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• Introduction
• About This Book
• Foolish Assumptions
• Icons Used in This Book
• Beyond the Book
• Where to Go from Here
• Part I: Getting Started with Customer Analytics
• Chapter 1: Introducing Customer Analytics
• Defining Customer Analytics
• The benefits of customer analytics
• Using customer analytics
• Compiling Big and Small Data
• Chapter 2: Embracing the Science and Art of Metrics
• Adding up Quantitative Data
• Discrete and continuous data
• Levels of data
• Variables
• Quantifying Qualitative Data
• Determining the Sample Size You Need
• Estimating with a confidence interval
• Computing a 95% confidence interval
• Determining What Data to Collect
• Managing the Right Measure
• Chapter 3: Planning a Customer Analytics Initiative
• A Customer Analytics Initiative Overview
• Defining the Scope and Outcome
• Identifying the Metrics, Methods, and Tools
• Setting a Budget
• Determining the Correct Sample Size
• Analyzing and Improving
• Controlling the Results
• Part II: Identifying Your Customers
• Chapter 4: Segmenting Customers
• Why Segment Customers
• Segmenting by the Five W’s
• Who
• Where
• What
• When
• Why
• How
• Analyzing the Data to Segment Your Customers
• Step 1: Tabulate your data
• Step 2: Cross-Tabbing
• Step 3: Cluster Analysis
• Step 4: Estimate the size of each segment
• Step 5. Estimate the value of each segment
• Chapter 5: Creating Customer Personas
• Recognizing the Importance of Personas
• Working with personas
• Getting More Personal with Customer Data
• Step 1: Collecting the appropriate data
• Step 2: Dividing data
• Step 3: Identifying and refining personas
• Answering Questions with Personas
• Chapter 6: Determining Customer Lifetime Value
• Why Your CLV Is Important
• Applying CLV in Business
• Calculating Lifetime Value
• Estimating revenue
• Calculating the CLV
• Identifying profitable customers
• Marketing to Profitable Customers
• Part III: Analytics for the Customer Journey
• Chapter 7: Mapping the Customer Journey
• Working with the Traditional Marketing Funnel
• What Is a Customer Journey Map?
• Define the Customer Journey
• Finding the data
• Sketching the journey
• Making the map more useful
• Chapter 8: Determining Brand Awareness and Attitudes
• Measuring Brand Awareness
• Unaided awareness
• Aided awareness
• Measuring product or service knowledge
• Measuring Brand Attitude
• Identifying brand pillars
• Checking brand affinity
• Measuring Usage and Intent
• Finding out past usage
• Measuring future intent
• Understanding the Key Drivers of Attitude
• Structuring a Brand Assessment Survey
• Chapter 9: Measuring Customer Attitudes
• Gauging Customer Satisfaction
• General satisfaction
• Attitude versus satisfaction
• Rating Usability with the SUS and SUPR-Q
• System Usability Scale (SUS)
• Standardized User Experience Percentile Rank Questionnaire (SUPR-Q)
• Measuring task difficulty with SEQ
• Scoring Brand Affection
• Finding Expectations: Desirability and Luxury
• Desirability
• Luxury
• Measuring Attitude Lift
• Asking for Preferences
• Finding Your Key Drivers of Customer Attitudes
• Writing Effective Customer Attitude Questions
• Chapter 10: Quantifying the Consideration and Purchase Phases
• Identifying the Consideration Touchpoints
• Company-driven touchpoints
• Customer-driven touchpoints
• Measuring the Customer-Driven Touchpoints
• Measuring the Three R’s of Company-Driven Touchpoints
• Reach
• Resonance
• Reaction
• Measuring resonance and reaction
• Tracking Conversions and Purchases
• Tracking micro conversions
• Creating micro-conversion opportunities
• Setting up conversion tracking
• Measuring conversion rates
• Measuring Changes through A/ B Testing
• Offline A/B testing
• Online A/B testing
• Testing multiple variables
• Making the Most of Website Analytics
• Chapter 11: Tracking Post-Purchase Behavior
• Dealing with Cognitive Dissonance
• Reducing dissonance
• Turning dissonance into satisfaction
• Tracking return rates
• Measuring the Post-Purchase Touchpoints
• Digging into the post-purchase touchpoints
• Assessing post-purchase satisfaction ratings
• Finding Problems Using Call Center Analysis
• Finding the Root Cause with Cause-and-Effect Diagrams
