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Andreas Weigend @aweigend www.weigend.com. San Francisco, CA 03 May 2010. 1990’s: Search - find 2000’s: Social - share 2010’s: Mobile - create. 3 Decades of Innovation. Social Data Revolution. How the. Changes (A lmost ) Everything. Social Data Revolution.
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Andreas Weigend @aweigend www.weigend.com San Francisco, CA 03 May 2010
1990’s: Search - find2000’s: Social - share2010’s: Mobile - create 3 Decades of Innovation
Social Data Revolution How the Changes (Almost) Everything
Social Data Revolution • How do we “utilize” the “community”? • Who do we listen to? • Who do we co-create with? • Physical friends • Peers (similar properties to you) • Ad hoc (e.g., for car purchase) • Experts (what bestows authority?) • Institution? Past action? • Reputation/ brand as shortcut to allocate attention
Social Data Revolution In the last minute • 4,000,000 search queries, • 500,000 pieces of content shared on FB, • 100,000 product searches on Amazon.com, • 40,000 bit.ly urls created, • 40,000 tweets sent
Data creation andsharing • Who creates data? • Data is the digital air in which we breathe • How will this data be used? • Improve product design, service delivery, relationships • How will this data be shared? • Every company is a publishing company • What (if anything) does it mean to “own” data?
SocialData Revolution 1800’s: Transport energy Industrial Revolution 1900’s: Transport data Information Revolution 2000’s: Create and share data
private public
Case study: weigend.com/blog Social: Distributed to FB friends
CompareFBconnecton blog withtraditional contact box (no social element)
Data • The amount of data each person creates doubles every 1.5 … 2 years • □ after five years x 10 • □ after ten years x 100 • □ after twenty years x 10000
Time Scales Biology: ~100k yrs Data, Technology: ~1year Social Norms: ~10 years
Social Data Revolution How the Changes (Almost) Everything
Purpose of communication:to transmit information? Or is information justan excuse for communication?
Web 0 Computers Web 1Pages Web 2People Data
C2B Part I:
Imagine... • You knew all the things people here have bought • You knew all of their friends • You knew their secret desires ... what would you do?
Decision making Discovery Recommendations
…but people want to discover and help with decisions! Google helps people find stuff
Amazon.com helps people make decisions… …based on reviews
C2B Data Strategy: • - Reviews • - Purchases, Clicks…
Amazon.com helps people make decisions… … based on clicks and purchases
Process of creating and refining product space awareness… Shopping? … only occasionallypunctuated by purchases
How do you know peoples’ secret desires? Accounting
Data Sources • Attention • Transactions • Clicks • Intention • Search • Context • Geolocation • Device
The Social Graph • Connection data
New phone product: How to market? • Traditional segmentation • Demographics • Loyalty • Connection data • Who called whom?
1.35% Adoptionrate 4.8x 0.28% • Traditionalsegmentation • Connection data
Business Customers
C2C Part II:
C2C = Customer-to-Customer • Customers share with each other
Amazingconversion 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)