to find the right data for the questions we’re trying to answer. Further, not all data is equally available, and the cost to acquire can vary significantly.
Begin with a data assets inventory. This matrix tells you what you already have and where it is. Often, just this simple tool can help you identify new opportunities to explore and ask valuable business questions of the data. But if you don’t have the data you need, consider the power of progressive profiling.
Progressive profiling is an intentional sequence of data collection, combining all possible methods of data acquisition in order to fully understand who the buyer is and where the buyer is in the consumption life cycle. Through combinations of surveying and experimental design, along with third-party data and digital body language, we can obtain a
fuller picture of our buyer.
Notes 101
The Model Cycle 103
Applications of Statistical Models 108
Types of Data—Types of Analytics 112
Contents ix
Matching Data to Models 113
Summary 118
Analytics can be separated into three categories based on the purpose: reporting, discovery, and production. Reporting analytics enable us to take complex systems and simplify them for monitoring, as well as for comparison. Discovery analytics are used to understand why something is happening, to test relationships in our conceptual map, or to identify new opportunities. Production analytics are models routinely and automatically applied as data streams so that our systems can make the right offers and other decisions.
Lead scoring and affinity models are common production models.
But discovery models depend on the type of data being used, such as cluster analysis used for segmentation. Since you have choices regarding the format of data when you create your data strategy, knowing how you plan to use the data should influence your data acquisition, which also requires that you put some thought into how you plan to analyze the data.
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
Turning Big Data into knowledge is only half the challenge—converting that knowledge into action and doing it quickly is the other half. If you can accelerate learning but you can’t accelerate action, cashing in on opportunities is more difficult.
One mantra I repeat often is to sell the way the buyer wants to buy.
But customers make a lot of little decisions along the way, so an important use of Big Data is to model the buyer’s decision process, then offer the right information at the right time to help that buyer along the path to purchase. This intelligent conversation requires automated models that score buyers based on their actions so that the rightmaterial is put in front of them.
The result is a cascading campaign, a series of brief conversations that engage buyers where they are in the decision process. Then, based on buyer choices, the next appropriate step in the process takes place.
Cascading campaigns are also ongoing experiments—what’s the right call to action, which salutation draws best—and similar research questions are constantly being tested. Using advanced experimental design, learning is accelerated as campaigns are honed.
In the B2B space, these campaigns nurture leads until they are “sales ready,” ready to receive a sales call. Sales cycles are shortened, costs are reduced, and customers actually like the process better. In the next chapter, we explore how Big Data can improve your customers’ experiences with your company and products.
Notes 133
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
Cabela’s is a great example of a company with strong attitudinal loyalty among its customers working to convert that positive emotion into greater share of wallet. Through effective application of data to create strategies designed to address all four drivers of customer loyalty —performance, responsiveness, transparency, and community—the
company has outperformed many in growing its business.
Nine out of 10 companies havemade customer experience a priority, but few really understand what makes for positive customer experience.
One helpful model is the personal value equation, that formula we all use to assess the quality of the products and services we use.
Not all customer relationships should be deep and long-lasting. For some customers, the value equation just doesn’t support an emotional attachment. To strengthen behavioral loyalty and increase switching costs, companies use loyalty programs. Self-serve technologies also support low-value customers who are willing to manage the relationship.
But make the self-serve requirements too onerous and the relationship becomes one of convenience.
With thought given to responsiveness (and making responsiveness transparent), companies can win the hearts of customers. Further, since today’s customer is empowered through social media, the importance of community can’t be overstated.
In the next chapter, we look at the metrics of Big Data. Big Data is changing how we measure and monitor our marketing strategies, integrated into the development of streaming insight.
Notes 150
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
Innovations 173
Building Absorptive Capacity 176
x C O N T E N T S
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
Big Data requires the adoption of many innovations. The technology needed to capture Web browsing data on your website, mobile technology for data capture and push messaging, automated Twitter drip campaigns, and lots, lots more are all part of the Big Data revolution.
Building absorptive capacity is needed to accelerate the adoption of these innovations nearly simultaneously.
The objective is to create competency in customer knowledge—to be competent in applying Big Data to create streaming customer insight.
To be competent requires people who can use tools effectively and apply processes to continuously get better. In other words, building absorptive capacity and customer knowledge competence can be accomplished through intentional actions.
Notes 195
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
“The only sustainable competitive advantage is to be able to learn faster than your competition, and to be able to act on that learning.” This quote from Jack Welch, the one we used to open the book, symbolizes Dynamic Customer Strategy. But with Big Data’s volume, velocity, and variety comes the need for better technology, stronger analytics, and
automated marketing systems. Put this all together in the right culture and you’ve got a powerful but agile machine, building a customer knowledge competency that will create a sustainable competitive advantage.
Put this to work and let me hear from you. Let’s put your success story in the second edition.
Notes 213
