Agenda. The Power of Social Data: Transforming Big Data into Decisions. 1. Data and Decisions. 2. Amazon as Data Refinery

Transcription

1 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 Handout page 1

2 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 Handout page 2

3 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 Handout page 3

4 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 Handout page 4

5 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 Handout page 5

6 Big Data: Mindset to turn Mess into Decisions <when> T00:17: :00</when> <gx:coord> </gx:coord> <when> T00:46: :00</when> <gx:coord> </gx:coord> <when> T00:47: :00</when> <gx:coord> </gx:coord> 11 Berkeley SF Home Facebook Stanford Google 12 Handout page 6

7 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 Handout page 7

8 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 Handout page 8

9 Big Data = Mindset to turn Mess into Decisions External (facing the outside) Internal (within the company) 19 What changed? The Journey of Amazon 20 Handout page 9

10 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 Handout page 10

11 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 Handout page 11

12 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 Handout page 12

13 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 Handout page 13

14 Content Context Connection Conversation The 4 C s Markets are Conversations 2013 Conversations are Markets 30 Handout page 14

15 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 Handout page 15

16 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 Handout page 16

17 Non Social: Audience Shift in Mindset Social: Connected Individual On the Internet, nobody knows you re a dog Handout page 17

18 2013 On the Internet, everybody knows you re a dog Non social: Attributes Shift in Identity Social: Relationships 38 Handout page 18

19 Shift in Business Models Non social: hotels.com, craigslist Social: airbnb, lyft, relay rides, 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 Handout page 19

20 Connected Individuals Rule #4: Embrace transparency: Make it trivially easy for people to connect, contribute, and collaborate 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 Handout page 20

21 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 Handout page 21

22 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 Handout page 22

23 Questions for me? Andreas Weigend weigend.com Social Data Lab 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 Handout page 23

24 Data Science vs Business Intelligence 50 Data Science vs Business Intelligence 51 Handout page 24