2013 the power of social data: dec transforming big data into decisions andreas weigend · 2020. 7....

The Power of Social Data: Transforming Big Data

into Decisions

Andreas Weigend bit.ly/weigenditalia

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Mila

no

, 04

Dec

20

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Agenda 1. Data and Decisions

Value of Data?

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 @aweigend 2

15 years ago: Connecting Pages (Google)

10 years ago: Connecting People (FB)

5 years ago: Connecting Apps (Apple)

Now: Connecting Data

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Today, in a single day,

we are creating more data

than mankind did

from its beginning

through 2000

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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

Social Data: Two Meanings

1. Relationships between people (“social graph”,

e.g., on Facebook or LinkedIn)

2. Data people share (or “socialize”, e.g., check-

in, purchase, book review, picture) -------------------------------------------------------------------------------------------------------------------------------------------------------------

Note: Social Media differs from Social Data (e.g., GPS) 6

• 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

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Social Data Revolution

1. Transport energy Industrial Revolution

Production

2. Transport bits Information Revolution

Communication

3. Create (and share) bits Social Data Rev

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Data and Decisions

Rule #1:

Start with a question, not with the data

E.g., Which route do I take?

E.g., Who do I work with?

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Mindset

Skillset

Toolset

Dataset

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Big Data: Mindset

to turn Mess into Decisions

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<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>

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Stanford

Berkeley

Google

Facebook

SF Home

Imagine…

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…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?

London 1854

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google.com/history

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15,317 searches

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

Big Data = Mindset

to turn Mess into Decisions

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• External (facing the outside)

• Internal (within the company)

The Journey of Amazon

What changed?

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The Journey of Amazon

What changed?

• Algorithms Data

• AI

• BI

• CI

• DI

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The Journey of Amazon

What changed?

• “Ask for forgiveness, not for permission”

• True customer-centricity

• Recommendations and Discovery

What did not change?

• Algorithms Data

• AI

• BI

• CI

• DI

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Agenda 1. Data and Decisions

Value of Data?

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 @aweigend 23

Goal: Help people make better decisions

Data strategy: Make it trivially easy to

Contribute

Connect

Collaborate 24

Amazon as Data Refinery

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

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Rule #3:

Focus on decisions and actions, and design

for feedback

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Equation of Business

5 Stages of Amazon Recommendations

1. Manual (Experts)

2. Implicit (Clicks, Searches)

3. Explicit (Reviews, Lists)

4. Situation (Local, Mobile)

5. Social graph (Connections)

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Social Commerce

Amazon’s Share the Love

The 4 C’s

• Content

• Context

• Connection

• Conversation

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Markets are Conversations

Conversations are Markets

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2000

2013

Company

Consumers

Where are the Conversations?

Agenda 1. Data and Decisions

Value of Data?

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 @aweigend 32

Data sources for marketing a new phone product

Social Graph

(Who called whom?)

Segmentation

(Demographics, Loyalty)

Social Graph Segmentation

0.28%

Adoption rate

1.35%

4.8x

Non-Social: Audience

Social: Connected Individual

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Shift in Mindset

“On the Internet, nobody knows you’re a dog”

1993

“On the Internet, everybody knows you’re a dog”

2013

Shift in Identity

Non-social: Attributes

Social: Relationships

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Shift in Business Models

Non-social: hotels.com, craigslist

Social: airbnb, lyft, relay rides,

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E, Me, We! 1. Digitize: E-commerce

Focus on company and products

2. Share: Me-commerce

Focus on consumer and attributes

3. Connect: We-c0mmerce

Focus on connection between consumers 40

Rule #4:

Embrace transparency: Make it trivially easy

for people to connect, contribute, and

collaborate

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Connected Individuals

Agenda 1. Data and Decisions

2. Amazon as Data Refinery

3. Implications of Social Data Revolution

4. Outlook and Summary

Last chance to tweet questions, @aweigend 42

GLΛSS

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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

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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

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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?

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Questions for me?

Andreas Weigend

weigend.com

Social Data Lab

aweigend@stanford.edu

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Data Scientist • Data literate

• Able to handle large data sets

• Understands domain and modeling

• Want to communicate and collaborate

• Curious with “can-do” attitude 49

Data Science vs Business Intelligence

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Data Science vs Business Intelligence

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