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Customer Analytics For Dummies

Book 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.

    Table of Contents

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