Milano, 04 Dec 2013 The Power of Social Data: Transforming Big Data into Decisions Andreas Weigend 1 1. Data and Decisions Value of Data? Agenda 2. Amazon as Data Refinery Equation of Business 3. Implications of Social Data Revolution Audience Connected Individuals and Context 4. Summary Questions via Twitter, use 2 www.weigend.com Handout page 1
15 years ago: Connecting Pages (Google) 10 years ago: Connecting People (FB) 5 years ago: Connecting Apps (Apple) Now: Connecting Data 3 Today, in a single day, we are creating more data than mankind did from its beginning through 2000 4 www.weigend.com Handout page 2
Mobile Context: Many sensors Identity: Proxy for person Easy for advertiser to reach user, but high cost of interrupt if inappropriate Easy for user to contribute 5 Social Data: Two Meanings 1. Relationships between people ( social graph, e.g., on Facebook or LinkedIn) 2. Data people share (or socialize, e.g., checkin, purchase, book review, picture) Note: Social Media differs from Social Data (e.g., GPS) 6 www.weigend.com Handout page 3
Social Data Revolution Google has changed the way a billion people think about information Facebook has changed the way a billion people think about identity Amazon has changed the way a billion people think about purchases 7 1. Transport energy Industrial Revolution Production 2. Transport bits Information Revolution Communication 3. Create (and share) bits Social Data Rev 8 www.weigend.com Handout page 4
Rule #1: Data and Decisions Start with a question, not with the data E.g., Which route do I take? E.g., Who do I work with? 9 Mindset Skillset Toolset Dataset 10 www.weigend.com Handout page 5
Big Data: Mindset to turn Mess into Decisions <when>2013 05 28T00:17:08.341 07:00</when> <gx:coord>11.0955646 47.4944176 0</gx:coord> <when>2013 05 28T00:46:14.410 07:00</when> <gx:coord>11.0894932 47.4880099 0</gx:coord> <when>2013 05 28T00:47:14.425 07:00</when> <gx:coord>11 1069126 47 5154249 0</gx:coord> 11 Berkeley SF Home Facebook Stanford Google 12 www.weigend.com Handout page 6
Imagine you had your geolocation from the last decade readily available at your fingertips What question would you ask? How would knowing that it is recorded 24/7 change your behavior? 13 London 1854 14 www.weigend.com Handout page 7
google.com/history 15,317 searches 17 What data would you pay for most? 1. Geolocation: Where did a customer go? 2. Search history: What did she search for? 3. Purchase history: What did she buy? 4. Social graph: Who are her friends? 5. Demographics and similar attributes 18 www.weigend.com Handout page 8
Big Data = Mindset to turn Mess into Decisions External (facing the outside) Internal (within the company) 19 What changed? The Journey of Amazon 20 www.weigend.com Handout page 9
What changed? Algorithms Data The Journey of Amazon AI BI CI DI 21 The Journey of Amazon What changed? Algorithms Data AI BI CI DI What did not change? Ask for forgiveness, not for permission True customercentricity Recommendations and Discovery 22 www.weigend.com Handout page 10
1. Data and Decisions Value of Data? Agenda 2. Amazon as Data Refinery Equation of Business 3. Implications of Social Data Revolution Audience Connected Individuals and Context 4. Summary Questions via Twitter, use 23 Goal: Help people make better decisions Data strategy: Make it trivially easy to Contribute Connect Amazon as Data Refinery Collaborate 24 www.weigend.com Handout page 11
Equation of Business Expresses business strategy, values etc. Needed for evaluation of experiments Rule #2: Base the equation of your business on metrics that matter to your customers 25 Equation of Business Rule #3: Focus on decisions and actions, and design for feedback 26 www.weigend.com Handout page 12
5 Stages of Amazon Recommendations 1. Manual (Experts) 2. Implicit (Clicks, Searches) 3. Explicit (Reviews, Lists) 4. Situation (Local, Mobile) 5. Social graph (Connections) 27 Social Commerce Amazon s Share the Love www.weigend.com Handout page 13
Content Context Connection Conversation The 4 C s 29 2000 Markets are Conversations 2013 Conversations are Markets 30 www.weigend.com Handout page 14
Where are the Conversations? Company Consumers 1. Data and Decisions Value of Data? Agenda 2. Amazon as Data Refinery Equation of Business 3. Implications of Social Data Revolution Audience Connected Individuals and Context 4. Summary Questions via Twitter, use 32 www.weigend.com Handout page 15
Data sources for marketing a new phone product Segmentation (Demographics, Loyalty) Social Graph (Who called whom?) Adoption rate 4.8x 1.35% 0.28% Segmentation Social Graph www.weigend.com Handout page 16
Non Social: Audience Shift in Mindset Social: Connected Individual 35 1993 On the Internet, nobody knows you re a dog www.weigend.com Handout page 17
2013 On the Internet, everybody knows you re a dog Non social: Attributes Shift in Identity Social: Relationships 38 www.weigend.com Handout page 18
Shift in Business Models Non social: hotels.com, craigslist Social: airbnb, lyft, relay rides, 39 1. Digitize: E commerce Focus on company and products E, Me, We! 2. Share: Me commerce Focus on consumer and attributes 3. Connect: We c0mmerce Focus on connection between consumers 40 www.weigend.com Handout page 19
Connected Individuals Rule #4: Embrace transparency: Make it trivially easy for people to connect, contribute, and collaborate 41 1. Data and Decisions Agenda 2. Amazon as Data Refinery 3. Implications of Social Data Revolution 4. Outlook and Summary Last chance to tweet questions, 42 www.weigend.com Handout page 20
GLΛSS 43 The 4 Data Rules 1. Start with a question, not with the data 2. Base the equation of your business on metrics that matter to your customers 3. Focus on decisions and actions, design for feedback 4. Embrace transparency: Make it trivially easy for people to connect, contribute, and collaborate 45 www.weigend.com Handout page 21
Some Data Beliefs 1. Let people do what people are good at, and computers do what computers are good at 2. Build stuff that enables a future you want to live in 3. Give data to get data 46 Questions for you 1. Do your customers understand the value they get when they give you data? 2. Does your product or service get better over time and with data, or worse? 47 www.weigend.com Handout page 22
Questions for me? Andreas Weigend weigend.com Social Data Lab aweigend@stanford.edu 48 Data literate Data Scientist Able to handle large data sets Understands domain and modeling Want to communicate and collaborate Curious with can do attitude 49 www.weigend.com Handout page 23
Data Science vs Business Intelligence 50 Data Science vs Business Intelligence 51 www.weigend.com Handout page 24