@aweigendaweigend@stanford.e

du

Andreas Weigendwww.weigend.com

New Course Spring QuarterSocial Data and E-BusinessMS&E 237 (formerly Statistics 252)

3 Units

Tue Thu 4:15 PM - 5:30 PM

More info at www.weigend.com

facebook.com/socialdatarevolution

Thesis 1:

Move fromE-BusinesstoMe-Businessto We-Business

Thesis 2: Bridge thePhysical and the Digital

Thesis 3:

The SDR changes (almost) everything

Thesis 4: Helpyourcustomersmake betterdecisions.

They are smart.

ConnectingComputers

ConnectingPages

ConnectingPeople

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)

Integrated Media Measurement Inc

•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% DNAoverlap

Abundant?

Scarce?

http://www.skout.com | http://www.boyahoy.com

Social Data RevolutionHow the Changes (Almost)

Everything

Social Data = Shared Data

................pieces of content

shared

per month

15 billion

Or is information justan excuse for

communication?

Purpose of communication:to transmit information?

100+ million users per day350+ million uniques January 2010

40 minutes avg per user per day

< 1 cent per user per day

Social Data = Shared Data

20 hoursof videos uploaded

every minute

Social Data = Shared Data

1 billionvideos watched

per .....da

y

Introduction Data

I C2B (Customer-to-Business)

II C2C (Customer-to-Customer)

III C2W (Customer-to-World)

IV Insights

Outline

C2BPart I:

+1 800-4-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 peoplemake decisions…

How do you know peoples’secret desires?

Situation Location

Device

Attention Transactions

Clicks

Intention Search

Data Sources

Business

Customers

C2CPart 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 email 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

private public

C2C = Customer-to-Customer

Customers share with each other

C2W = Customer-to-World

Customers share with everybody

PLEASE HELP.

C2WPart III:

Amazon.com: Public sharing of interests

You are your tags Tags are distilled attention

Top Tagsweb2.0weigendstanfordamazonpeopledataminingtechnologystatisticsinternetblogdatawebsocialscienceandreasanalyticsresearch

10196575745433623232222151514121211

http://www.mrtweet.com

InsightsPart IV:

+ wheels

+ heels

=

=

Product

Customer

Brand

From controlled production for the masses…

… to uncontrolled production by the masses

Web 0 Computers

Web 1Pages

Web 2People

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

Socialfilter

http://weigend.com/blog @aweigend

Q & A

Real Time Web April 20, 2010 MIT Stanford VLABGSB, Bishop Auditorium

Any pointers to related startups?Email andreas@weigend.com