A note on the digital index
A link in an index entry is displayed as the section title in which that entry appears. Because some sections have multiple index markers, it is not unusual for an entry to have several links to the same section. Clicking on any link will take you directly to the place in the text in which the marker appears.
A
- A/B tests, Testing for Correlation
- Adams, Douglas, Formulate the Question
- advertising, The Promise, Social Media Is Cheap, Branding, Where is Linda?–Return on Investment: Was Linda Worth It?, How to Find Purchase Intent, PR Often Has No Measurable ROI, Context in PR, Impersonation
- Coca-Cola and, Branding
- Google and, How to Find Purchase Intent
- impersonation PR disasters and, Impersonation
- measuring, PR Often Has No Measurable ROI
- social media is cheap myth, Social Media Is Cheap
- Super Bowl and, Context in PR
- targeted, The Promise
- Virgin Atlantic Airways case, Where is Linda?–Return on Investment: Was Linda Worth It?
- Advertising Value Equivalent (AVE), PR Often Has No Measurable ROI
- Air Force (US), The US Military’s Search for Social Media Robots
- airline customer satisfaction, A Case of Airline Customer Satisfaction–A Case of Airline Customer Satisfaction
- Aldi (company), PR Often Has No Measurable ROI
- Alloca, Kevin, Speed to disaster
- Amatriain, Xavier, How to Build a Recommendation System: Start Small, Data Selection, Sampling
- Amazon.com, Analytics Focus, Social Media Is Just Another Channel, Recommendation Systems, Collaborative Recommendations, How to Build a Recommendation System: Start Small
- big data and, Analytics Focus
- product reviews, Social Media Is Just Another Channel
- recommendation systems, Recommendation Systems, Collaborative Recommendations, How to Build a Recommendation System: Start Small
- Amleshwaram, Amit A., How to Spot Bots
- analytical CRM, Analytical CRM: The New Frontier, Issues with the Traditional Way–Issues with the Traditional Way, Turning CRM Around, Facebook and Open Graph–Facebook and Open Graph
- about, Analytical CRM: The New Frontier
- Facebook and Open Graph, Facebook and Open Graph–Facebook and Open Graph
- issues with traditional way, Issues with the Traditional Way–Issues with the Traditional Way
- turning CRM around, Turning CRM Around
- anaphora algorithm, Anaphora Algorithm
- Anderson, Chris, The Promise
- Anderson, Tom, Use the Right Data
- Anscombe, F. J., Creative Discovery
- antitrust case (Microsoft), Microsoft’s Antitrust Case
- AP News, Measuring People–Measuring People
- Apache Hadoop, Better Technology
- Apple Computer, Is Social Customer Care the New Commodity?
- Arab Spring, Six principles of influence, Inappropriate selling
- Aris, Annet, Issues with the Traditional Way
- Arizona State University, Prediction of Learning
- Arthur, Charles, Conclusion
- ask-measure-learn systems, Analytics Focus, Analytics Focus, Analytics Focus, Predicting the Future, Predicting the Future, Predicting the Future, Ask the Right Question–Workbook Questions, Use the Right Data–Workbook, Define the Right Measurement–Workbook
- asking the right question, Analytics Focus, Predicting the Future, Ask the Right Question–Workbook Questions
- defining the right measure, Analytics Focus, Predicting the Future, Define the Right Measurement–Workbook
- using the right data, Analytics Focus, Predicting the Future, Use the Right Data–Workbook
- asking the right question (AML), Analytics Focus, Predicting the Future, Ask the Right Question–Ask the Right Question, Case Study: Major Telecom Company–Was He Heard?, Formulate the Question–The Right Question, An Industry in Search of a Question, Summary–Workbook Questions, Workbook Questions
- about, Ask the Right Question–Ask the Right Question
- analytic focus, Analytics Focus
- chapter summary, Summary–Workbook Questions
- formulating the question, Formulate the Question–The Right Question
- measurement companies and, An Industry in Search of a Question
- predicting the future, Predicting the Future
- telecom case study, Case Study: Major Telecom Company–Was He Heard?
- workbook questions, Workbook Questions
- Assange, Julian, Conclusion
- astroturfing, Spreading Paid Opinions: Grassroots and Astroturfing–Cause, Access, and Reach, How to Spot Attempts to Create Contagiousness
- Asur, Sitaram, Insights with Caution
- author, context of, Context of the author
- authority (influence principle), Six principles of influence, Influence
- automation and customer care, Automation and Business Intelligence–Case: Dell’s customer care
- AVE (Advertising Value Equivalent), PR Often Has No Measurable ROI
- awareness (brand), Marketing and Social Media: The Promise and the Reality, Reach Does Not Equal Awareness, Case: Virgin Atlantic Airways–Return on Investment: Was Linda Worth It?, Reach Versus Intention–Reach Versus Intention, Knowledge Bases and Customer Self-Service
- about, Marketing and Social Media: The Promise and the Reality
- customer self-service and, Knowledge Bases and Customer Self-Service
- reach and, Reach Does Not Equal Awareness, Reach Versus Intention–Reach Versus Intention
- Virgin Atlantic Airways case, Case: Virgin Atlantic Airways–Return on Investment: Was Linda Worth It?
B
- Baird, Carolyn Heller, Return on Investment: Was Linda Worth It?
- Bakshy, Eytan, The Influencer, Journalism CRM, Viral by Design
- Bandurski, David, China’s 50-Cent Bloggers
- banking industry, Is Social Customer Care the New Commodity?
- Beane, Billy, Cost and Insider Knowledge
- behavioral targeting, Behavioral Targeting–Behavioral Targeting, Behavioral Targeting, Behavioral Targeting, Behavioral Targeting, Social Connections versus Behavior–Social Connections versus Behavior
- about, Behavioral Targeting–Behavioral Targeting
- creepiness factor, Behavioral Targeting
- limits of, Behavioral Targeting
- machine learning and, Behavioral Targeting
- social connections and, Social Connections versus Behavior–Social Connections versus Behavior
- Berners-Lee, Tim, Facebook and Open Graph
- Best Buy retail chain, Resources and Scaling
- Bhatt, Rushi, Social Connections versus Behavior
- bias, Selection Bias, Bad PR Bias
- bad PR, Bad PR Bias
- selection, Selection Bias
- Bieber, Justin, Ask the Right Question
- big data, Introduction, The Fourth V of Data–The Fourth V of Data, The Promise, The Promise–The Promise, The Promise, The Data Focus–Better Technology, More Data, Better Technology, Analytics Focus–Analytics Focus, Conclusion
- about, Introduction, More Data
- analytics focus of, Analytics Focus–Analytics Focus
- data focus of, The Data Focus–Better Technology
- predictive policing and, The Promise
- privacy and, Conclusion
- promise of, The Promise–The Promise
- technology and, Better Technology
- three Vs of, The Fourth V of Data–The Fourth V of Data
- usage examples, The Promise
- bin Laden, Osama, Introduction
- Blakley, Johanna, Issues with the Traditional Way
- Blodget, Henry, Your Friends Like It...
- Blog Post Automator, The Myth of Number of Articles
- Bohr, Nils, Predicting the Future
- Boshmaf, Yazan, Creating Reach, How to Spot Bots
- bots (robots), Spam and Robots–Spam and Robots, Creating Reach–Creating Reach, Creating Reach, How to Spot Bots–How to Spot Bots, How to Spot Bots, Smearing Opponents–Follower Scandals, Creating Influence and Intention–The US Military’s Search for Social Media Robots, Spreading Paid Opinions: Grassroots and Astroturfing–Cause, Access, and Reach, Blurry Lines
- about, Spam and Robots–Spam and Robots
- blurry lines, Blurry Lines
- creating influence and intention, Creating Influence and Intention–The US Military’s Search for Social Media Robots
- creating reach, Creating Reach–Creating Reach
- prevalence of, Creating Reach
- setting up, How to Spot Bots
- smearing opponents, Smearing Opponents–Follower Scandals
- spotting, How to Spot Bots–How to Spot Bots
- spreading paid opinions, Spreading Paid Opinions: Grassroots and Astroturfing–Cause, Access, and Reach
- box offices, predicting, Predicting Box Offices–Conclusion
- branding, Marketing and Social Media: The Promise and the Reality, Social Media Is Just Another Channel, Branding–Return on Investment: Was Linda Worth It?, Branding, Reach Does Not Equal Awareness–Return on Investment: Was Linda Worth It?, Return on Investment: Was Linda Worth It?, Knowledge Bases and Customer Self-Service
- brand awareness, Marketing and Social Media: The Promise and the Reality, Reach Does Not Equal Awareness–Return on Investment: Was Linda Worth It?, Knowledge Bases and Customer Self-Service
- Coca-Cola and, Branding
- owned media and, Social Media Is Just Another Channel
- ROI and, Return on Investment: Was Linda Worth It?
