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Social Media Analytics: Techniques and Insights for Extracting Business Value Out of Social Media

Book Description

Transform Raw Social Media Data into Real Competitive Advantage

There’s real competitive advantage buried in today’s deluge of social media data. If you know how to analyze it, you can increase your relevance to customers, establishing yourself as a trusted supplier in a cutthroat environment where consumers rely more than ever on “public opinion” about your products, services, and experiences.

Social Media Analytics is the complete insider’s guide for all executives and marketing analysts who want to answer mission-critical questions and maximize the business value of their social media data. Two leaders of IBM’s pioneering Social Media Analysis Initiative offer thorough and practical coverage of the entire process: identifying the right unstructured data, analyzing it, and interpreting and acting on the knowledge you gain.

Their expert guidance, practical tools, and detailed examples will help you learn more from all your social media conversations, and avoid pitfalls that can lead to costly mistakes.

You’ll learn how to:

  • Focus on the questions that social media data can realistically answer

  • Determine which information is actually useful to you—and which isn’t

  • Cleanse data to find and remove inaccuracies

  • Create data models that accurately represent your data and lead to more useful answers

  • Use historical data to validate hypotheses faster, so you don’t waste time

  • Identify trends and use them to improve predictions

  • Drive value “on-the-fly” from real-time/ near-real-time and ad hoc analyses

  • Analyze text, a.k.a. “data at rest”

  • Recognize subtle interrelationships that impact business performance

  • Improve the accuracy of your sentiment analyses

  • Determine eminence, and distinguish “talkers” from true influencers

  • Optimize decisions about marketing and advertising spend

  • Whether you’re a marketer, analyst, manager, or technologist, you’ll learn how to use social media data to compete more effectively, respond more rapidly, predict more successfully…grow profits, and keep them growing.

