| Introduction |
|
1 | (4) |
|
|
|
1 | (1) |
|
|
|
2 | (1) |
|
|
|
2 | (1) |
|
|
|
3 | (1) |
|
|
|
3 | (2) |
| Part I: Getting Started with Customer Analytics |
|
5 | (36) |
|
Chapter 1 Introducing Customer Analytics |
|
|
7 | (8) |
|
Defining Customer Analytics |
|
|
7 | (5) |
|
The benefits of customer analytics |
|
|
8 | (3) |
|
|
|
11 | (1) |
|
Compiling Big and Small Data |
|
|
12 | (3) |
|
Chapter 2 Embracing the Science and Art of Metrics |
|
|
15 | (16) |
|
Adding up Quantitative Data |
|
|
15 | (5) |
|
Discrete and continuous data |
|
|
16 | (1) |
|
|
|
16 | (3) |
|
|
|
19 | (1) |
|
Quantifying Qualitative Data |
|
|
20 | (2) |
|
Determining the Sample Size You Need |
|
|
22 | (5) |
|
Estimating a confidence interval |
|
|
24 | (1) |
|
Computing a 95% confidence interval |
|
|
25 | (2) |
|
Determining What Data to Collect |
|
|
27 | (1) |
|
Managing the Right Measure |
|
|
28 | (3) |
|
Chapter 3 Planning a Customer Analytics Initiative |
|
|
31 | (10) |
|
A Customer Analytics Initiative Overview |
|
|
31 | (2) |
|
Defining the Scope and Outcome |
|
|
33 | (1) |
|
Identifying the Metrics, Methods, and Tools |
|
|
34 | (1) |
|
|
|
35 | (1) |
|
Determining the Correct Sample Size |
|
|
36 | (1) |
|
|
|
37 | (1) |
|
|
|
38 | (3) |
| Part II: Identifying Your Customers |
|
41 | (44) |
|
Chapter 4 Segmenting Customers |
|
|
43 | (18) |
|
|
|
43 | (4) |
|
Segmenting by the Five W's |
|
|
47 | (6) |
|
|
|
47 | (1) |
|
|
|
48 | (1) |
|
|
|
49 | (3) |
|
|
|
52 | (1) |
|
|
|
52 | (1) |
|
|
|
52 | (1) |
|
Analyzing the Data to Segment Your Customers |
|
|
53 | (8) |
|
Step 1: Tabulate your data |
|
|
53 | (1) |
|
|
|
54 | (2) |
|
|
|
56 | (1) |
|
Step 4: Estimate the size of each segment |
|
|
57 | (1) |
|
Step 5: Estimate the value of each segment |
|
|
57 | (4) |
|
Chapter 5 Creating Customer Personas |
|
|
61 | (14) |
|
Recognizing the Importance of Personas |
|
|
61 | (5) |
|
|
|
64 | (2) |
|
Getting More Personal with Customer Data |
|
|
66 | (5) |
|
Step 1: Collecting the appropriate data |
|
|
66 | (2) |
|
|
|
68 | (1) |
|
Step 3: Identifying and refining personas |
|
|
68 | (3) |
|
Answering Questions with Personas |
|
|
71 | (4) |
|
Chapter 6 Determining Customer Lifetime Value |
|
|
75 | (10) |
|
Why your CLV is important |
|
|
76 | (1) |
|
|
|
77 | (1) |
|
Calculating Lifetime Value |
|
|
77 | (5) |
|
|
|
78 | (2) |
|
|
|
80 | (2) |
|
Identifying profitable customers |
|
|
82 | (1) |
|
Marketing to profitable customers |
|
|
82 | (3) |
| Part III: Analytics for the Customer Journey |
|
85 | (100) |
|
Chapter 7 Mapping the Customer Journey |
|
|
87 | (16) |
|
Working with the Traditional Marketing Funnel |
|
|
87 | (4) |
|
What Is a Customer Journey Map? |
|
|
91 | (2) |
|
Define the Customer Journey |
|
|
93 | (10) |
|
|
|
93 | (1) |
|
|
|
94 | (7) |
|
Making the map more useful |
|
|
101 | (2) |
|
Chapter 8 Determining Brand Awareness and Attitudes |
|
|
103 | (10) |
|
Measuring Brand Awareness |
|
|
103 | (4) |
|
|
|
104 | (1) |
|
|
|
105 | (1) |
|
Measuring product or service knowledge |
|
|
106 | (1) |
|
|
|
107 | (3) |
|
Identifying brand pillars |
|
|
108 | (1) |
|
|
|
108 | (2) |