- social media and, Branding–Return on Investment: Was Linda Worth It?
- Breen, Jeffrey, A Case of Airline Customer Satisfaction–Sentiment Algorithm
- Brenner, Jeffrey, The Promise
- British Petroleum case, Causation–All of the above
- Brooks, David, The Promise
- Brown, Danny, Social Media Is Cheap
- Brown, Gordon, Copying
- Brynley-Jones, Luke, Resources and Scaling
- Buck, Michael, PR Often Has No Measurable ROI
- Bulova advertisement, Social Media Is Cheap
- Burson Marstella (PR firm), Self-censorship, The Case of Facebook
- business intelligence and customer care, Automation and Business Intelligence–Case: Dell’s customer care
C
- C-SAT score, Case Study: Customer Lifecycle
- CAPTCHA codes, How to Spot Bots, How to Spot Bots
- Carnegie, Dale, Six principles of influence
- Carroll, Dave, United Breaks Guitars, Ask the Right Question
- causal inference, Testing for Correlation
- causal relationships, Return on Investment: Was Linda Worth It?
- causation, Causation–All of the above, Case: A Matchmaking Engine, Influence
- data selection process and, Causation–All of the above, Case: A Matchmaking Engine
- influence and, Influence
- centrality (metric), The Influencer, Reach in PR, Influence
- about, The Influencer
- influence and, Influence
- public relations and, Reach in PR
- change metrics, Overcoming the issues
- chatbots, A Turing Test on Twitter
- Checa, Nicolas, Issues with the Traditional Way
- Chinese 50-cent bloggers, China’s 50-Cent Bloggers
- Cialdini, Robert, Reason, Six principles of influence
- Circle Me (company), You Like It...
- Clausen, Lasse, The Cold-Start Problem
- click-through rate (CTR), Reach Does Not Equal Awareness, Social Connections versus Behavior
- Clinton, Bill, Case: Haiti
- clipping articles, Clipping–The Myth of Number of Articles
- closed metrics, Overcoming the issues
- CLV (customer lifetime value), Return on Investment: Was Linda Worth It?
- co-creation concept, Analytical CRM: The New Frontier
- Coca-Cola, Social Media Is Just Another Channel–Branding
- Cocozza, Frankie, Context of the content
- Colao, Vittorio, Was He Heard?
- cold-start problem (recommendation systems), The Cold-Start Problem
- collaborative recommendations, Collaborative Recommendations, The Cold-Start Problem
- Comcast service provider, Customer Care 2.0
- comments, User comments (see ratings and reviews)
- commitment (influence principle), Six principles of influence
- communication, public relations and, Communication Is Human–Six principles of influence
- Compass.co, Reach Versus Intention
- Confirmit survey, Social Media: Too Shallow?
- Consortium for Service Innovation, Knowledge Bases and Customer Self-Service
- consumer preferences, Consumer Preference–Consumer Preference
- contagiousness, Contagiousness, Kony2012, Viral by Design–Viral by Design, The Truth about the Truth, How to Spot Attempts to Create Contagiousness
- about, Contagiousness
- false information and, The Truth about the Truth
- Kony2012 video, Kony2012
- spotting attempts to create, How to Spot Attempts to Create Contagiousness
- viral by design, Viral by Design–Viral by Design
- content, Context of the content, Smart Selection, Spreading Paid Opinions: Grassroots and Astroturfing, Kony2012
- context of, Context of the content
- grassroot movement success factor, Spreading Paid Opinions: Grassroots and Astroturfing
- Kony2012 video, Kony2012
- measuring for customer care, Smart Selection
- content-based recommendations, Content-Based Recommendations
- context, Measuring People, Context in PR–Context of the author, Context of the content, Context of the author, Context
- of author, Context of the author
- of content, Context of the content
- public relations and, Measuring People, Context in PR–Context of the author
- sentiment algorithm and, Context
- controlled responding, Reason
- cookies, behavioral targeting with, Behavioral Targeting
- correlation, Correlation versus causation, Direct effect, Testing for Correlation–Testing for Correlation
- causation versus, Correlation versus causation
- direct, Direct effect
- testing for, Testing for Correlation–Testing for Correlation
- costs, Cost and Insider Knowledge, Case: A Matchmaking Engine, Influence
- for data retrieval, Cost and Insider Knowledge, Case: A Matchmaking Engine
- influence and, Influence
- Coyne, Chris, Case: A Matchmaking Engine
- Crayfourd, Peter, Case Study: Customer Lifecycle–Case Study: Customer Lifecycle, Turning CRM Around
- creating the right measure, Summary (see defining the right measure)
- creative discovery, Creative Discovery–Creative Discovery
- creepiness factor, Behavioral Targeting, Case: A Matchmaking Engine
- CRM (customer relationship management), Journalism CRM, Customer Care 2.0, Positive Publicity, Dos and Don’ts–Resources and Scaling, Dos and Don’ts–Is Social Customer Care the New Commodity?, Social CRM: Market Research–Workbook, Analytical CRM: The New Frontier–Facebook and Open Graph
- analytical, Analytical CRM: The New Frontier–Facebook and Open Graph
- dos and don’ts, Dos and Don’ts–Resources and Scaling
- journalism, Journalism CRM
- social, Positive Publicity, Dos and Don’ts–Is Social Customer Care the New Commodity?, Social CRM: Market Research–Workbook
- social media channels and, Customer Care 2.0
- Croll, Alistair, Ask the Right Question
- crowdsourcing, Predicting the Future, Prediction of Learning, Predicting Elections
- confusion around, Predicting the Future
- predicting elections, Predicting Elections
- predicting learning, Prediction of Learning
- CTR (click-through rate), Reach Does Not Equal Awareness, Social Connections versus Behavior
- customer care, Customer Care, New Voice of the Customer–United Breaks Guitars, Dell Hell, United Breaks Guitars, United Breaks Guitars, Customer Care 2.0–Positive Publicity, Knowledge Bases and Customer Self-Service–Happier Employees, Knowledge Bases and Customer Self-Service–Happier Employees, Knowledge Bases and Customer Self-Service, Happier Employees, Smart Selection, Smart Selection, Smart Selection, Positive Publicity, Positive Publicity, Dos and Don’ts–Resources and Scaling, Dos and Don’ts–Is Social Customer Care the New Commodity?, Get Clients into Your Service Channel, Mind the Trolls, Resources and Scaling, Automation and Business Intelligence–Case: Dell’s customer care, Automation and Business Intelligence–Case: Dell’s customer care, Case Sony Ericsson—special words–A Dynamic Approach to Machine Learning, A Dynamic Approach to Machine Learning–Case: Dell’s customer care, Case: Dell’s customer care, Summary, Workbook
- about, Customer Care
- automation and, Automation and Business Intelligence–Case: Dell’s customer care
- business intelligence and, Automation and Business Intelligence–Case: Dell’s customer care
- chapter summary, Summary
- cost cutting and, New Voice of the Customer–United Breaks Guitars
- customer self-service and, Knowledge Bases and Customer Self-Service–Happier Employees
- Dell Computer cases, Dell Hell, Case: Dell’s customer care
- Delta Airlines case, United Breaks Guitars
- detecting dissatisfaction, Smart Selection
- dos and don’ts, Dos and Don’ts–Resources and Scaling
- engagement metric and, Smart Selection
- happier employees and, Happier Employees
- knowledge bases and, Knowledge Bases and Customer Self-Service–Happier Employees
- machine learning and, A Dynamic Approach to Machine Learning–Case: Dell’s customer care
- measuring content for, Smart Selection
- positive publicity, Positive Publicity
- responding to clients publicly, Get Clients into Your Service Channel
- ROI on, Knowledge Bases and Customer Self-Service
- social CRM and, Positive Publicity, Dos and Don’ts–Is Social Customer Care the New Commodity?
- social media channels and, Customer Care 2.0–Positive Publicity
- Sony Ericsson case, Case Sony Ericsson—special words–A Dynamic Approach to Machine Learning
- staffing considerations, Resources and Scaling
- trolls and, Mind the Trolls
- United Airlines case, United Breaks Guitars
- workbook questions, Workbook
- customer lifetime value (CLV), Return on Investment: Was Linda Worth It?
