Customer Analytics For Dummies

Ensuring your customers are having positive experiences with your company at all levels, including initial brand awareness and loyalty, is crucial to the success of your business. Customer Analytics For Dummies shows you how to measure each stage of the customer journey and use the right analytics to understand customer behavior and make key business decisions.

Customer Analytics For Dummies gets you up to speed on what you should be testing. You'll also find current information on how to leverage A/B testing, social media's role in the post-purchasing analytics, usability metrics, prediction and statistics, and much more to effectively manage the customer experience. Written by a highly visible expert in the area of customer analytics, this guide will have you up and running on putting customer analytics into practice at your own business in no time.

• Shows you what to measure, how to measure, and ways to interpret the data
• Provides real-world customer analytics examples from companies such as Wikipedia, PayPal, and Walmart
• Explains how to use customer analytics to make smarter business decisions that generate more loyal customers
• Offers easy-to-digest information on understanding each stage of the customer journey

Whether you're part of a Customer Engagement team or a product, marketing, or design professional looking to get a leg up, Customer Analytics For Dummies has you covered.

Introduction 1

About This Book 1

Foolish Assumptions 2

Icons Used in This Book 2

Beyond the Book 3

Where to Go from Here 3

Part I: Getting Started with Customer Analytics 5

Chapter 1: Introducing Customer Analytics 7

Defining Customer Analytics 7

The benefits of customer analytics 8

Using customer analytics 11

Compiling Big and Small Data 12

Chapter 2: Embracing the Science and Art of Metrics 15

Adding up Quantitative Data 15

Discrete and continuous data 16

Levels of data 16

Variables 19

Quantifying Qualitative Data 20

Determining the Sample Size You Need 22

Estimating a confidence interval 24

Computing a 95% confidence interval 25

Determining What Data to Collect 27

Managing the Right Measure 28

Chapter 3: Planning a Customer Analytics Initiative 31

A Customer Analytics Initiative Overview 31

Defining the Scope and Outcome 33

Identifying the Metrics, Methods, and Tools 34

Setting a Budget 35

Determining the Correct Sample Size 36

Analyzing and Improving 37

Controlling the Results 38

Part II: Identifying Your Customers 41

Chapter 4: Segmenting Customers 43

Why Segment Customers 43

Segmenting by the Five W’s 47

Who 47

Where 48

What 49

When 52

Why 52

How 52

Analyzing the Data to Segment Your Customers 53

Step 1: Tabulate your data 53

Step 2: Cross-Tabbing 54

Step 3: Cluster Analysis 56

Step 4: Estimate the size of each segment 57

Step 5 Estimate the value of each segment 57

Chapter 5: Creating Customer Personas 61

Recognizing the Importance of Personas 61

Working with personas 64

Getting More Personal with Customer Data 66

Step 1: Collecting the appropriate data 66

Step 2: Dividing data 68

Step 3: Identifying and refining personas 68

Answering Questions with Personas 71

Chapter 6: Determining Customer Lifetime Value 75

Why your CLV is important 76

Applying CLV in Business 77

Calculating Lifetime Value 77

Estimating revenue 78

Calculating the CLV 80

Identifying profitable customers 82

Marketing to profitable customers 82

Part III: Analytics for the Customer Journey 85

Chapter 7: Mapping the Customer Journey 87

Working with the Traditional Marketing Funnel 87

What Is a Customer Journey Map? 91

Define the Customer Journey 93

Finding the data 93

Sketching the journey 94

Making the map more useful 101

Chapter 8: Determining Brand Awareness and Attitudes 103

Measuring Brand Awareness 103

Unaided awareness 104

Aided awareness 105

Measuring product or service knowledge 106

Measuring Brand Attitude 107

Identifying brand pillars 108

Checking brand affinity 108

Measuring Usage and Intent 110

Finding out past usage 110

Measuring future intent 110

Understanding the Key Drivers of Attitude 111

Structuring a Brand Assessment Survey 111

Chapter 9: Measuring Customer Attitudes 113

Gauging Customer Satisfaction 113

General satisfaction 114

Attitude versus satisfaction 115

Rating Usability with the SUS and SUPR-Q 117

System Usability Scale (SUS) 117

Standardized User Experience Percentile Rank Questionnaire (SUPR-Q) 120

Measuring task difficulty with SEQ 122

Scoring Brand Affection 123

Finding Expectations: Desirability and Luxury 125

Desirability 125

Luxury 125

Measuring Attitude Lift 126

Asking for Preferences 128

Finding Your Key Drivers of Customer Attitudes 129

