- ホーム
- > 洋書
- > 英文書
- > Business / Economics
Full Description
Transform Raw Social Media Data into Real Competitive AdvantageThere'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 spendWhether 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.
Contents
Foreword xviiiPreface: Mining for Gold (or Digging in the Mud) xxJust What Do We Mean When We Say Social Media? xxWhy Look at This Data? xxiHow Does This Translate into Business Value? xxiiThe Book's Approach xxivData Identification xxivData Analysis xxvInformation Interpretation xxviWhy You Should Read This Book xxviiWhat This Book Does and Does Not Focus On xxixAcknowledgments xxxiMatt Ganis xxxiAvinash Kohirkar xxxiJoint Acknowledgments xxxiiAbout the Authors xxxivPart I: Data IdentificationChapter 1: Looking for Data in All the Right Places 1What Data Do We Mean? 2What Subset of Content Are We Interested In? 4Whose Comments Are We Interested In? 6What Window of Time Are We Interested In? 7Attributes of Data That Need to Be Considered 7Structure 8Language 9Region 9Type of Content 10Venue 13Time 14Ownership of Data 14Summary 15Chapter 2: Separating the Wheat from the Chaff 17It All Starts with Data 18Casting a Net 19Regular Expressions 23A Few Words of Caution 27It's Not What You Say but WHERE You Say It 28Summary 29Chapter 3: Whose Comments Are We Interested In? 31Looking for the Right Subset of People 32Employment 32Sentiment 32Location or Geography 33Language 33Age 34Gender 34Profession/Expertise 34Eminence or Popularity 35Role 35Specific People or Groups 35Do We Really Want ALL the Comments? 35Are They Happy or Unhappy? 37Location and Language 39Age and Gender 41Eminence, Prestige, or Popularity 42Summary 45Chapter 4: Timing Is Everything 47Predictive Versus Descriptive 48Predictive Analytics 49Descriptive Analytics 53Sentiment 55Time as Your Friend 57Summary 58Chapter 5: Social Data: Where and Why 61Structured Data Versus Unstructured Data 63Big Data 65Social Media as Big Data 67Where to Look for Big Data 69Paradox of Choice: Sifting Through Big Data 70Identifying Data in Social Media Outlets 74Professional Networking Sites 75Social Sites 77Information Sharing Sites 78Microblogging Sites 79Blogs/Wikis 80Summary 81Part II: Data AnalysisChapter 6: The Right Tool for the Right Job 83The Four Dimensions of Analysis Taxonomy 84Depth of Analysis 85Machine Capacity 86Domain of Analysis 88External Social Media 88Internal Social Media 93Velocity of Data 99Data in Motion 99Data at Rest 100Summary 101Chapter 7: Reading Tea Leaves: Discovering Themes, Topics, or Trends 103Validating the Hypothesis 104Youth Unemployment 104Cannes Lions 2013 11056th Grammy Awards 112Discovering Themes and Topics 113Business Value of Projects 114Analysis of the Information in the Business ValueField 115Our Findings 115Using Iterative Methods 117Summary 119Chapter 8: Fishing in a Fast-Flowing River 121Is There Value in Real Time? 122Real Time Versus Near Real Time 123Forewarned Is Forearmed 125Stream Computing 126IBM InfoSphere Streams 128SPL Applications 129Directed Graphs 130Streams Example: SSM 131Step 1 133Step 2 134Step 3 134Step 4 135Steps 5 and 6 136Steps 7 and 8 136Value Derived from a Conference Using Real-TimeAnalytics 138Summary 139Chapter 9: If You Don't Know What You Want, You Just May Find It!: Ad Hoc Exploration 141Ad Hoc Analysis 142An Example of Ad Hoc Analysis 144Data Integrity 150Summary 155Chapter 10: Rivers Run Deep: Deep Analysis 157Responding to Leads Identified in Social Media 157Identifying Leads 158Qualifying/Classifying Leads 160Suggested Action 161Support for Deep Analysis in Analytics Software 163Topic Evolution 163Affinity Analysis in Reporting 165Summary 167Chapter 11: The Enterprise Social Network 169Social Is Much More Than Just Collaboration 170Transparency of Communication 171Frictionless Redistribution of Knowledge 172Deconstructing Knowledge Creation 172Serendipitous Discovery and Innovation 172Enterprise Social Network Is the Memory of the Organization 172Understanding the Enterprise Graph 174Personal Social Dashboard: Details of Implementation 175Key Performance Indicators (KPIs) 177Assessing Business Benefits from Social Graph Data 183What's Next for the Enterprise Graph? 185Summary 186Part III: Information InterpretationChapter 12: Murphy Was Right! The Art of What Could Go Wrong 189Recap: The Social Analytics Process 190Finding the Right Data 193Communicating Clearly 195Choosing Filter Words Carefully 198Understanding That Sometimes Less Is More 198Customizing and Modifying Tools 201Using the Right Tool for the Right Job 204Analyzing Consumer Reaction During Hurricane Sandy 204Summary 209Chapter 13: Visualization as an Aid to Analytics 211Common Visualizations 212Pie Charts 213Bar Charts 214Line Charts 216Scatter Plots 218Common Pitfalls 219Information Overload 219The Unintended Consequences of Using 3D 220Using Too Much Color 221Visually Representing Unstructured Data 222Summary 225AppendicesAppendix A: Case Study 227Introduction to the Case Study: IBMAmplify 228Data Identification 228Taking a First Pass at the Analysis 234Data Analysis 241A Second Attempt at Analyzing the Data 243Information Interpretation 244Conclusions 247Index 249