- customer relationship management, Positive Publicity (see CRM)
D
- data analytics, Analytics Focus–Analytics Focus, Marketing, Which Data?–Personal Data: Too Sensitive?, Social Media: Too Shallow?, Personal Data: Too Sensitive?–Personal Data: Too Sensitive?, Gaming the System–Workbook
- challenges of, Analytics Focus–Analytics Focus
- data considerations, Which Data?–Personal Data: Too Sensitive?
- gaming the system, Gaming the System–Workbook
- marketing and, Marketing
- sensitive data and, Personal Data: Too Sensitive?–Personal Data: Too Sensitive?
- social-sourced data, Social Media: Too Shallow?
- data sparsity, Not Enough Data
- data-driven sales, Data-Driven Sales
- data-reduction process, A Case of Airline Customer Satisfaction
- De Vany, Arthur S., Branding, Conclusion
- defining the right measure (AML), Analytics Focus, Predicting the Future, Define the Right Measurement–Define the Right Measurement, Examples of Social Media Metrics–The Quest for ROI, The Risks of Metrics–Overcoming the issues, Summary, Workbook
- about, Define the Right Measurement–Define the Right Measurement
- analytic focus, Analytics Focus
- chapter summary, Summary
- examples of social media metrics, Examples of Social Media Metrics–The Quest for ROI
- predicting the future, Predicting the Future
- risks of metrics, The Risks of Metrics–Overcoming the issues
- workbook questions, Workbook
- Dell Computer cases, Dell Hell, Case: Dell’s customer care, Predicting the Future
- Delta Airlines case, United Breaks Guitars
- Deming, W. Edwards, Closing Predictions
- Derwent Capital hedge fund, Predicting the Stock Market
- Deutsche Bahn, Mind the Trolls
- Dig Your Well Before You’re Thirsty (Mackay), Influence
- digital ethnography, Analytical CRM: The New Frontier
- Digital Journalism Study, Spam and Robots
- digital Maoism, Collaborative Recommendations
- Dior, Aja, Measuring People
- distributing information (public relations), Public Relations–Public Relations, Measuring People–Six principles of influence, Measuring Distributing–Case: Spread of the Idea of “Resilient India”, Clipping–Clipping, The Myth of Number of Articles–The Myth of Number of Articles, The Myth of Number of Articles–Case: Spread of the Idea of “Resilient India”, Reading Lists, Case: Spread of the Idea of “Resilient India”–Case: Spread of the Idea of “Resilient India”
- about, Public Relations–Public Relations
- aggregating articles, The Myth of Number of Articles–The Myth of Number of Articles
- by clipping articles, Clipping–Clipping
- engagement metric, The Myth of Number of Articles–Case: Spread of the Idea of “Resilient India”
- measuring, Measuring Distributing–Case: Spread of the Idea of “Resilient India”
- measuring people, Measuring People–Six principles of influence
- reading lists, Reading Lists
- resilient India case, Case: Spread of the Idea of “Resilient India”–Case: Spread of the Idea of “Resilient India”
- domain knowledge, Domain Knowledge
- Doran, George T., The Right Question
- double-blind trials, Testing for Correlation
- D’Antonio, Giuseppe, You Like It...
E
- earned media, Social Media Is Just Another Channel, Reach Does Not Equal Awareness
- eccomplished study, Do Social Confirmation and Peer Pressure Work?
- edge stores, Facebook and Open Graph
- elections predictions for, Predicting Elections–Predicting Voting Behavior
- Eliason, Frank, Customer Care 2.0
- ELIZA computer program, A Turing Test on Twitter
- employees, Happier Employees, Resources and Scaling
- customer care considerations, Resources and Scaling
- happier, Happier Employees
- engagement (metric), Reach Does Not Equal Awareness, Case: Virgin Atlantic Airways–Return on Investment: Was Linda Worth It?, How to Find Purchase Intent, The Myth of Number of Articles–Engagement, Engagement, Engagement, Engagement, Engagement, Engagement, Clicking, Sharing, Liking, Thumbs Up, Commenting, Copying, Smart Selection, Influence
- about, Reach Does Not Equal Awareness, The Myth of Number of Articles–Engagement
- clicking, Engagement, Clicking
- commenting, Engagement, Commenting
- copying, Engagement, Copying
- customer care and, Smart Selection
- influence and, Influence
- sharing, Engagement, Sharing, Liking, Thumbs Up
- social targeting and, How to Find Purchase Intent
- tracking, Engagement
- Virgin Atlantic Airways case, Case: Virgin Atlantic Airways–Return on Investment: Was Linda Worth It?
- entropy, defined, Behavioral Targeting
- Erlang C model, Resources and Scaling
- error rates, Error, or Why Structured Data Is Superior–Unstructured, Case: A Matchmaking Engine, Influence
- influence and, Influence
- structured data and, Error, or Why Structured Data Is Superior–Unstructured, Case: A Matchmaking Engine
- European Union, Copying
- expectation gap in social media, Return on Investment: Was Linda Worth It?
F
- F-commerce, Social Sales
- Facebook, Marketing and Social Media: The Promise and the Reality, Where is Linda?, Social Targeting, Social Sales, Public Relations, Speed to disaster, United Breaks Guitars, Customer Care 2.0, Positive Publicity, Get Clients into Your Service Channel, Mind the Trolls, Facebook and Open Graph–Facebook and Open Graph, Spam and Robots, How to Spot Bots, How to Spot Bots, How to Spot Bots, The Case of Facebook, Predicting Elections, Insights with Caution
- about, Marketing and Social Media: The Promise and the Reality
- bot detection and, How to Spot Bots, How to Spot Bots, How to Spot Bots
- checking rate of escalation, Speed to disaster
- criticism of, The Case of Facebook
- CRM and, Customer Care 2.0
- customer care and, Get Clients into Your Service Channel
- Delta Airlines case, United Breaks Guitars
- Deutsche Bahn and, Mind the Trolls
- KLM airline and, Positive Publicity
- Open Graph and, Facebook and Open Graph–Facebook and Open Graph
- predicting box offces, Insights with Caution
- predicting elections and, Predicting Elections
- social commerce and, Social Sales
- social spam and, Spam and Robots
- social targeting and, Social Targeting
- Virgin Atlantic Airways case, Where is Linda?
- Zuckerberg and, Public Relations
- filtering, No Surprises, No truth
- keywords and, No truth
- recommendation systems and, No Surprises
- financial metrics, PR Often Has No Measurable ROI, Unstructured, Wrong behavior
- Finger, Lutz, Context of the author
- Fisheye Analytics, Introduction, Context of the content, Reading Lists, Sharing, Liking, Thumbs Up, Speed to disaster, Speed to disaster, China’s 50-Cent Bloggers, Use the Right Data, Causation
- about, Introduction, Use the Right Data
- on birthday party flash mob, Speed to disaster
- on 50-cent bloggers, China’s 50-Cent Bloggers
- “mood of the nation” experiment, Context of the content
- Net-Sentiment-Score, Causation
- on share of voice within media lists, Reading Lists
- on sharing articles, Sharing, Liking, Thumbs Up, Speed to disaster
- flash mobs, Speed to disaster
- FOIA (Freedom of Information Act), Consumer Preference
- Ford Motor Company, Marketing and Social Media: The Promise and the Reality, Your Friends Like It...
- FOUNDD, The Cold-Start Problem
- Foursquare app, Case: A Matchmaking Engine
- Freedom of Information Act (FOIA), Consumer Preference
- Freeman, Linton C., Reach in PR
- future, predicting the, Predicting the Future–Predicting the Future
G
- Gambhir, Ashish, Case: newBrandAnalytics, Consumer Preference
- gaming the system, Gaming the System, Spam and Robots, Creating Reach–Creating Reach, How to Spot Bots–How to Spot Bots, Smearing Opponents–Follower Scandals, Follower Scandals, Creating Influence and Intention–The US Military’s Search for Social Media Robots, Spreading Paid Opinions: Grassroots and Astroturfing–Cause, Access, and Reach, Contagiousness–How to Spot Attempts to Create Contagiousness, The Opposite of Virality: Suppressing Messages, Blurry Lines, Summary–Workbook, Workbook
- about, Gaming the System
- analyzing motivation behind, Follower Scandals
- blurry lines, Blurry Lines
- chapter summary, Summary–Workbook
- contagiousness and, Contagiousness–How to Spot Attempts to Create Contagiousness
- creating influence and intention, Creating Influence and Intention–The US Military’s Search for Social Media Robots
- creating reach, Creating Reach–Creating Reach
- smearing opponents, Smearing Opponents–Follower Scandals
- spam and robots, Spam and Robots
- spotting bots, How to Spot Bots–How to Spot Bots
- spreading paid opinions, Spreading Paid Opinions: Grassroots and Astroturfing–Cause, Access, and Reach
- suppressing messages, The Opposite of Virality: Suppressing Messages
- workbook questions, Workbook
- Gawker social media channel, United Breaks Guitars
- General Electric, Predicting the Future
- Gilles, Martin, Personal Data: Too Sensitive?
