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.

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

Select Guide Rating

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