@aweigend aweigend@stanford. edu Andreas Weigend
@aweigend [email protected] Andreas Weigend .
Dec 22, 2015
Welcome message from author
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
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 [email protected]
Related Documents