- Gingrich, Newt, Follower Scandals
- Gladwell, Malcolm, Your Friends Like It..., Your Friends Like It...
- Goethe, Johann Wolfgang von, Your Friends Like It..., Case: A Matchmaking Engine
- Google, The Promise, Analytics Focus, How to Find Purchase Intent, Social Targeting, Collaborative Recommendations, The Technology of Recommendation Systems, The Myth of Number of Articles, Knowledge Bases and Customer Self-Service, Spam and Robots, SOPA and PIPA Act: A Modern Grassroots Movement, The Case of Facebook, Predicting Voting Behavior, I know keywords, Define the Right Measurement, The Risks of Metrics
- customer self-service and, Knowledge Bases and Customer Self-Service
- dominance of, Analytics Focus
- Facebook case and, The Case of Facebook
- grassroots movement, SOPA and PIPA Act: A Modern Grassroots Movement
- keyword setup, I know keywords
- online advertising and, How to Find Purchase Intent
- PageRank metric, Define the Right Measurement, The Risks of Metrics
- predicting voting behavior, Predicting Voting Behavior
- predictions and, The Promise, The Technology of Recommendation Systems
- recommendation systems, Collaborative Recommendations
- SEO algorithm and, Spam and Robots
- social targeting and, Social Targeting
- spam blogs and, The Myth of Number of Articles
- Google Prediction API, The Technology of Recommendation Systems, Predicting Elections
- Google Search API, Predicting the Stock Market
- Gorkana Group, Journalism CRM
- GPS devices, The Promise
- Granovetter, Mark, No Early Warning Systems
- grassroots movements, Spreading Paid Opinions: Grassroots and Astroturfing–Cause, Access, and Reach
- Greenpeace environmental group, Impersonation
- Griffiths, José-Marie, Acknowledgments
- Gruning, James E., Public Relations, PR to Warn
- Gutjahr, Richard, Copying
H
- Hadoop (Apache), Better Technology
- Haenlein, Michael, Social Media Is Just Another Channel
- Haiti case, Case: Haiti
- HealthGrades.com website, User Ratings
- Herrmann, Bjoern Lasse, Reach Versus Intention
- Heywood, Jamie, Social Media: Too Shallow?
- The Hitchhiker’s Guide to the Galaxy (Adams), Formulate the Question
- Holland, Paul W., Testing for Correlation
- Hollywood Economics (De Vany), Branding
- homophily, Your Friends Like It..., Homophily versus Influence, Homophily versus Influence, Measuring People, Influence
- about, Your Friends Like It..., Homophily versus Influence
- influence versus, Homophily versus Influence, Measuring People, Influence
- Hotels.com website, User comments
- Houston, Whitney, Measuring People
- How to Win Friends and Influence People (Carnegie), Six principles of influence
- Huberman, Bernardo A., Insights with Caution
- Hussey, Michael, How to Spot Bots
I
- IBM, The Promise
- identity theft, Six principles of influence
- ifbyphone surveys, PR Often Has No Measurable ROI
- ifttt.com service, How to Spot Bots
- impersonation (PR disasters), Impersonation
- inappropriate selling (PR disaster), Inappropriate selling
- Incapsula (company), Creating Reach
- influence, Homophily versus Influence, Social Connections versus Behavior, The Influencer, What—or Who—Would Make You Buy?, Measuring People, Six principles of influence–Six principles of influence, Creating Influence and Intention–The US Military’s Search for Social Media Robots, Influence–Influence, Influence, Influencing the metric
- gaming the system, Creating Influence and Intention–The US Military’s Search for Social Media Robots
- homophily versus, Homophily versus Influence, Measuring People, Influence
- local scope of, The Influencer
- measurements and, Influencing the metric
- principles of, Six principles of influence–Six principles of influence
- of ratings and reviews, Social Connections versus Behavior, What—or Who—Would Make You Buy?
- social media metric examples, Influence–Influence
- influencers, Marketing and Social Media: The Promise and the Reality, Marketing and Social Media: The Promise and the Reality, Marketing and Social Media: The Promise and the Reality, Your Friends Like It...–Your Friends Like It..., The Influencer–The Influencer, Public Relations
- about, Marketing and Social Media: The Promise and the Reality
- brand message and, Marketing and Social Media: The Promise and the Reality
- Ford Motor Company and, Marketing and Social Media: The Promise and the Reality
- public relations and, Public Relations
- purchase intent and, The Influencer–The Influencer
- social targeting and, Your Friends Like It...–Your Friends Like It...
- information intake, behavioral targeting and, How to Find Purchase Intent
- insider knowledge, data retrieval and, Cost and Insider Knowledge
- intent to purchase, Marketing and Social Media: The Promise and the Reality (see purchase intent)
- IOC (International Olympic Committee), Smart Selection
- IT Infrastructure Library (ITIL), Knowledge Bases and Customer Self-Service
- ITIL (IT Infrastructure Library), Knowledge Bases and Customer Self-Service
J
- Jacobson, Matt, Insights with Caution
- Jarvis, Jeff, Dell Hell
K
- Kaplan, Andreas M., Social Media Is Just Another Channel
- Katz, Elihu, Your Friends Like It...
- Kauskik, Avinash, Behavioral Targeting
- KCS (Knowledge-Centered Support), Knowledge Bases and Customer Self-Service–Happier Employees
- Kenneth Cole (manufacturer), Inappropriate selling
- Kentucky Fried Chicken (KFC), Inappropriate selling
- keywords, Subsets–No truth, No truth
- KFC (Kentucky Fried Chicken), Inappropriate selling
- Kimmel, Jimmy, Nondeterministic
- KLM airline, Positive Publicity
- Klout (company), Influencing the metric
- knowledge as sales driver, Introduction, Data-Driven Sales, Reach Versus Intention
- about, Introduction
- creating purchase intent, Reach Versus Intention
- data-driven sales and, Data-Driven Sales
- Knowledge-Centered Support (KCS), Knowledge Bases and Customer Self-Service–Happier Employees
- Knox, Steve, Six principles of influence
- Kony, Joseph, Kony2012
- Kony2012 video, Kony2012
- Krux Consumer Survey, Behavioral Targeting
L
- Lakeview Gusher spill, Testing for Correlation
- Lanier, Jaron, The Promise, Collaborative Recommendations
- Larson, Erik, More Data
- Lazarsfeld, Paul Felix, Your Friends Like It...
- learning, prediction of, Prediction of Learning–Predicting Elections
- Leck, Nigel, Creating Influence and Intention–The US Military’s Search for Social Media Robots
- Leetaru, Kalev, Introduction
- Leskovec, Jure, Do Social Confirmation and Peer Pressure Work?
- Lewis, Andrew, Behavioral Targeting
- Lewis, Kevin, Marketing, Homophily versus Influence
- Lewis, Michael, Predicting the Future, Cost and Insider Knowledge
- Library of Congress, More Data
- Liebmann, Michael, Context, Predicting the Stock Market
- liking (influence principle), Six principles of influence
- LinkedIn, Not Enough Data
- Liu, Kun, Social Connections versus Behavior
- LobbyPlag service, Copying
- Lohr, Steve, More Data
- Lotan, Gilad, Measuring People, Kony2012
M
- machine learning, Behavioral Targeting, The Technology of Recommendation Systems–The Technology of Recommendation Systems, A Dynamic Approach to Machine Learning–Case: Dell’s customer care, A Dynamic Approach to Machine Learning
- behavioral targeting and, Behavioral Targeting
- dynamic approach to, A Dynamic Approach to Machine Learning–Case: Dell’s customer care
- supervised learning and, The Technology of Recommendation Systems–The Technology of Recommendation Systems, A Dynamic Approach to Machine Learning
- Mackay, Harvey, Influence
- market research, Social CRM: Market Research, Case Study: Customer Lifecycle–Case Study: Customer Lifecycle, Analytical CRM: The New Frontier, Which Data?–Personal Data: Too Sensitive?, Summary, Workbook
- about, Social CRM: Market Research
- analytical CRM and, Analytical CRM: The New Frontier
- chapter summary, Summary
- customer lifecycle case study, Case Study: Customer Lifecycle–Case Study: Customer Lifecycle
- linking data, Which Data?–Personal Data: Too Sensitive?