|
Measuring Usage and Intent |
|
|
110 | (1) |
|
|
|
110 | (1) |
|
|
|
110 | (1) |
|
Understanding the Key Drivers of Attitude |
|
|
111 | (1) |
|
Structuring a Brand Assessment Survey |
|
|
111 | (2) |
|
Chapter 9 Measuring Customer Attitudes |
|
|
113 | (20) |
|
Gauging Customer Satisfaction |
|
|
113 | (4) |
|
|
|
114 | (1) |
|
Attitude versus satisfaction |
|
|
115 | (2) |
|
Rating Usability with the SUS and SUPR-Q |
|
|
117 | (6) |
|
System Usability Scale (SUS) |
|
|
117 | (3) |
|
Standardized User Experience Percentile Rank Questionnaire (SUPR-Q) |
|
|
120 | (2) |
|
Measuring task difficulty with SEQ |
|
|
122 | (1) |
|
|
|
123 | (2) |
|
Finding Expectations: Desirability and Luxury |
|
|
125 | (1) |
|
|
|
125 | (1) |
|
|
|
125 | (1) |
|
|
|
126 | (2) |
|
|
|
128 | (1) |
|
Finding Your Key Drivers of Customer Attitudes |
|
|
129 | (2) |
|
Writing Effective Customer Attitude Questions |
|
|
131 | (2) |
|
Chapter 10 Quantifying the Consideration and Purchase Phases |
|
|
133 | (18) |
|
Identifying the Consideration Touchpoints |
|
|
133 | (2) |
|
Company-driven touchpoints |
|
|
134 | (1) |
|
Customer-driven touchpoints |
|
|
134 | (1) |
|
Measuring the Customer-Driven Touchpoints |
|
|
135 | (2) |
|
Measuring the Three R's of Company-Driven Touchpoints |
|
|
137 | (2) |
|
|
|
137 | (1) |
|
|
|
137 | (1) |
|
|
|
138 | (1) |
|
Measuring resonance and reaction |
|
|
139 | (1) |
|
Tracking Conversions and Purchases |
|
|
139 | (4) |
|
Tracking micro conversions |
|
|
140 | (1) |
|
Creating micro-conversion opportunities |
|
|
141 | (1) |
|
Setting up conversion tracking |
|
|
142 | (1) |
|
Measuring conversion rates |
|
|
142 | (1) |
|
Measuring Changes through A/B Testing |
|
|
143 | (5) |
|
|
|
144 | (1) |
|
|
|
144 | (4) |
|
Testing multiple variables |
|
|
148 | (1) |
|
Making the Most of Website Analytics |
|
|
148 | (3) |
|
Chapter 11 Tracking Post-Purchase Behavior |
|
|
151 | (12) |
|
Dealing with Cognitive Dissonance |
|
|
152 | (2) |
|
|
|
152 | (1) |
|
Turning dissonance into satisfaction |
|
|
153 | (1) |
|
|
|
153 | (1) |
|
Measuring the Post-Purchase Touchpoints |
|
|
154 | (5) |
|
Digging into the post-purchase touchpoints |
|
|
155 | (3) |
|
Assessing post-purchase satisfaction ratings |
|
|
158 | (1) |
|
Finding Problems Using Call Center Analysis |
|
|
159 | (1) |
|
Finding the Root Cause with Cause-and-Effect Diagrams |
|
|
160 | (3) |
|
Creating a cause-and-effect diagram |
|
|
161 | (2) |
|
Chapter 12 Measuring Customer Loyalty |
|
|
163 | (22) |
|
Measuring Customer Loyalty |
|
|
164 | (13) |
|
|
|
164 | (2) |
|
|
|
166 | (8) |
|
|
|
174 | (3) |
|
Finding Key Drivers of Loyalty |
|
|
177 | (10) |
|
Valuing positive word of mouth |
|
|
178 | (4) |
|
Valuing negative word of mouth |
|
|
182 | (3) |
| Part IV: Analytics for Product Development |
|
185 | (70) |
|
Chapter 13 Developing Products That Customers Want |
|
|
187 | (20) |
|
Gathering Input on Product Features |
|
|
187 | (1) |
|
Finding Customers' Top Tasks |
|
|
188 | (5) |
|
|
|
189 | (1) |
|
|
|
189 | (1) |
|
|
|
190 | (1) |
|
|
|
190 | (1) |
|
|
|
191 | (2) |
|
Conducting a Gap Analysis |
|
|
193 | (1) |
