1. @aweigend aweigend@stanford.edu Andreas Weigend www.weigend.com

2. 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 www.weigend.com facebook.com/socialdatarevolution

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

4. Thesis 2: Bridge the Physical and the Digital

5. Thesis 3: The SDR changes (almost) everything

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

7. Connecting Computers

8. Connecting Pages

9. Connecting People

10. Underlying?

11. 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

13. Colin Harrison The Next Big Thing 1996

14. 1 billion connected flash players

15. 40 billion RFID tags worldwide

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

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

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

19. 99% DNA overlap

20. Abundant? Scarce?

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

23. Social Data Revolution How the Changes (Almost) Everything

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

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

26. 100+ million users per day 350+ million uniques January 2010 40 minutes avg per user per day < 1 cent per user per day

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

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

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

30. C2B Part I:

31. +1 800-4-SCHWAB

32. 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

33. 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

34. Customers who bought this item also bought …

35. Customers who viewed this item also viewed …

36. Customers who viewed this item ultimately bought …

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

38. How do you know peoples’ secret desires ?

39. Situation Location Device Attention Transactions Clicks Intention Search Data Sources

41. Business Customers

42. C2C Part II:

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

44. Amazon.com Share the Love

45. 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)

50. Connecting People

51. Social network intelligence

52. Social graph targeting Provide list of prospects

53. Fraud reduction – Provide risk scores

54. privatepublic

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

56. C2W = Customer-to-World Customers share with everybody

57. PLEASE HELP.

58. C2W Part III:

60. Amazon.com: Public sharing of interests

64. You are your tags Tags are distilled attention

66. http://www.mrtweet.com

70. Insights Part IV:

71. + wheels + heels = =

72. Product Customer Brand

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

74. Web 0 Computers Web 1 Pages Web 2 People Data

75. Social Data Revolution

76. 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

77. Social filter

78. http://weigend.com/blog @aweigend

79. Q & A

80. Real Time Web April 20, 2010 MIT Stanford VLAB GSB, Bishop Auditorium Any pointers to related startups? Email andreas@weigend.com