- workbook questions, Workbook
- market research online communities (MROCs), Analytical CRM: The New Frontier
- marketing, Marketing, Marketing and Social Media: The Promise and the Reality–Marketing and Social Media: The Promise and the Reality, Marketing and Social Media: The Promise and the Reality, Marketing and Social Media: The Promise and the Reality, Three Myths about Social Media–Social Media Is Just Another Channel, Branding–Return on Investment: Was Linda Worth It?, Purchase Intent–The Influencer, Summary–Workbook, Workbook, Predicting the Future
- about, Marketing
- branding and, Marketing and Social Media: The Promise and the Reality, Branding–Return on Investment: Was Linda Worth It?
- chapter summary, Summary–Workbook
- crowdsourcing and, Predicting the Future
- purchase intent and, Marketing and Social Media: The Promise and the Reality, Purchase Intent–The Influencer
- social media and, Marketing and Social Media: The Promise and the Reality–Marketing and Social Media: The Promise and the Reality
- social media myths, Three Myths about Social Media–Social Media Is Just Another Channel
- workbook questions, Workbook
- Marsteller, Burson, Your Friends Like It...
- massive open online courses (MOOCs), Prediction of Learning
- master data management (MDM), Social CRM: Market Research
- matchmaking engine case, Case: A Matchmaking Engine–Case: A Matchmaking Engine
- Mathuros, Fon, Case: Spread of the Idea of “Resilient India”
- McDonald’s case, Case: McDonald’s
- McKinsey & Company, Marketing and Social Media: The Promise and the Reality, Predicting the Future
- McServed website, Case: McDonald’s
- MDM (master data management), Social CRM: Market Research
- measurement paradoxon, The Risks of Metrics
- measurements (metrics), Analytics Focus, Social Media Is Just Another Channel–Social Media Is Just Another Channel, Social Media: A New Class of Metrics, PR Often Has No Measurable ROI–PR Often Has No Measurable ROI, Measuring People–Six principles of influence, Measuring Distributing–Case: Spread of the Idea of “Resilient India”, Smart Selection, Gaming the System–Workbook, How to Spot Bots–How to Spot Bots, Predicting the Future, Ask the Right Question, An Industry in Search of a Question, Cost and Insider Knowledge, Define the Right Measurement–Workbook, Examples of Social Media Metrics–The Quest for ROI, The Risks of Metrics–Overcoming the issues, Influencing the metric, Wrong behavior, Changes Over Time and Space–Changes Over Time and Space, Overcoming the issues
- asking the right question and, An Industry in Search of a Question
- changes over time and space, Changes Over Time and Space–Changes Over Time and Space
- complaint importance, Smart Selection
- defining the right measurement, Analytics Focus, Predicting the Future, Define the Right Measurement–Workbook
- differing platforms for, Social Media: A New Class of Metrics
- gaming the system, Gaming the System–Workbook
- influencing, Influencing the metric
- measuring distributing information, Measuring Distributing–Case: Spread of the Idea of “Resilient India”
- measuring people, Measuring People–Six principles of influence
- measuring ROI, PR Often Has No Measurable ROI–PR Often Has No Measurable ROI
- overcoming issues, Overcoming the issues
- risks of, The Risks of Metrics–Overcoming the issues
- sabermetrics, Cost and Insider Knowledge
- social media examples, Examples of Social Media Metrics–The Quest for ROI
- social media myths and, Social Media Is Just Another Channel–Social Media Is Just Another Channel
- spotting bots, How to Spot Bots–How to Spot Bots
- wrong behavior, Wrong behavior
- YouTube views, Ask the Right Question
- Meltwater, NLA v., Clipping
- memes, How to Spot Attempts to Create Contagiousness
- Menczer, Filippo, How to Spot Attempts to Create Contagiousness
- Merton, Robert, Your Friends Like It...
- metadata, Facebook and Open Graph
- Microsoft antitrust case, Microsoft’s Antitrust Case
- Milgram, Stanley, How to Spot Bots
- Milgrim experiments, Six principles of influence
- Miller, Paul, Conclusion
- Ming Fai Wong, Felix, Insights with Caution
- MIT Technology Review, Creating Influence and Intention
- Monck, Adrian, Ask the Right Question
- Moneyball (Lewis), Predicting the Future, Cost and Insider Knowledge
- MOOCs (massive open online courses), Prediction of Learning
- “mood of the nation”, Context of the content
- Moog (company), PR Often Has No Measurable ROI
- Moran, Robert, Analytical CRM: The New Frontier
- Morano, Nadine, Follower Scandals
- motivation behind fraudulent behavior, Follower Scandals
- movie industry, Predicting Box Offices–Conclusion
- MROCs (market research online communities), Analytical CRM: The New Frontier
N
- National Center for Academic Transformation, The Promise
- Net Promoter Score (NPS), Case Study: Customer Lifecycle, Consumer Preference
- Net-Sentiment-Score, Causation, The unknown third
- Netflix, Recommendation Systems, The Cold-Start Problem, How to Build a Recommendation System: Start Small–How to Build a Recommendation System: Start Small, Personal Relationships, Turning CRM Around
- analytical CRM and, Turning CRM Around
- personal relationships and, Personal Relationships
- recommendation systems, Recommendation Systems, The Cold-Start Problem, How to Build a Recommendation System: Start Small–How to Build a Recommendation System: Start Small
- newBrandAnalytics (company), Case: newBrandAnalytics, Consumer Preference
- Ng, Andrew, The Technology of Recommendation Systems, Prediction of Learning
- Niessing, Jörg, Case Study: Customer Lifecycle, Analytical CRM: The New Frontier
- NLA v. Meltwater, Clipping
- Nothhaft, Hank, Facebook and Open Graph
- NPS (Net Promoter Score), Case Study: Customer Lifecycle, Consumer Preference
O
- Obama, Barack, Impersonation, John Sununu
- Ognibeni, Björn, Happier Employees
- online tracking, Behavioral Targeting
- Open Graph, Facebook and, Facebook and Open Graph–Facebook and Open Graph
- opponents, smearing, Smearing Opponents–Follower Scandals, The Case of Facebook
- Osbourne, Ozzy, Issues with the Traditional Way
- owned media, Social Media Is Just Another Channel
- O’Reilly, Tim, Social Media: A New Class of Metrics
P
- PageRank metric, Define the Right Measurement, The Risks of Metrics
- paid media, Social Media Is Just Another Channel (see advertising)
- paid opinions, spreading, Spreading Paid Opinions: Grassroots and Astroturfing–Cause, Access, and Reach
- Paine, Katie Delahaye, Sentiment Algorithm
- Parasnis, Gautam, Return on Investment: Was Linda Worth It?
- Pariser, Eli, No Surprises
- PatienceLikeMe patient community, Social Media: Too Shallow?
- Patino, Anthony, Social Media: Too Shallow?
- Paul, Ron, Predicting Elections
- Payless shoe retailer, Content-Based Recommendations
- Pearl, Judea, Correlation versus causation
- PeekYou (company), How to Spot Bots, Follower Scandals
- peer pressure, Peer Pressure–Do Social Confirmation and Peer Pressure Work?
