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Customer Centricity: The Art of Social Data

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Cleverbridge, Boston, MA, USA (October 12-13, 2011), Download the Presentation? Please pay with a Tweet by following: http://www.paywithatweet.com/pay/?id=f666e658f9f7982aeeadfd40b5935b06

Cleverbridge, Boston, MA, USA (October 12-13, 2011), Download the Presentation? Please pay with a Tweet by following: http://www.paywithatweet.com/pay/?id=f666e658f9f7982aeeadfd40b5935b06

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  • My background:80’s: Traces of elementary particles (CERN)90’s: Traces of traders (30MM t-bond futures trades)00’s: Traces of people on the web (Amazon.com)Now: Bridging physical and digital, social and mobile data
  • Digitizing back office: 100 people
  • 100k poke
  • Connecting Content / Content is king100M
  • CommunitySocial filters
  • Measure, detectorsContext is king
  • Has been talked to death: Social media etc. My perspective: People choose to shareWhat really is this revolution? - again, talk about social data as being qualitatively different. 
  • Means of production
  • Means of production
  • on averageChange behavior: of individualOf citiesWhy do people do this? They get attention? They get belongingTHIS IS VERY DIFFERENT FROM A SOCIAL MEDIA / TWITTER CAMPAIGN
  • What is this? This is a scale. This is my scaleIt is connected to the internet.
  • twitter.com/aweigendmetricsSurprised, influences my behavior. I used to step on a scale once a yearChanges behaviorFor myself / trackingFor others (peer groups)Harvard TED talkBook: LinkedOther examples:
  • Lightweight E.g., QR code -- classhttp://www.youtube.com/watch?v=EHlN21ebeakPhone = Proxy for the personCf partner?How do you feel when you leave home without mobile?Playground
  • Ok, all kinds of stuff. Now who does it?Statistics: no more need for sampling.Amazon observes every customer. People can’t hide!“CDC”Consequences.Study of consumer has changedConsumer has changed
  • BrainwashingPeople share.ConfusionCollaborationCommunity
  • Application to business:
  • Data source
  • based on experience, intuition, and data Picture of graph from FosterLeveraging the social graph
  • based on experience, intuition, and data Picture of graph from FosterLeveraging the social graph“Birds of a feather shop together”Hill, Provost, & Volinsky, Network-based Marketing. Statistical Science 21 256–276 (2006)
  • DC: Redo, as discussed in ER
  • forCY: makes this table or individual arrowsAlso of plz adjust title to what we had in the outline
  • MerchandisingDefine viral marketing10 years agoAmazon vs FacebookAmazon is about products, interactions with store, not with friends. No NewsFeed.Facebook is about interactions between friends.
  • Information the purpose for communication
  • based on experience, intuition, and data Transient: (anonymous) Computes scores across social networks in the US
  • forCY: makes this table or individual arrowsAlso of plz adjust title to what we had in the outline
  • Reduce barriers for contribution (e.g., light-weight annotation)Design incentives that work (clear value to contributor)
  • Point is to show specifics
  • companies thought they own their customer  switching costs largely gonecompanies thought they own product -> who knows more about my phone: manufacturer? Carrier? … or the web?companies thought they own brand  co-creationcustomers talk about whatever they want to talk about CONVERSATION Ultimately, all about data. Social data.
  • Mass Production / Industrial Age
  • Successful social systems are engineered for individuals to express themselves and interact effortlessly.Community or connection
  • Accenture: Get it right the first time
  • emphasize how different this is from the past
  • emphasize how different this is from the past
  • emphasize how different this is from the past This is the new “mission, vision, values”
  • My background:80’s: Traces of elementary particles (CERN)90’s: Traces of traders (30MM t-bond futures trades)00’s: Traces of people on the web (Amazon.com)Now: Bridging physical and digital, social and mobile data
  • Transcript

    • 1. Customer Centricity:The Art of Social Datacleverbridge Networking Event, Boston12 October 2011 Andreas Weigend @aweigend www.weigend.com
    • 2. Building 1970’sComputers
    • 3. Connecting 1980’sComputers
    • 4. Connecting 1990’sPages
    • 5. Connecting 2000’sPeople
    • 6. Connecting 2010’sSensors
    • 7. Social vs Transactional Data vs Social MediaRevolution vs Evolution
    • 8. “Social Data is the New Oil”UN General Assembly, 8 November 2011
    • 9. Irreversible Shiftin Customer Mindset
    • 10. Nike+Customers- engage- connect- share 3 times per week on average
    • 11. Smartphones and Sensors Context, situation • Sound • Light Customers interact • Tag • Scan
    • 12. Location: Google Latitude
    • 13. Location Absolute: Place, time • Individual: Identity, History • Aggregate: Insights Relative: Distance • To places: Advertising • Between people: Dating • Between devices: Risk
    • 14. Create Distribute Consume Everybody
    • 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. Social Data Growth is EXPONENTIAL This year people will generate more data than mankind has from its beginning through last year
    • 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. The ABCAttention AdvertisingBelonging BrandingCollaboration Confusion
    • 19. Twitter -- The Illusion of an Audience World Innovation Forum, NYC, June 2010Two Monologues don’t make a Dialogue GDI, Zurich, January 2010
    • 20. Fundamental Shift in Communication One-way  Two-wayAsynchronous  Synchronous Planning  Interaction List  Flow Private  Public
    • 21. Data Strategy Attention Situation • Clicks, Transactions • Geo-location • Device Intention • Search User generated Connection • Reviews • Social graph
    • 22. Case study: What data for targeting of a new phone product?Traditional segmentation Connection data• Demographics • Who called who?• Loyalty
    • 23. 1.35% Adoption rate 4.8x 0.28%Traditional Connectionsegmentation data
    • 24. CompanyCustomers
    • 25. Amazon.com Share the Love
    • 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. Purpose of communication:to transmit information? Or is information just an excuse for communication?
    • 28. 1993“On the Internet, nobody knows you’re a dog”
    • 29. 2011“On the Internet, everybody knows you’re a dog”
    • 30. Social network intelligenceSocial graph targeting: Provide prospectsFraud reduction: Provide risk scores
    • 31. Audience C2B: Customer to Business C2C: Customer to Customers C2W: Customer to World
    • 32. Amazon.com: Public sharing of interests
    • 33. Who Can You Get To Work For You? 100M Customers 100k Employees 100 Specialists
    • 34. Marketer-generated Consumer-generatedaware share consider opinion buy use Funnel Megaphone
    • 35. Get Your Customers To Work For You! • Reduce barriers for contribution • Design incentives that work
    • 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. Customer Product Brand
    • 38. From controlled production for the masses… … to uncontrolled production by the masses
    • 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. 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. 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. 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. 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. Thank you!Customer Centricity: The Art of Social Datacleverbridge Networking Event, Boston12 October 2011 Andreas Weigend @aweigend www.weigend.com