|
Mapping Business Needs to Customer Requirements |
|
|
194 | (5) |
|
Identifying customers' wants and needs |
|
|
195 | (1) |
|
Identifying the voice of the customer |
|
|
196 | (1) |
|
Identifying the how's (the voice of the company) |
|
|
196 | (1) |
|
Building the relationship between the customer and company voices |
|
|
197 | (1) |
|
|
|
197 | (1) |
|
|
|
198 | (1) |
|
Measuring Customer Delight with the Kano Model |
|
|
199 | (1) |
|
Assessing the Value of Each Combination of Features |
|
|
200 | (2) |
|
Finding Out Why Problems Occur |
|
|
202 | (5) |
|
Chapter 14 Gaining Insights through a Usability Study |
|
|
207 | (24) |
|
Recognizing the Principles of Usability |
|
|
207 | (1) |
|
Conducting a Usability Test |
|
|
208 | (10) |
|
Determining what you want to test |
|
|
209 | (1) |
|
|
|
209 | (1) |
|
|
|
209 | (3) |
|
|
|
212 | (3) |
|
|
|
215 | (1) |
|
|
|
216 | (2) |
|
Coding and analyzing your data |
|
|
218 | (1) |
|
Summarizing and presenting the results |
|
|
218 | (1) |
|
Considering the Different Types of Usability Tests |
|
|
218 | (3) |
|
Finding and Reporting Usability Problems |
|
|
221 | (4) |
|
Facilitating a Usability Study |
|
|
225 | (6) |
|
Chapter 15 Measuring Findability and Navigation |
|
|
231 | (18) |
|
Finding Your Areas of Findability |
|
|
232 | (1) |
|
Identifying What Customers Want |
|
|
233 | (2) |
|
Prepping for a Findability Test |
|
|
235 | (5) |
|
|
|
235 | (1) |
|
|
|
235 | (2) |
|
Looking at your findability metrics |
|
|
237 | (3) |
|
Conducting Your Findability Study |
|
|
240 | (4) |
|
|
|
240 | (1) |
|
|
|
241 | (1) |
|
|
|
242 | (2) |
|
|
|
244 | (5) |
|
|
|
244 | (1) |
|
|
|
245 | (1) |
|
|
|
245 | (1) |
|
Measuring findability after changes |
|
|
246 | (3) |
|
Chapter 16 Considering the Ethics of Customer Analytics |
|
|
249 | (6) |
|
|
|
249 | (3) |
|
|
|
250 | (1) |
|
|
|
251 | (1) |
|
|
|
251 | (1) |
|
|
|
252 | (1) |
|
|
|
252 | (3) |
| Part V: The Part of Tens |
|
255 | (22) |
|
Chapter 17 Ten Customer Metrics You Should Collect |
|
|
257 | (6) |
|
Chapter 18 Ten Methods to Improve the Customer Experience |
|
|
263 | (4) |
|
Chapter 19 Ten Common Analytic Mistakes |
|
|
267 | (4) |
|
Chapter 20 Ten Methods for Identifying Customer Needs |
|
|
271 | (6) |
| Appendix: Predicting With Customer Analytics |
|
277 | (34) |
|
Finding Similarities and Associations |
|
|
278 | (10) |
|
|
|
279 | (1) |
|
Quantifying the strength of a relationship |
|
|
280 | (4) |
|
Associations between binary variables |
|
|
284 | (4) |
|
|
|
288 | (3) |
|
Randomized experimental study |
|
|
288 | (1) |
|
Quasi-experimental design |
|
|
289 | (1) |
|
|
|
290 | (1) |
|
|
|
290 | (1) |
|
|
|
291 | (1) |
|
Predicting with Regression |
|
|
291 | (10) |
|
Predicting with the regression line |
|
|
293 | (1) |
|
Creating a regression equation in Excel |
|
|
294 | (2) |
|
Multiple regression analysis |
|
|
296 | (4) |
|
Predicting with binary data |
|
|
300 | (1) |
|
Predicting Trends with Time Series Analysis |
|
|
301 | (7) |
|
Exponential (non-linear) growth |
|
|
304 | (2) |
|
Training and validation periods |
|
|
306 | (2) |
|
|
|
308 | (3) |
| Index |
|
311 | |