- peerindex (company), Context of the author
- personal relationships and sales, Personal Relationships
- Petraeus, David, The US Military’s Search for Social Media Robots
- Phelps, Joseph E., Context in PR
- Piatetsky-Shapiro, Gregory, What This Book Covers
- PIPA (Protect Intellectual Property Act), SOPA and PIPA Act: A Modern Grassroots Movement
- politics, Smearing Opponents–Follower Scandals, Predicting Elections–Predicting Voting Behavior
- predicting elections, Predicting Elections–Predicting Voting Behavior
- smearing opponents, Smearing Opponents–Follower Scandals
- positive reinforcement, The Technology of Recommendation Systems
- predictions, The Promise, The Technology of Recommendation Systems, Predicting the Future–Predicting the Future, Prediction of Learning–Predicting Elections, Predicting Elections–Predicting Voting Behavior, Predicting Voting Behavior–Predicting Box Offices, Predicting Box Offices–Conclusion, Predicting the Stock Market–Predicting the Stock Market, Closing Predictions–Closing Predictions, Workbook Questions
- about the future, Predicting the Future–Predicting the Future
- for box offices, Predicting Box Offices–Conclusion
- closing, Closing Predictions–Closing Predictions
- for elections, Predicting Elections–Predicting Voting Behavior
- Google and, The Promise, The Technology of Recommendation Systems
- for learning, Prediction of Learning–Predicting Elections
- for stock market, Predicting the Stock Market–Predicting the Stock Market
- voting behavior, Predicting Voting Behavior–Predicting Box Offices
- workbook questions, Workbook Questions
- privacy, Conclusion, Case: A Matchmaking Engine, Case: A Matchmaking Engine
- AML systems and, Case: A Matchmaking Engine
- big data and, Conclusion
- matchmaking engine case, Case: A Matchmaking Engine
- Prophet consultancy, Case Study: Customer Lifecycle
- Protect Intellectual Property Act (PIPA), SOPA and PIPA Act: A Modern Grassroots Movement
- Provalis Research, Context
- Prudhoe Bay oil spill, Testing for Correlation
- public relations, Public Relations, Public Relations–Public Relations, Public Relations–Public Relations, PR Often Has No Measurable ROI–PR Often Has No Measurable ROI, Measuring People–Six principles of influence, Measuring People–Case: Spread of the Idea of “Resilient India”, Measuring People–Six principles of influence, Measuring People, Context in PR–Context of the author, Journalism CRM, Communication Is Human–Six principles of influence, PR to Warn–Warning Signals, Examples of PR disasters–Impersonation, Case: McDonald’s, Summary–Workbook, Workbook, Predicting the Future, Bad PR Bias
- about, Public Relations
- bad PR bias, Bad PR Bias
- chapter summary, Summary–Workbook
- communication and, Communication Is Human–Six principles of influence
- context metric and, Measuring People, Context in PR–Context of the author
- crowdsourcing and, Predicting the Future
- disaster examples, Examples of PR disasters–Impersonation
- distributing information, Public Relations–Public Relations, Measuring People–Case: Spread of the Idea of “Resilient India”
- giving warning, Public Relations–Public Relations, PR to Warn–Warning Signals
- journalism CRM, Journalism CRM
- McDonald’s case, Case: McDonald’s
- measuring people, Measuring People–Six principles of influence
- measuring ROI, PR Often Has No Measurable ROI–PR Often Has No Measurable ROI
- reach metric and, Measuring People–Six principles of influence
- workbook questions, Workbook
- purchase intent, Marketing and Social Media: The Promise and the Reality, Purchase Intent, How to Find Purchase Intent, Behavioral Targeting–Behavioral Targeting, Social Targeting–Your Friends Like It..., Homophily versus Influence, Social Connections versus Behavior–Social Connections versus Behavior, The Influencer–The Influencer, Reach Versus Intention–What—or Who—Would Make You Buy?, Social Confirmation Creates Trust, User Ratings, User comments, Peer Pressure–Do Social Confirmation and Peer Pressure Work?, Creating Influence and Intention–The US Military’s Search for Social Media Robots, Creating Influence and Intention
- about, Marketing and Social Media: The Promise and the Reality, Purchase Intent
- behavioral targeting, Behavioral Targeting–Behavioral Targeting
- finding, How to Find Purchase Intent
- gaming the system, Creating Influence and Intention–The US Military’s Search for Social Media Robots
- homophily versus influence, Homophily versus Influence
- influencers and, The Influencer–The Influencer
- peer pressure and, Peer Pressure–Do Social Confirmation and Peer Pressure Work?
- reach versus, Reach Versus Intention–What—or Who—Would Make You Buy?, Creating Influence and Intention
- social confirmation and, Social Confirmation Creates Trust
- social connections versus behavior, Social Connections versus Behavior–Social Connections versus Behavior
- social targeting, Social Targeting–Your Friends Like It...
- user comments and, User comments
- user ratings and, User Ratings
R
- Rand, Paul M., Your Friends Like It...
- randomized controlled trials, Testing for Correlation
- RateMyProfessors.com website, User Ratings
- ratings and reviews, Social Media Is Just Another Channel, Social Connections versus Behavior, User Ratings, What—or Who—Would Make You Buy?
- creating trust, User Ratings
- earned media and, Social Media Is Just Another Channel
- influence of, Social Connections versus Behavior, What—or Who—Would Make You Buy?
- Ratkiewicz, J., The Truth about the Truth
- reach (metric), Social Media: A New Class of Metrics, Reach Does Not Equal Awareness, Social Connections versus Behavior, Reach Versus Intention–What—or Who—Would Make You Buy?, Reach Versus Intention–Reach Versus Intention, Measuring People–Reach in PR, Creating Reach–Creating Reach, Creating Influence and Intention, Spreading Paid Opinions: Grassroots and Astroturfing
- awareness and, Reach Versus Intention–Reach Versus Intention
- brand awareness and, Reach Does Not Equal Awareness
- creating, Creating Reach–Creating Reach
- CTR and, Social Connections versus Behavior
- grassroot movement success factor, Spreading Paid Opinions: Grassroots and Astroturfing
- O’Reilly measurement overview, Social Media: A New Class of Metrics
- public relations and, Measuring People–Reach in PR
- purchase intent versus, Reach Versus Intention–What—or Who—Would Make You Buy?, Creating Influence and Intention
- reading lists, Reading Lists
- reason as sales component, Reason
- reciprocal altruism, Six principles of influence
- reciprocity (influence principle), Six principles of influence
- recommendation systems, Data-Driven Sales, What—or Who—Would Make You Buy?–Recommendation Systems, Collaborative Recommendations, Collaborative Recommendations, Content-Based Recommendations–The Technology of Recommendation Systems, The Technology of Recommendation Systems–How to Build a Recommendation System: Start Small, The Cold-Start Problem, The Cold-Start Problem, Not Enough Data, No Surprises, No Surprises, How to Build a Recommendation System: Start Small–How to Build a Recommendation System: Start Small, Facebook and Open Graph
- about, Data-Driven Sales, What—or Who—Would Make You Buy?–Recommendation Systems
- building, How to Build a Recommendation System: Start Small–How to Build a Recommendation System: Start Small
- cold-start problem, The Cold-Start Problem
- collaborative, Collaborative Recommendations, The Cold-Start Problem
- content-based, Content-Based Recommendations–The Technology of Recommendation Systems
- data sparsity, Not Enough Data
- filtering information, No Surprises
- inaccuracy of, Collaborative Recommendations
- personalization gap and, Facebook and Open Graph
- popular choices and, No Surprises
- technology of, The Technology of Recommendation Systems–How to Build a Recommendation System: Start Small
- Reddy, Ashwin, China’s 50-Cent Bloggers
- regression analysis, The Technology of Recommendation Systems–The Technology of Recommendation Systems
- Reichheld, Fred, Consumer Preference
- Renaissance Technologies fund, Predicting the Stock Market
- resilient India case, Case: Spread of the Idea of “Resilient India”–Case: Spread of the Idea of “Resilient India”
- return on investment, Return on Investment: Was Linda Worth It? (see ROI)
- Riewa, Jens, Happier Employees, Positive Publicity
- risks of metrics, The Risks of Metrics–Overcoming the issues
- robots, Spam and Robots (see bots)
- Rogers, Carl, A Turing Test on Twitter
- ROI (return on investment), Return on Investment: Was Linda Worth It?–Return on Investment: Was Linda Worth It?, Return on Investment: Was Linda Worth It?, Social Sales, PR Often Has No Measurable ROI–PR Often Has No Measurable ROI, Knowledge Bases and Customer Self-Service, The Quest for ROI–The Quest for ROI
- about, The Quest for ROI–The Quest for ROI
- branding campaigns and, Return on Investment: Was Linda Worth It?
- on customer care, Knowledge Bases and Customer Self-Service
- linking effects from social media, Social Sales
- on public relations, PR Often Has No Measurable ROI–PR Often Has No Measurable ROI
- Virgin Atlantic Airways case, Return on Investment: Was Linda Worth It?–Return on Investment: Was Linda Worth It?
