Customer Analytics For Dummies
Published on 24. March 2015
Book
Paperback/Softback
336 pages
978-1-118-93759-4 (ISBN)
Description
The easy way to grasp customer analytics
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.
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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.
Content
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
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