@aweigend Andreas Weigend

New Course Spring Quarter Social Data and E-Business MS&E 237 (formerly Statistics 252) 3 Units Tue Thu 4:15 PM - 5:30 PM More info at facebook.com/socialdatarevolution

Thesis 1: Move from E-Business to Me-Business to We-Business

Thesis 2: Bridge the Physical and the Digital

Thesis 3: The SDR changes (almost) everything

Thesis 4: Help your customers make better decisions. They are smart.

Connecting Computers

Connecting Pages

Connecting People

Underlying?

Data The amount of data created by each person doubles every 1.5 … 2 years □ after five years  x 10 □ after ten years  x 100 □ after twenty years  x 10000

Colin Harrison The Next Big Thing 1996

1 billion connected flash players

40 billion RFID tags worldwide

Pay-as-you-drive car insurance (GPS)

IMMI Listening into your room every 30 seconds, for 10 seconds.

Biology: ~100k yrs Time Scales Social Norms: ~10 years Data, Technology: ~1 year “Real Time”: ~h? m? s?

99% DNA overlap

Abundant? Scarce?

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Social Data Revolution How the Changes (Almost) Everything

Social Data = Shared Data pieces of content shared per month 15 billion

Or is information just an excuse for communication? Purpose of communication: to transmit information?

100+ million users per day 350+ million uniques January minutes avg per user per day < 1 cent per user per day

Social Data = Shared Data 20 hours of videos uploaded every minute

Social Data = Shared Data 1 billion videos watched per..... day

Introduction Data I C2B (Customer-to-Business) II C2C (Customer-to-Customer) III C2W (Customer-to-World) IV Insights Outline

C2B Part I:

SCHWAB

Imagine... You knew all the things people here have bought... what would you do? You knew all of their friends You knew their secret desires

1. People know what they want 2. People know what’s out there 3. People know what they will actually get 3 Myths about Decision Making

Customers who bought this item also bought …

Customers who viewed this item also viewed …

Customers who viewed this item ultimately bought …

… based on clicks and purchases Amazon.com helps people make decisions …

How do you know peoples’ secret desires ?

Situation Location Device Attention Transactions Clicks Intention Search Data Sources

Business Customers

C2C Part II:

C2C = Customer-to-Customer Customers share with each other

Amazon.com Share the Love

Amazing conversion rates since you chose: Content (the item ) Context ( you just bought that item) Connection (you ask Amazon to your friend ) Conversation (information as excuse for communication)

Connecting People

Social network intelligence

Social graph targeting Provide list of prospects

Fraud reduction – Provide risk scores

privatepublic

C2C = Customer-to-Customer Customers share with each other

C2W = Customer-to-World Customers share with everybody

PLEASE HELP.

C2W Part III:

Amazon.com: Public sharing of interests

You are your tags Tags are distilled attention

Insights Part IV:

+ wheels + heels = =

Product Customer Brand

From controlled production for the masses… … to uncontrolled production by the masses

Web 0 Computers Web 1 Pages Web 2 People Data

Social Data Revolution

Shift in Customer Expectations People trust reviews and comments by others more than marketing messages They use their friends’ attention to filter information and discover

Social filter

Q & A

Real Time Web April 20, 2010 MIT Stanford VLAB GSB, Bishop Auditorium Any pointers to related startups?