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Customer Centricity: The Art of Social Data

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Customer Centricity: The Art of Social Data

  1. 1. Customer Centricity:The Art of Social Datacleverbridge Networking Event, Boston12 October 2011 Andreas Weigend @aweigend www.weigend.com
  2. 2. Building 1970’sComputers
  3. 3. Connecting 1980’sComputers
  4. 4. Connecting 1990’sPages
  5. 5. Connecting 2000’sPeople
  6. 6. Connecting 2010’sSensors
  7. 7. Social vs Transactional Data vs Social MediaRevolution vs Evolution
  8. 8. “Social Data is the New Oil”UN General Assembly, 8 November 2011
  9. 9. Irreversible Shiftin Customer Mindset
  10. 10. Nike+Customers- engage- connect- share 3 times per week on average
  11. 11. Smartphones and Sensors Context, situation • Sound • Light Customers interact • Tag • Scan
  12. 12. Location: Google Latitude
  13. 13. Location Absolute: Place, time • Individual: Identity, History • Aggregate: Insights Relative: Distance • To places: Advertising • Between people: Dating • Between devices: Risk
  14. 14. Create Distribute Consume Everybody
  15. 15. Social Data Growth is EXPONENTIAL The amount of data a person creates doubles every 1.5 years • after five years  x 10 • after ten years  x 100
  16. 16. Social Data Growth is EXPONENTIAL This year people will generate more data than mankind has from its beginning through last year
  17. 17. Economics of Social Data* With social data being abundant, attentionhas become the scarcest resource.* To get attention, people will do anything,including socialize their data
  18. 18. The ABCAttention AdvertisingBelonging BrandingCollaboration Confusion
  19. 19. Twitter -- The Illusion of an Audience World Innovation Forum, NYC, June 2010Two Monologues don’t make a Dialogue GDI, Zurich, January 2010
  20. 20. Fundamental Shift in Communication One-way  Two-wayAsynchronous  Synchronous Planning  Interaction List  Flow Private  Public
  21. 21. Data Strategy Attention Situation • Clicks, Transactions • Geo-location • Device Intention • Search User generated Connection • Reviews • Social graph
  22. 22. Case study: What data for targeting of a new phone product?Traditional segmentation Connection data• Demographics • Who called who?• Loyalty
  23. 23. 1.35% Adoption rate 4.8x 0.28%Traditional Connectionsegmentation data
  24. 24. CompanyCustomers
  25. 25. Amazon.com Share the Love
  26. 26. 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)
  27. 27. Purpose of communication:to transmit information? Or is information just an excuse for communication?
  28. 28. 1993“On the Internet, nobody knows you’re a dog”
  29. 29. 2011“On the Internet, everybody knows you’re a dog”
  30. 30. Social network intelligenceSocial graph targeting: Provide prospectsFraud reduction: Provide risk scores
  31. 31. Audience C2B: Customer to Business C2C: Customer to Customers C2W: Customer to World
  32. 32. Amazon.com: Public sharing of interests
  33. 33. Who Can You Get To Work For You? 100M Customers 100k Employees 100 Specialists
  34. 34. Marketer-generated Consumer-generatedaware share consider opinion buy use Funnel Megaphone
  35. 35. Get Your Customers To Work For You! • Reduce barriers for contribution • Design incentives that work
  36. 36. Corner / Oversized Rooms: Rooms Ending in: Oversized, Corner Room, Quiet 04 Room Oversized, Corner Room with North 24 Times Square Views (Higher Floors are PreferredRooms to Avoid:Rooms Ending in: 01, 21 Possible Ice Machine / Elevator Noise 08, 17 Limited View Rooms
  37. 37. Customer Product Brand
  38. 38. From controlled production for the masses… … to uncontrolled production by the masses
  39. 39. E  Me  We-commerceWho talks to whom? Consumers to consumersWho trusts whom? Shift from institutions to individualsWho is in control? From e-commerce (company focus, Web 1.0) to me-commerce (customer focus, Web 2.0) to we-commerce (relationship focus, Web 3.0)
  40. 40. Innovation• Innovation Internal  External “Most smart people don’t work here.” Bill Joy• Data Collect and analyze  Create and share• Experiments Push and pray  Launch and learn
  41. 41. Data Culture Do not have  Somewhere already Cannot get  Can! User will give Must not use  Embrace it Be secretive  Be transparent Information  Information symmetry asymmetry
  42. 42. Product Culture Help people make better decisions Make it trivially easy for people to contribute Give people an excuse to connect Note: Products and services that use social data improve over time
  43. 43. Company Culture Write the equations of your business in terms of customer centric metrics e.g., View-weighted availability Capture the space created by the disappearing constraints of the past e.g., Communication costs
  44. 44. Thank you!Customer Centricity: The Art of Social Datacleverbridge Networking Event, Boston12 October 2011 Andreas Weigend @aweigend www.weigend.com

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