    Table of Contents

    1. About This E-Book
    2. Title Page
    3. Copyright Page
    4. Dedication Page
    5. Contents
    6. Foreword
    7. Preface: Mining for Gold (or Digging in the Mud)
      1. Just What Do We Mean When We Say Social Media?
      2. Why Look at This Data?
      3. How Does This Translate into Business Value?
      4. The Book’s Approach
        1. Data Identification
        2. Data Analysis
        3. Information Interpretation
      5. Why You Should Read This Book
      6. What This Book Does and Does Not Focus On
      7. Endnotes
    8. Acknowledgments
      1. Matt Ganis
      2. Avinash Kohirkar
      3. Joint Acknowledgments
    9. About the Authors
    10. Part I: Data Identification
      1. 1. Looking for Data in all the Right Places
        1. What Data Do We Mean?
        2. What Subset of Content Are We Interested In?
        3. Whose Comments Are We Interested In?
        4. What Window of Time Are We Interested In?
        5. Attributes of Data That Need to Be Considered
          1. Structure
          2. Language
          3. Region
          4. Type of Content
          5. Venue
          6. Time
          7. Ownership of Data
        6. Summary
        7. Endnotes
      2. 2. Separating the Wheat from the Chaff
        1. It All Starts with Data
        2. Casting a Net
        3. Regular Expressions
        4. A Few Words of Caution
        5. It’s Not What You Say but WHERE You Say It
        6. Summary
        7. Endnotes
      3. 3. Whose Comments Are We Interested In?
        1. Looking for the Right Subset of People
          1. Employment
          2. Sentiment
          3. Location or Geography
          4. Language
          5. Age
          6. Gender
          7. Profession/Expertise
          8. Eminence or Popularity
          9. Role
          10. Specific People or Groups
          11. Do We Really Want ALL the Comments?
        2. Are They Happy or Unhappy?
        3. Location and Language
        4. Age and Gender
        5. Eminence, Prestige, or Popularity
        6. Summary
        7. Endnotes
      4. 4. Timing Is Everything
        1. Predictive Versus Descriptive
          1. Predictive Analytics
          2. Descriptive Analytics
        2. Sentiment
        3. Time as Your Friend
        4. Summary
        5. Endnotes
      5. 5. Social Data: Where and Why
        1. Structured Data Versus Unstructured Data
        2. Big Data
          1. Social Media as Big Data
          2. Where to Look for Big Data
        3. Paradox of Choice: Sifting Through Big Data
        4. Identifying Data in Social Media Outlets
          1. Professional Networking Sites
          2. Social Sites
          3. Information Sharing Sites
          4. Microblogging Sites
          5. Blogs/Wikis
        5. Summary
        6. Endnotes
    11. Part II: Data Analysis
      1. 6. The Right Tool for the Right Job
        1. The Four Dimensions of Analysis Taxonomy
        2. Depth of Analysis
        3. Machine Capacity
        4. Domain of Analysis
          1. External Social Media
          2. Internal Social Media
        5. Velocity of Data
          1. Data in Motion
          2. Data at Rest
        6. Summary
        7. Endnotes
      2. 7. Reading Tea Leaves: Discovering Themes, Topics, or Trends
        1. Validating the Hypothesis
          1. Youth Unemployment
          2. Cannes Lions 2013
          3. 56th Grammy Awards
        2. Discovering Themes and Topics
          1. Business Value of Projects
          2. Analysis of the Information in the Business Value Field
          3. Our Findings
        3. Using Iterative Methods
        4. Summary
        5. Endnotes
      3. 8. Fishing in a Fast-Flowing River
        1. Is There Value in Real Time?
        2. Real Time Versus Near Real Time
        3. Forewarned Is Forearmed
        4. Stream Computing
        5. IBM InfoSphere Streams
        6. SPL Applications
        7. Directed Graphs
        8. Streams Example: SSM
          1. Step 1
          2. Step 2
          3. Step 3
          4. Step 4
          5. Steps 5 and 6
          6. Steps 7 and 8
        9. Value Derived from a Conference Using Real-Time Analytics
        10. Summary
        11. Endnotes
      4. 9. If You Don’t Know What You Want, You Just May Find It: Ad Hoc Analysis
        1. Ad Hoc Analysis
        2. An Example of Ad Hoc Analysis
        3. Data Integrity
        4. Summary
        5. Endnotes
      5. 10. Rivers Run Deep: Deep Analysis
        1. Responding to Leads Identified in Social Media
          1. Identifying Leads
          2. Qualifying/Classifying Leads
          3. Suggested Action
        2. Support for Deep Analysis in Analytics Software
          1. Topic Evolution
          2. Affinity Analysis in Reporting
        3. Summary
        4. Endnotes
      6. 11. The Enterprise Social Network
        1. Social Is Much More Than Just Collaboration
          1. Transparency of Communication
          2. Frictionless Redistribution of Knowledge
          3. Deconstructing Knowledge Creation
          4. Serendipitous Discovery and Innovation
        2. Enterprise Social Network Is the Memory of the Organization
        3. Understanding the Enterprise Graph
        4. Personal Social Dashboard: Details of Implementation
          1. Key Performance Indicators (KPIs)
          2. Assessing Business Benefits from Social Graph Data
        5. What’s Next for the Enterprise Graph?
        6. Summary
        7. Endnotes
    12. Part III: Information Interpretation
      1. 12. Murphy Was Right! The Art of What Could Go Wrong
        1. Recap: The Social Analytics Process
        2. Finding the Right Data
        3. Communicating Clearly
        4. Choosing Filter Words Carefully
        5. Understanding That Sometimes Less Is More
        6. Customizing and Modifying Tools
        7. Using the Right Tool for the Right Job
        8. Analyzing Consumer Reaction During Hurricane Sandy
        9. Summary
        10. Endnotes
      2. 13. Visualization as an Aid to Analytics
        1. Common Visualizations
          1. Pie Charts
          2. Bar Charts
          3. Line Charts
          4. Scatter Plots
        2. Common Pitfalls
          1. Information Overload
          2. The Unintended Consequences of Using 3D
          3. Using Too Much Color
        3. Visually Representing Unstructured Data
        4. Summary
        5. Endnotes
    13. Appendices
      1. A. Case Study
        1. Introduction to the Case Study: IBMAmplify
        2. Data Identification
          1. Taking a First Pass at the Analysis
        3. Data Analysis
          1. A Second Attempt at Analyzing the Data
        4. Information Interpretation
        5. Conclusions
    14. Index
    15. Code Snippets