Cover image for Ask, Measure, Learn

Book description

This non-technical guide shows you how to extract significant business value from big data with Ask-Measure-Learn, a system that helps you ask the right questions, measure the right data, and then learn from the results. Two experts provide business managers and analysts with a high-level overview of the system, and demonstrate specific ways to apply social media analytics to marketing, sales, public relations, and customer management, using examples and case studies.

Table of Contents

  1. Ask, Measure, Learn
  2. Dedication
  3. Dedication
  4. Introduction
    1. The Fourth V of Data
    2. The Promise
    3. The Data Focus
      1. More Data
      2. Better Technology
    4. Analytics Focus
    5. What This Book Covers
      1. Chapter Outline
    6. Safari® Books Online
    7. How to Contact Us
  5. Acknowledgments
  6. I. Media Measurement by Function
    1. 1. Marketing
      1. Marketing and Social Media: The Promise and the Reality
      2. Three Myths about Social Media
        1. Social Media Is Cheap
        2. Social Media Is Fast
        3. Social Media Is Just Another Channel
      3. Branding
        1. Social Media: A New Class of Metrics
        2. Reach Does Not Equal Awareness
        3. Case: Virgin Atlantic Airways
          1. Where is Linda?
          2. Return on Investment: Was Linda Worth It?
      4. Purchase Intent
        1. How to Find Purchase Intent
        2. Behavioral Targeting
        3. Social Targeting
          1. You Say It...
          2. You Like It...
          3. Your Friends Like It...
        4. Homophily versus Influence
        5. Social Connections versus Behavior
        6. The Influencer
      5. Summary
        1. Workbook
    2. 2. Sales
      1. Introduction
        1. Social Sales
        2. Data-Driven Sales
      2. Reach Versus Intention
        1. Social Confirmation Creates Trust
          1. User Ratings
          2. User comments
        2. Peer Pressure
        3. Do Social Confirmation and Peer Pressure Work?
        4. What—or Who—Would Make You Buy?
      3. Recommendation Systems
        1. Collaborative Recommendations
        2. Content-Based Recommendations
      4. The Technology of Recommendation Systems
        1. The Cold-Start Problem
        2. Not Enough Data
        3. No Surprises
        4. How to Build a Recommendation System: Start Small
      5. Trust, Personality, and Reason
        1. Personal Relationships
        2. Reason
      6. Summary
        1. Workbook
    3. 3. Public Relations
      1. PR Often Has No Measurable ROI
      2. Measuring People
        1. Reach in PR
        2. Context in PR
          1. Context of the content
          2. Context of the author
        3. Journalism CRM
        4. Communication Is Human
          1. Six principles of influence
      3. Measuring Distributing
        1. Clipping
        2. The Myth of Number of Articles
        3. Reading Lists
        4. Engagement
          1. Clicking
          2. Sharing, Liking, Thumbs Up
          3. Commenting
          4. Copying
        5. Effort versus Impact
        6. Case: Spread of the Idea of “Resilient India”
      4. PR to Warn
        1. Examples of PR disasters
          1. Inappropriate selling
          2. Underestimation of virality
          3. Self-censorship
          4. Impersonation
        2. No Early Warning Systems
          1. Nondeterministic
          2. Speed to disaster
        3. Case: McDonald’s
        4. Warning Signals
      5. Summary
        1. Workbook
    4. 4. Customer Care
      1. New Voice of the Customer
        1. Dell Hell
        2. United Breaks Guitars
      2. Customer Care 2.0
        1. Knowledge Bases and Customer Self-Service
        2. Happier Employees
        3. Smart Selection
        4. Positive Publicity
      3. Dos and Don’ts
        1. Get Clients into Your Service Channel
        2. Mind the Trolls
        3. Resources and Scaling
      4. Is Social Customer Care the New Commodity?
