1. Andreas Weigend @aweigend www.weigend.com

3. Who creates data? Production : Data is digital air How will this data be shared? Distribution : Everyone is a publisher What will this data be used for? Consumption : Behavior changes

4. Collect Solicit Mine Segment Share Distribute Interpret Empower A Shift in Language

5. 1800’s: Transport energy  Industrial Revolution 1900’s: Transport data  Information Revolution 2000’s: Create data  Social Data Revolution Technologies Enabling Innovation

6. 4,000,000Web searches 500,000Content shares 100,000Product searches 40,000Tweets created 40,000Links shortened In the last sixty seconds…

7. us.hsmglobal.com/contenidos/…  bit.ly/WIF2010

8. 1990’s: Search  find 2000’s: Social  share 2010’s: Mobile  create Waves of Innovation  Social Data Revolution

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

10. Introduction Data and Behavior I C2B (Customer-to-Business) II C2C (Customer-to-Customer) III C2W (Customer-to-World) IV Insights 4:55–5:00pm Q&A Agenda

11. Connecting Computers

12. Connecting Pages

13. Connecting People

14. Underlying ?

15. Data The amount of data each person creates doubles every 1.5 … 2 years 2x time?

16. 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

18. + Computation Since then… + Communication + Sensing

19. 1 billion connected sensors

20. 40 billion RFID tags

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

24. Monitors your excercise and sleep

25. 99% DNA overlap

26. Biology: ~100k yrs Time Scales Social Norms: ~10 years Data, Technology: ~1 year

27. Introduction Data and Behavior I C2B (Customer-to-Business) II C2C (Customer-to-Customer) III C2W (Customer-to-World) IV Insights 4:55–5:00pm Q&A Agenda

28. C2B Part I:

29. +1 800-4-SCHWAB

30. 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

31. …based on reviews Amazon.com helps people make decisions…

32. Customers who bought this item also bought …

33. Customers who viewed this item also viewed …

34. Customers who viewed this item ultimately bought …

35. Social proof: Put your money where your mouth is

36. How do you know peoples’ secret desires ? World Innovation Forum

37. Situation Geo-location Device Attention Clicks, Transactions Intention Search Data Sources Connection Social graph

38. New phone product: How to market? Connection data Who called whom? Traditional segmentation Demographics Loyalty

39. Connection data Traditional segmentation 0.28% Adoption rate 1.35% 4.8x

40. Business Customers

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

42. C2C Part II:

43. Amazon.com Share the Love

44. Result: Amazing conversion rates since customer chooses Content (the item ) Context ( she just bought that item) Connection (she asked Amazon to email her friend ) Conversation (information as excuse for communication)

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

46. What do my friends think of this product?

47. Social graph targeting Provide list of prospects

48. Fraud reduction – Provide risk scores

49. Social network intelligence

50. C2W Part III:

51. Amazon.com: Public sharing of interests

52. Add on-line features to off-line products…

53. Consumers - Engage - Share - Connect 3 times per week

54. “We are not in the business of keeping the media companies alive.” “We are in the business of connecting with consumers.” Trevor Edwards Nike Corporate Vice President for Brand and Category Management Q: Or rather in the business of facilitating consumers to connect with each other?

55. Search tweets Create tweets Follow users

56. The Illusion of an Audience

58. Insights Part IV:

59. Product Customer Brand

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

63. Consumers discussing ideas

64. Consumers helping consumers

67. Rooms to Avoid: 01, 21 08, 17 Rooms Ending in: Possible Ice Machine / Elevator Noise Limited View Rooms Corner / Oversized Rooms: 04 24 Oversized, Corner Room, Quiet Room Oversized, Corner Room with North Times Square Views (Higher Floors are Preferred Rooms Ending in:

68. Group buying… “get a better deal”

69. From e-business… (company focus, Web 1.0) …to me-business (customer focus, Web 2.0) …to we-business (community focus, Web 3.0)

70. Dead data  Live data Collect and analyze  Create, share, experiment Internal  External “Most smart people don’t work here.” Bill Joy

71. Questions Part V:

72. Do you have any advice on how we can be authentic in the era of Social Data? For companies with limited resources, what are the costs of some of the suggestions you mentioned in the talk?

73. What is the most important ingredient for a successful innovation strategy? Do you have any specific suggestion for traditional companies: how can we learn more about the culture change of the Social Data Revolution?

74. @aweigend Andreas Weigend | www.weigend.com Thank you!