The document discusses how customer experiences have evolved from transactional to experiential as digital technologies have created more interactivity between customers and organizations, allowing customers to take on new roles beyond just buyers. It outlines how social capabilities are expanding across business processes and how social data is becoming integrated into decision making. Finally, it shows how social media has progressed from a basic presence for brand awareness to being used for more advanced functions like sales and service through collaboration excellence.

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7. A Customer Experience Map

8. The “Buyer’s” Journey
The Buyer’s Journey is just the first leg of the
Digital Customer Journey…
Research Purchase Re-
Discover purchase
Trial
Use
Buyer’s Journey...
There is more to Digital Customer Experience

9. Customers Are More Than Buyers…
Traditional Customer Digital Customer
Transactional Experiential
Target Interactive
Receiver Buyer Engaged
Advocate Iterative
Reseller
Influencer
Innovator
Peer Advisor
Solution Integrator
Solution Developer
Digital creates transparency and
“Employee”
interactivity between customers Competition
and organizations, allowing
customers to play new roles

10. Customer Experience in Digital Enterprises
Discover Ideate
Research Design
SOCIAL
Trial Develop
Purchase Test
Use Package
Go to Products
Recommend Market and Services Bundle
MOBILE DIGITAL DISRUPTERS CLOUD
Source Value Culture and Crowdsource
Ecosystem Organization
Sell/Resell Join
BIG
Support Alert
DATA
Service Rate
Compete Advise

11. • Communication
Systems of Engagement
• Collaboration
Social capabilities expand across business processes
• Coordination
Social data becomes an integral part of decision processes
Social integration with other digital assets
Social media functional expansion (e.g., sales, service)
Social media footprint expansion (i.e. new social media channels)
Basic social media presence for brand awareness
The need for collaboration excellence drives the evolution of social
from consumer phenomena to relationship platform

12. Yesterday’s Today’s Tomorrow’s
Customer Customer Customer

13. Dr. Kalyan Raman
Chief Technology Officer,
The Nielsen Company
• Joined The Nielsen Company in 2008
• Previously leading Practice Leader with
TCS.
• Responsible for enabling Nielsen to
successfully compete in technology
relevant to Nielsen's business and
customers.
• Led the development of Nielsen¹s
Product / Technology Roadmap, a true
market differentiator.

14. Professor V S Subrahmanian
Department of Computer Science, Director of
the Institute for Advanced Computer Studies,
University of Maryland
• Research in databases and artificial intelligence.
• AI work spans rule-based expert systems and
logic programs, nonmonotonic
reasoning, probabilistic reasoning, temporal
reasoning, hybrid reasoning, and software
agents.
• Applying to study of foreign cultures and
terrorist groups–building stochastic behavioral
models.
• Several scalable systems in use by government
and industry.

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18. http://socialnetworkingeducation.wikia.com/
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wiki/Social_Networking_in_Education_Wiki

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28. user363 0.006897
user1182 0.006849
user543 0.006623
user1923 0.006623
user210 0.00641
user1846 0.006369
user173 0.006329
user314 0.006289
user832 0.005988
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32. user18 4.690000087
user337 4.340000093
user131 4.28000012
user25 4.200
user33 4.092000142
user1072 4.062000155
user1203 3.892000139
user3337 3.732000172
user1004 3.58400014
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r
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43. teen
poor
old
average
rich adult
young adult
Colleague
Friend
Child
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44. Likes_P(X) & child(X,Y) & teen(Y) --> likes_P(Y) | 0.95
Likes_P(X) & child(X,Y) & youngadult(Y) --> Likes_P(Y) | 0.35
Likes_P(X) & friend(Y,X) & youngadult(Y) --> Likes_P(Y) | 0.2
Likes_P(X) & friend(Y,X) & old(Y) --> Likes_P(Y) | 0.6
Likes_P(X) & friend(Y,X) & adult(Y) --> Likes_P(Y) | 0.4
Likes_P(X) & colleague(Y,X) & rich(Y) --> Likes_P(Y) | 0.5
Likes_P(X) & colleague(Y,X) & poor(Y) --> Likes_P(Y) | 0.2
Likes_P(X) & colleague(Y,X) & average(Y) --> Likes_P(Y) | 0.3
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a
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54. a specifies level of influence of a candidate on voters.
Higher a => more influence. Tested on graphs with upto 8M
edges.
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