• Creating a cause-and-effect diagram
• Chapter 12: Measuring Customer Loyalty
• Measuring Customer Loyalty
• Repurchase rate
• Net Promoter Score
• Bad profits
• Finding Key Drivers of Loyalty
• Valuing positive word of mouth
• Valuing negative word of mouth
• Part IV: Analytics for Product Development
• Chapter 13: Developing Products That Customers Want
• Gathering Input on Product Features
• Finding Customers’ Top Tasks
• Listing the tasks
• Finding customers
• Selecting five tasks
• Graphing and analyzing
• Taking an internal view
• Conducting a Gap Analysis
• Mapping Business Needs to Customer Requirements
• Identifying customers’ wants and needs
• Identifying the voice of the customer
• Identifying the How’s (the voice of the company)
• Building the relationship between the customer and company voices
• Generating priorities
• Examining priorities
• Measuring Customer Delight with the Kano Model
• Assessing the Value of Each Combination of Features
• Finding Out Why Problems Occur
• Chapter 14: Gaining Insights through a Usability Study
• Recognizing the Principles of Usability
• Conducting a Usability Test
• Determining what you want to test
• Identifying the goals
• Outlining task scenarios
• Recruiting users
• Testing your users
• Collecting metrics
• Coding and analyzing your data
• Summarizing and presenting the results
• Considering the Different Types of Usability Tests
• Finding and Reporting Usability Problems
• Facilitating a Usability Study
• Chapter 15: Measuring Findability and Navigation
• Finding Your Areas of Findability
• Identifying What Customers Want
• Prepping for a Findability Test
• Finding your baseline
• Designing the study
• Looking at your findability metrics
• Conducting Your Findability Study
• Determining sample size
• Recruiting users
• Analyzing the results
• Improving Findability
• Cross-linking products
• Regrouping categories
• Rephrasing the tasks
• Measuring findability after changes
• Chapter 16: Considering the Ethics of Customer Analytics
• Getting Informed Consent
• Facebook
• OKCupid
• Amazon and Orbitz
• Mint.com
• Deciding to Experiment
• Part V: The Part of Tens
• Chapter 17: Ten Customer Metrics You Should Collect
• Customer Revenue
• Customer Satisfaction
• Customer Profitability
• Customer Lifetime Value
• Brand Awareness
• Top Tasks
• Customer Loyalty
• Conversion Rate
• Completion Rate
• Churn Rate
• Chapter 18: Ten Methods to Improve the Customer Experience
• True Intent/Voice of Customer Study
• Customer Segmentation
• Persona Development
• Journey Mapping
• Top-Task Analysis
• Usability Study
• Findability Study
• Conjoint Analysis
• Key Driver Analysis
• Gap Analysis
• Chapter 19: Ten Common Analytic Mistakes
• Optimizing around the Wrong Metric
• Relying Too Much on Behavioral or Attitudinal Data
• Not Having a Large Enough Sample Size
• Eyeballing Data and Patterns
• Confusing Statistical Significance with Practical Significance
• Not Having an Interdisciplinary Team
• Not Cleaning Your Data First
• Improperly Formatted Data
• Not Having Clear Research Questions to Answer
• Waiting for Perfect Data
• Chapter 20: Ten Methods for Identifying Customer Needs
• Starting with Existing Data
• Interviewing Stakeholders
• Mapping the Customer Process
• Mapping the Customer Journey
• Conducting “Follow Me Home” Research
• Interviewing Customers
• Conducting Voice of Customer Surveys
• Analyzing Your Competition
• Analyzing Cause-and-Effect Relationships
• Recording Experiences through Diary Studies
• Appendix: Predicting with Customer Analytics
• Finding Similarities and Associations
• Visualizing associations
• Quantifying the strength of a relationship
• Associations between binary variables
• Determining Causation
• Randomized experimental study
• Quasi-experimental design
• Correlational study
• Single-subjects study
• Anecdotes
• Predicting with Regression
• Predicting with the regression line
• Creating a regression equation in Excel
• Multiple regression analysis
• Predicting with binary data
• Predicting Trends with Time Series Analysis
• Exponential (non-linear) growth
• Training and validation periods
• Detecting Differences
• About the Author
• Cheat Sheet
• More Dummies Products