      5. Automation and Business Intelligence
        1. A Case of Airline Customer Satisfaction
        2. Sentiment Algorithm
          1. Anaphora Algorithm
          2. Context
        3. Can we use it at all?
          1. Case Sony Ericsson—special words
        4. A Dynamic Approach to Machine Learning
          1. Case: newBrandAnalytics
          2. Case: Dell’s customer care
      6. Summary
        1. Workbook
    5. 5. Social CRM: Market Research
      1. Case Study: Customer Lifecycle
      2. Analytical CRM: The New Frontier
        1. Issues with the Traditional Way
        2. Turning CRM Around
        3. Facebook and Open Graph
      3. Which Data?
        1. Social Media: Too Shallow?
        2. Personal Data: Too Sensitive?
      4. Summary
        1. Workbook
    6. 6. Gaming the System
      1. Spam and Robots
      2. Creating Reach
      3. How to Spot Bots
      4. Smearing Opponents
        1. John Sununu
        2. Follower Scandals
      5. Creating Influence and Intention
        1. A Turing Test on Twitter
        2. The US Military’s Search for Social Media Robots
      6. Spreading Paid Opinions: Grassroots and Astroturfing
        1. SOPA and PIPA Act: A Modern Grassroots Movement
        2. Microsoft’s Antitrust Case
        3. China’s 50-Cent Bloggers
        4. Cause, Access, and Reach
      7. Contagiousness
        1. Kony2012
        2. Viral by Design
        3. The Truth about the Truth
        4. How to Spot Attempts to Create Contagiousness
      8. The Opposite of Virality: Suppressing Messages
      9. Blurry Lines
        1. The Case of Facebook
      10. Summary
        1. Workbook
    7. 7. Predictions
      1. Predicting the Future
      2. Prediction of Learning
      3. Predicting Elections
        1. Selection Bias
        2. Bad PR Bias
        3. Predicting Voting Behavior
      4. Predicting Box Offices
        1. The Movie Industry
        2. Insights with Caution
        3. Conclusion
      5. Predicting the Stock Market
      6. Closing Predictions
      7. Workbook Questions
  7. II. Build Your Own Ask-Measure-Learn System
    1. 8. Ask the Right Question
      1. Case Study: Major Telecom Company
        1. Background Knowledge
        2. Was He Heard?
      2. Formulate the Question
        1. Creative Discovery
        2. Domain Knowledge
        3. The Right Question
      3. An Industry in Search of a Question
      4. Summary
        1. Workbook Questions
    2. 9. Use the Right Data
      1. Which Data Is Important?
        1. Causation
          1. Correlation versus causation
            1. Direct effect
            2. Reverse effect
            3. The unknown third
            4. All of the above
        2. Testing for Correlation
        3. Error, or Why Structured Data Is Superior
          1. Structured
          2. Unstructured
        4. Cost and Insider Knowledge
        5. Case: A Matchmaking Engine
      2. Data Selection
        1. Sampling
        2. Subsets
          1. I know keywords
          2. No truth
        3. Case: Haiti
      3. Summary
        1. Workbook
    3. 10. Define the Right Measurement
      1. Examples of Social Media Metrics
        1. Influence
        2. Consumer Preference
        3. The Quest for ROI
      2. The Risks of Metrics
        1. Influencing the metric
        2. Wrong behavior
        3. Changes Over Time and Space
        4. Overcoming the issues
      3. Summary
        1. Workbook
  8. III. Appendix
    1. A. All Names
      1. Endorsement
      2. Introduction
      3. Chapter 1, Marketing
      4. Chapter 2, Sales
      5. Chapter 3, Public Relations
      6. Chapter 4, Customer Care
      7. Chapter 5, Social CRM: Market Research
      8. Chapter 6, Gaming the System
      9. Chapter 7, Predictions
      10. Chapter 8, Ask the Right Question
      11. Chapter 9, Use the Right Data
      12. Chapter 10, Define the Right Measurement
  9. Index
  10. About the Authors
  11. Colophon
  12. Copyright