- Romney, Mitt, John Sununu–Follower Scandals, Predicting Elections
- Rose, John, Personal Data: Too Sensitive?
- Rosset, Xavier, Conclusion
- Roth, Carol, Collaborative Recommendations
- Rowady, Paul, Predicting the Stock Market
- Rowinski, Dan, SOPA and PIPA Act: A Modern Grassroots Movement
S
- S.M.A.R.T. mnemonic device, The Right Question
- sabermetrics, Cost and Insider Knowledge
- Salathe, Marcel, The Influencer
- sales, Introduction, Introduction, Social Sales–Social Sales, Data-Driven Sales, Reach Versus Intention–What—or Who—Would Make You Buy?, Recommendation Systems–How to Build a Recommendation System: Start Small, Trust, Personality, and Reason, Personal Relationships, Reason, Summary–Workbook
- about, Introduction
- chapter summary, Summary–Workbook
- data-driven, Data-Driven Sales
- personal relationships and, Personal Relationships
- reach versus intention, Reach Versus Intention–What—or Who—Would Make You Buy?
- reason component in, Reason
- recommendation systems, Recommendation Systems–How to Build a Recommendation System: Start Small
- social, Social Sales–Social Sales
- trust component in, Introduction, Trust, Personality, and Reason
- sampling data, Sampling
- Samuel, Arthur, The Technology of Recommendation Systems
- Santorum, Rick, Predicting Elections
- scarcity (influence principle), Six principles of influence
- Schneider, Dave, United Breaks Guitars
- Schufa credit-rating company, Personal Data: Too Sensitive?–Personal Data: Too Sensitive?
- Schwab, Klaus, Case: Haiti
- SCM (Structural Causal Models), Correlation versus causation
- search engine optimization (SEO), Spam and Robots, The Risks of Metrics
- Sebastian, Miguel, Was He Heard?
- secret metrics, Overcoming the issues
- selection bias, Selection Bias
- self-censorship (PR disasters), Self-censorship
- self-servicing customers, Knowledge Bases and Customer Self-Service–Happier Employees
- semantic web, Facebook and Open Graph
- Semasio (company), Behavioral Targeting
- sentiment analytics, Automation and Business Intelligence, Sentiment Algorithm–Context
- SEO (search engine optimization), Spam and Robots, The Risks of Metrics
- al-Sharif, Manal, Warning Signals
- Shirky, Clay, Six principles of influence
- shortlink services, Clicking
- Shulman, Stuart, Case: Dell’s customer care
- Sina Weibo, The Fourth V of Data
- Skou, Kasper, Behavioral Targeting
- Sky TV channel, Resources and Scaling
- small world experiment, How to Spot Bots
- smearing opponents, Smearing Opponents–Follower Scandals, The Case of Facebook
- Smith, Catharine, Collaborative Recommendations
- Snowden, Edward, Context in PR, Conclusion
- social commerce, Social Sales–Social Sales, Recommendation Systems
- social confirmation, Social Confirmation Creates Trust, User Ratings, User comments, Peer Pressure–Do Social Confirmation and Peer Pressure Work?
- creating trust, Social Confirmation Creates Trust
- peer pressure and, Peer Pressure–Do Social Confirmation and Peer Pressure Work?
- user comments, User comments
- user ratings, User Ratings
- social connections versus behavior, Social Connections versus Behavior–Social Connections versus Behavior
- social CRM, Positive Publicity, Dos and Don’ts–Resources and Scaling, Is Social Customer Care the New Commodity?, Social CRM: Market Research, Case Study: Customer Lifecycle–Case Study: Customer Lifecycle, Which Data?–Personal Data: Too Sensitive?, Summary, Workbook
- about, Is Social Customer Care the New Commodity?, Social CRM: Market Research
- chapter summary, Summary
- customer lifecycle case study, Case Study: Customer Lifecycle–Case Study: Customer Lifecycle
- dos and don’ts, Dos and Don’ts–Resources and Scaling
- linking data, Which Data?–Personal Data: Too Sensitive?
- positive publicity, Positive Publicity
- workbook questions, Workbook
- social media, Three Myths about Social Media–Social Media Is Just Another Channel, Social Media Is Just Another Channel, Return on Investment: Was Linda Worth It?
- expectation gap in, Return on Investment: Was Linda Worth It?
- myths about, Three Myths about Social Media–Social Media Is Just Another Channel
- platform categories, Social Media Is Just Another Channel
- social media analytics, The Fourth V of Data, Marketing–Workbook, Marketing and Social Media: The Promise and the Reality–Marketing and Social Media: The Promise and the Reality
- social proof (influence principle), Six principles of influence
- social targeting, Social Targeting–Your Friends Like It...
- socialflow (company), Context of the content
- Soghoian, Christopher, The Case of Facebook
- Sommer, Robin, Creative Discovery
- Sony Ericsson case, Case Sony Ericsson—special words–A Dynamic Approach to Machine Learning
- SOPA (Stop Online Piracy Act), SOPA and PIPA Act: A Modern Grassroots Movement
- Sophos survey, Six principles of influence
- spam blogs, The Myth of Number of Articles
- spamming, Spam and Robots–Spam and Robots, Creating Reach–Creating Reach, How to Spot Bots, Smearing Opponents–Follower Scandals, Creating Influence and Intention–The US Military’s Search for Social Media Robots, Spreading Paid Opinions: Grassroots and Astroturfing–Cause, Access, and Reach, The Opposite of Virality: Suppressing Messages
- about, Spam and Robots–Spam and Robots
- creating influence and intention, Creating Influence and Intention–The US Military’s Search for Social Media Robots
- creating reach, Creating Reach–Creating Reach
- smearing opponents, Smearing Opponents–Follower Scandals
- spreading paid opinions, Spreading Paid Opinions: Grassroots and Astroturfing–Cause, Access, and Reach
- suppressing messages, The Opposite of Virality: Suppressing Messages
- Twitter and, How to Spot Bots
- Spencer, Stephan, Overcoming the issues
- StackOverflow, More Data
- StatusPeople (company), How to Spot Bots
- stock market, predicting, Predicting the Stock Market–Predicting the Stock Market
- Stop Online Piracy Act (SOPA), SOPA and PIPA Act: A Modern Grassroots Movement
- Structural Causal Models (SCM), Correlation versus causation
- structured data, Error, or Why Structured Data Is Superior–Unstructured, Subsets
- subprime mortage crisis, Wrong behavior
- subsets of data, Subsets–No truth
- Sununu, John, John Sununu
- supervised learning, The Technology of Recommendation Systems–The Technology of Recommendation Systems, A Dynamic Approach to Machine Learning
- SupportIndustry.com portal site, Customer Care 2.0
- suppressing messages, The Opposite of Virality: Suppressing Messages
- Sysomos (company), Sentiment Algorithm
T
- T-commerce, Social Sales
- Tang, Lei, Social Connections versus Behavior
- targeted advertising, The Promise
- tastemakers, Speed to disaster
- TBG Digital, Social Media Is Cheap
- technology of recommendation systems, The Technology of Recommendation Systems–The Technology of Recommendation Systems, The Cold-Start Problem, Not Enough Data, No Surprises, How to Build a Recommendation System: Start Small–How to Build a Recommendation System: Start Small
- about, The Technology of Recommendation Systems–The Technology of Recommendation Systems
- building recommendation systems, How to Build a Recommendation System: Start Small–How to Build a Recommendation System: Start Small
- cold-start problem, The Cold-Start Problem
- data sparsity, Not Enough Data
- popular choices and, No Surprises
- telecommunication industry, Case Study: Customer Lifecycle–Case Study: Customer Lifecycle, Case Study: Major Telecom Company–Was He Heard?
- testing, Testing for Correlation–Testing for Correlation, No truth
- for correlation, Testing for Correlation–Testing for Correlation
- keyword setup, No truth
- Thong, Jacqueline, Social Media: Too Shallow?
- Thrun, Sebastian, Prediction of Learning
- The Tipping Point (Gladwell), Your Friends Like It...
- Toubon, Jacques, Was He Heard?
- touchpoints with customers, Turning CRM Around
- tracking, Behavioral Targeting, Engagement
- engagement metric, Engagement
- online, Behavioral Targeting
- Treehugger media outlet, A Turing Test on Twitter
- triggers to buying, Marketing and Social Media: The Promise and the Reality, Reach Does Not Equal Awareness, Where is Linda?, Social Targeting–Your Friends Like It...