Writing Effective Customer Attitude Questions 131

Chapter 10: Quantifying the Consideration and Purchase Phases 133

Identifying the Consideration Touchpoints 133

Company-driven touchpoints 134

Customer-driven touchpoints 134

Measuring the Customer-Driven Touchpoints 135

Measuring the Three R’s of Company-Driven Touchpoints 137

Reach 137

Resonance 137

Reaction 138

Measuring resonance and reaction 139

Tracking Conversions and Purchases 139

Tracking micro conversions 140

Creating micro-conversion opportunities 141

Setting up conversion tracking 142

Measuring conversion rates 142

Measuring Changes through A/ B Testing 143

Offline A/B testing 144

Online A/B testing 144

Testing multiple variables 148

Making the Most of Website Analytics 148

Chapter 11: Tracking Post-Purchase Behavior 151

Dealing with Cognitive Dissonance 152

Reducing dissonance 152

Turning dissonance into satisfaction 153

Tracking return rates 153

Measuring the Post-Purchase Touchpoints 154

Digging into the post-purchase touchpoints 155

Assessing post-purchase satisfaction ratings 158

Finding Problems Using Call Center Analysis 159

Finding the Root Cause with Cause-and-Effect Diagrams 160

Creating a cause-and-effect diagram 161

Chapter 12: Measuring Customer Loyalty 163

Measuring Customer Loyalty 164

Repurchase rate 164

Net Promoter Score 166

Bad profits 174

Finding Key Drivers of Loyalty 177

Valuing positive word of mouth 178

Valuing negative word of mouth 182

Part IV: Analytics for Product Development 185

Chapter 13: Developing Products That Customers Want 187

Gathering Input on Product Features 187

Finding Customers’ Top Tasks 188

Listing the tasks 189

Finding customers 189

Selecting five tasks 190

Graphing and analyzing 190

Taking an internal view 191

Conducting a Gap Analysis 193

Mapping Business Needs to Customer Requirements 194

Identifying customers’ wants and needs 195

Identifying the voice of the customer 196

Identifying the how’s (the voice of the company) 196

Building the relationship between the customer and company voices 197

Generating priorities 197

Examining priorities 198

Measuring Customer Delight with the Kano Model 199

Assessing the Value of Each Combination of Features 200

Finding Out Why Problems Occur 202

Chapter 14: Gaining Insights through a Usability Study 207

Recognizing the Principles of Usability 207

Conducting a Usability Test 208

Determining what you want to test 209

Identifying the goals 209

Outlining task scenarios 209

Recruiting users 212

Testing your users 215

Collecting metrics 216

Coding and analyzing your data 218

Summarizing and presenting the results 218

Considering the Different Types of Usability Tests 218

Finding and Reporting Usability Problems 221

Facilitating a Usability Study 225

Chapter 15: Measuring Findability and Navigation 231

Finding Your Areas of Findability 232

Identifying What Customers Want 233

Prepping for a Findability Test 235

Finding your baseline 235

Designing the study 235

Looking at your findability metrics 237

Conducting Your Findability Study 240

Determining sample size 240

Recruiting users 241

Analyzing the results 242

Improving Findability 244

Cross-linking products 244

Regrouping categories 245

Rephrasing the tasks 245

Measuring findability after changes 246

Chapter 16: Considering the Ethics of Customer Analytics 249

Getting Informed Consent 249

Facebook 250

OKCupid 251

Amazon and Orbitz 251

Mintcom 252

Deciding to Experiment 252

Part V: The Part of Tens 255

Chapter 17: Ten Customer Metrics You Should Collect 257

Chapter 18: Ten Methods to Improve the Customer Experience 263

Chapter 19: Ten Common Analytic Mistakes 267

Chapter 20: Ten Methods for Identifying Customer Needs 271

Appendix: Predicting with Customer Analytics 277

Finding Similarities and Associations 278

Visualizing associations 279

Quantifying the strength of a relationship 280

Associations between binary variables 284

Determining Causation 288

Randomized experimental study 288

Quasi-experimental design 289

Correlational study 290

Single-subjects study 290

Anecdotes 291

Predicting with Regression 291

Predicting with the regression line 293

Creating a regression equation in Excel 294

Multiple regression analysis 296

Predicting with binary data 300

Predicting Trends with Time Series Analysis 301

Exponential (non-linear) growth 304

Training and validation periods 306

Detecting Differences 308

Index 311

Jeff Sauro is a Six-Sigma trained statistical analyst and pioneer in quantifying the customer experience. He writes a weekly column at measuringu.com and has been an invited speaker at Fortune 500 companies, industry conferences, and as an expert witness.