Customer Analytics For Dummies

Lýsing:The easy way to grasp customer analytics Ensuring your customers are having positive...
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Customer Analytics For Dummies

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Rafbók til eignar. Rafbók til eignar þarf að hlaða niður á þau tæki sem þú vilt nota innan eins árs frá því bókin er keypt. Útgáfa: 1

Efnisyfirlit

  • 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

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