- brand awareness as, Reach Does Not Equal Awareness
- social media as, Marketing and Social Media: The Promise and the Reality
- social targeting and, Social Targeting–Your Friends Like It...
- Virgin Atlantic Airways case, Where is Linda?
- TripAdvisor.com website, User comments
- trolls, customer care and, Mind the Trolls
- Trotter, Fred, Consumer Preference
- trust as sales driver, Introduction, Data-Driven Sales, Reach Versus Intention, Social Confirmation Creates Trust, The Cold-Start Problem, Trust, Personality, and Reason
- about, Introduction, Trust, Personality, and Reason
- creating purchase intent, Reach Versus Intention
- data-driven sales and, Data-Driven Sales
- recommendation systems and, The Cold-Start Problem
- social confirmation and, Social Confirmation Creates Trust
- Turing Test (bot), A Turing Test on Twitter
- Turing, Alan, A Turing Test on Twitter
- Twitter, More Data, Marketing and Social Media: The Promise and the Reality, Social Media Is Fast, Social Media: A New Class of Metrics, Social Sales, Speed to disaster, Case: McDonald’s, Warning Signals, Customer Care 2.0, Positive Publicity, Resources and Scaling, Case Sony Ericsson—special words, Spam and Robots, How to Spot Bots, How to Spot Bots, How to Spot Bots, How to Spot Bots, John Sununu, Creating Influence and Intention, Viral by Design, Predicting the Future, Changes Over Time and Space
- about, More Data, Marketing and Social Media: The Promise and the Reality, Social Media Is Fast
- bot detection and, How to Spot Bots, How to Spot Bots, How to Spot Bots
- bot influence and, Creating Influence and Intention
- changes over time and space, Changes Over Time and Space
- checking rate of escalation, Speed to disaster
- Comcast service channel on, Customer Care 2.0
- customer care and, Resources and Scaling
- General Electric and, Predicting the Future
- KLM airline and, Positive Publicity
- O’Reilly measurement overview, Social Media: A New Class of Metrics
- smearing opponents and, John Sununu
- social commerce and, Social Sales
- social spam and, Spam and Robots
- Sony Ericsson case, Case Sony Ericsson—special words
- spamming and, How to Spot Bots
- trending topics, Case: McDonald’s
- virality by design, Viral by Design
- Women2Drive campaign and, Warning Signals
- Twitter API, A Case of Airline Customer Satisfaction
- 2Style4You, The Promise
U
- unintended consequences, law of, Spam and Robots
- United Airlines case, United Breaks Guitars, Ask the Right Question
- unstructured data, Unstructured, Subsets
- uplift (metric), Social Connections versus Behavior
- US Air Force, The US Military’s Search for Social Media Robots
- using the right data (AML), Analytics Focus, Predicting the Future, Use the Right Data, Which Data Is Important?–Case: A Matchmaking Engine, Causation–All of the above, Causation–All of the above, Testing for Correlation–Testing for Correlation, Error, or Why Structured Data Is Superior–Unstructured, Error, or Why Structured Data Is Superior–Unstructured, Cost and Insider Knowledge, Cost and Insider Knowledge, Case: A Matchmaking Engine–Case: A Matchmaking Engine, Case: A Matchmaking Engine, Case: A Matchmaking Engine, Case: A Matchmaking Engine, Case: A Matchmaking Engine, Data Selection–Case: Haiti, Case: Haiti, Summary, Workbook
- about, Use the Right Data
- analytic focus, Analytics Focus
- British Petroleum case and, Causation–All of the above
- causation and, Causation–All of the above, Case: A Matchmaking Engine
- chapter summary, Summary
- costs for data retrieval, Cost and Insider Knowledge, Case: A Matchmaking Engine
- data selection, Data Selection–Case: Haiti
- error and, Error, or Why Structured Data Is Superior–Unstructured, Case: A Matchmaking Engine
- Haiti case, Case: Haiti
- identifying which data, Which Data Is Important?–Case: A Matchmaking Engine
- insider knowledge and, Cost and Insider Knowledge
- matchmaking engine case, Case: A Matchmaking Engine–Case: A Matchmaking Engine
- predicting the future, Predicting the Future
- privacy considerations, Case: A Matchmaking Engine
- structured data, Error, or Why Structured Data Is Superior–Unstructured
- testing for correlation, Testing for Correlation–Testing for Correlation
- workbook questions, Workbook
V
- vaccinations, child, The Influencer
- value (Vs of big data), The Fourth V of Data, The Promise, More Data, PR Often Has No Measurable ROI–PR Often Has No Measurable ROI
- about, The Fourth V of Data
- finding, The Promise, More Data
- measuring ROI and, PR Often Has No Measurable ROI–PR Often Has No Measurable ROI
- Van Dongen, Rachel, Predicting Elections
- variety (Vs of big data), The Fourth V of Data, More Data
- velocity (Vs of big data), The Fourth V of Data, More Data
- Verizon (company), Case Study: Customer Lifecycle
- Vernal, Mike, Facebook and Open Graph
- virality and viral outbreaks, Social Media Is Fast, Branding, Underestimation of virality, No Early Warning Systems–Case: McDonald’s, No Early Warning Systems–Nondeterministic, No Early Warning Systems–Case: McDonald’s, No Early Warning Systems, Smart Selection, Contagiousness–How to Spot Attempts to Create Contagiousness, Viral by Design–Viral by Design
- about, No Early Warning Systems
- gaming the system and, Contagiousness–How to Spot Attempts to Create Contagiousness
- nondeterministic timing of, No Early Warning Systems–Nondeterministic
- rewording term, Branding
- social media speed myth and, Social Media Is Fast
- speed to disaster, No Early Warning Systems–Case: McDonald’s
- spreading customer dissatisfaction, Smart Selection
- underestimating, Underestimation of virality
- viral by design, Viral by Design–Viral by Design
- warning systems and, No Early Warning Systems–Case: McDonald’s
- Virgin Atlantic Airways case, Case: Virgin Atlantic Airways–Return on Investment: Was Linda Worth It?
- volume (Vs of big data), The Fourth V of Data, More Data
- Vonn, Lindsey, Smart Selection
- voting behavior, predicting, Predicting Voting Behavior–Predicting Box Offices
W
- Wagstaff, Bob, Ask the Right Question
- Warby Parker (manufacturer), Reach Versus Intention
- warning task (public relations), Public Relations–Public Relations, PR to Warn, Examples of PR disasters–Impersonation, No Early Warning Systems–Case: McDonald’s, Speed to disaster, Speed to disaster, Case: McDonald’s, Warning Signals
- about, Public Relations–Public Relations, PR to Warn
- early warning systems and, No Early Warning Systems–Case: McDonald’s
- McDonald’s case, Case: McDonald’s
- organizational issues, Speed to disaster
- PR disaster examples, Examples of PR disasters–Impersonation
- technical requirements, Speed to disaster
- warning signals, Warning Signals
- web scraping, Analytical CRM: The New Frontier
- Weiner, Jeff, The Data Focus
- WikiLeaks, Conclusion
- Wikipedia, Marketing, SOPA and PIPA Act: A Modern Grassroots Movement
- Wildfire Interactive, Where is Linda?
- Women2Drive campaign, Warning Signals
- word clouds, Context of the author
- Wordstream study, Social Targeting
- World Economic Forum, Analytics Focus, Clipping–The Myth of Number of Articles, Engagement, Case: Spread of the Idea of “Resilient India”–Case: Spread of the Idea of “Resilient India”, Ask the Right Question
X
- X Factor (TV show), Context of the content
Y
- Yagan, Sam, Case: A Matchmaking Engine
- Yahoo!, Social Sales
- Yes Men activist group, Impersonation
- York, Jillian, The Opposite of Virality: Suppressing Messages
- YouTube, Speed to disaster, Warning Signals, United Breaks Guitars, Positive Publicity, Mind the Trolls, Kony2012, Ask the Right Question, Ask the Right Question
- checking rate of escalation, Speed to disaster
- Deutsche Bahn and, Mind the Trolls
- KLM airline and, Positive Publicity
- Kony2012 video, Kony2012
- measuring views, Ask the Right Question
- United Airlines case, United Breaks Guitars, Ask the Right Question
- Women2Drive campaign and, Warning Signals
Z
- Zafu, The Promise
- Zopf, Yann, Clipping, The Myth of Number of Articles
- Zuckerberg, Mark, Public Relations, Facebook and Open Graph
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