The document discusses the future of customer care and different models for providing customer support. It analyzes machine, human, individual and community-based approaches as well as self-service, smart care, customer support and communal support. Specific technologies and trends are also examined, including predictive assistants, artificial agents, video vaults, little data, network narratives, mobile communities, gamification and more. The future of customer care is seen as relying more on new technologies but also understanding customers at an individual level.

1. Smart Care
What is the future of customer care?

2. Receiving great
customer service
triggers the same
cerebral reactions as
feeling loved
Based
on
American
Express
study
in
2013

3. Types of Care
What are customers trying to do?

4. Types of Care
Experience
Management
Finding
Information
Requests of
Change
How do I manage my
Service's experience?
How do I know more
About something ?
Who can help me in this
Task that I cannot do
By myself?

5. Lost Customers?
Why customers cannot do, what they want to do?

6. Because of issues in:
Usability / Complexity
Relevancy / Understanding
Resolving / Frustration
They
will
Call your call canter!
OR Visiting your business

7. Solutions to current problems should invert the current situation
Usability / Complexity
Relevancy / Understanding
Resolving / Frustration
Happy Customers
Simplicity
Relevancy
WhichSolving
Will
Result
in

8. Re-Thinking Care
A Framework for Understanding Customer Care

9. Machine
Human
Individual
Community
Communal Support Provided
Self Service Smart Care
Customer Support
For
Provided
by

10. Customer Support
Human Interactions
Professional Support
Keeping the Brand Promise
Authorized Access to Private Information
Standardization
High Operational Costs
Economics Of Scale

11. Self Service
Economics Of Scale
24x7 Access
Multi-Language Support
Development Costs / Management Costs
Personalization
Speed
Machine Based Conversations

12. Communal Support
Diversity / Any Language Support
Socialization
Ability to Widen Topics Beyond Support
Applied Know How By The People
Keeping the Brand Promise
Variable Quality
Ability to Scale
Standardization
Privacy Concerns Access to Private Data

13. Smart Care
Intelligence
Predictability / Personalization
Development Efforts / Costs
Complexity
Data Availability
Finding the Right Partners

14. Type of Care Provided
Machine
Human
Individual
Community
Communal Support Provided
Self Service Smart Care
Experience
Management
Finding
Information
Customer Support
For
Provided
by
Requests of
Change
Experience
Management
Finding
Information
Requests of
Change
Experience
Optimization
Experience
Management
Predictive
Information
Self
Healing
Finding
Information
Requests of
Change

15. Questions to be Answered
Machine
Human
Individual
Community
Self Service Smart Care
Who are you?What we can learn more about you?
What do you want to do?Did you find the solution helpful?
What do you want to do?
How about this ….?
Customer Support
Communal Support For
By
Who are you?
What is your problem?
What is your problem?
Who can help in this? Who is expert?
Is this really you?
How about this ….?
What do you want to know?
What do you want to know?
Did you find the solution helpful?
Did you find the solution helpful?

16. Machine
Human
Individual
Community
Self Service Smart Care
Customer Support
Communal Support For
By
Customer Care Agents
Employees
Customers
Non-Customers
Humans in the Equation

17. Machine
Human
Individual
Community
Self Service Smart Care
Electronic
Channels (Web, IVR, USSD)
Predictive Assistants
(CORTANA)
Artificial Agents
(Web, Voice, Hologram)
Call Center
Offline Customer
Representative
Online Agents
(Text, Video, Audio)
Social Media
Agents
Sensing
Networks
Social
Sentiment
Big Data
Personal
Census
Mobile
Communities
Timeline
Narratives
Network
Narratives
Web
Communities (Blogs, Forums)
Native
Conversations
Customer Support
Predictive
Hints
Collaborative
Wisdom
Communal Support Little Data
Video Vaults
Gamification
Trends
Provided
For
Provided
by

18. Machine
Human
Individual
Community
Self Service WEB. 3.0
Smart Care
Artificial Intelligence
Electronic
Channels (Web, IVR, USSD)
Predictive Assistants
(CORTANA)
Artificial Agents
(Web, Voice, Hologram)
Call Center
Offline Customer
Representative
Online Agents
(Text, Video, Audio)
Social Media
Agents
Sensing
Networks
Social
Sentiment
Big Data
M2M
Artificial Intelligence
Personal
Census
Mobile
Communities
Timeline
Narratives
Network
Narratives
Web
Communities (Blogs, Forums)
Native
Conversations
Customer Support
Predictive
Hints
Collaborative
Wisdom
Communal Support Little Data
For
By
BIG DATA
Advance UI
Quantified Self
Contact Center Tech
WEB.2.0
Reward / Incentive Platforms
Social Media
Multi-media Conferencing
Technologies in play

19. Emerging Trends
Seeing the Future in Customer Care, Today

20. But its not about the technology … !!
The future of
customer service is
more than just
drone delivery!
Trendswatching,
The
Future
of
Customer
Service
Report

21. Machine
Human
Individual
Community
Self Service Smart Care
Electronic
Channels (Web, IVR, USSD)
Predictive Assistants
(CORTANA)
Artificial Agents
(Web, Voice, Hologram)
Call Center
Offline Customer
Representative
Online Agents
(Text, Video, Audio)
Social Media
Agents
Sensing
Networks
Social
Sentiment
Big Data
Personal
Census
Mobile
Communities
Timeline
Narratives
Network
Narratives
Web
Communities (Blogs, Forums)
Native
Conversations
Customer Support
Predictive
Hints
Collaborative
Wisdom
Communal Support Little Data
For
By
Video Vaults
Gamification

22. Customer
Support
1
Call Center
Offline Customer
Representative
Online Agents
(Text, Video, Audio)
Video Vaults
Social Media
Agents

23. VIDEO VAULTS
Consumers worldwide have had enough of
ineffective virtual 'assistance' by web or
phone.
With new tech existing like real time video
chatting and conferencing, a new forward-thinking
of providing webcam-enabled face-to-
face interaction with their customer
service representatives, as and when
consumers need it.

24. Self Service
2
Electronic
Channels (Web, IVR, USSD)
Smart Assistants
(CORTONA)
Network
Narratives
Little Data
Artificial Agents
(Web, Voice, Hologram)

25. LITTLE DATA
Little data is small information bits that can
be used to customize and personalize the
customer services.
In contrast to big data, which is normally
provide indications for collective group
trends. Little data is information that can
indicate insights on an individual level, which
can be used for personalization and
customization.

26. NETWORK NARRATIVES
Having network elements communicate
with the crowd, so they can better optimize
their usage.
Data like number of subscribes attached to a
certain cell and the utilization of a cell can be
shared. This will create a sense of
transparency to customers and provide
credibility.

27. SMART ASSISTANTS
With in the mobile phone, smart assistants
that understand the context and the services.
These assistants with time can predict what
action we are willing to take and provide
us with proactive answers to customer
needs.

28. Communal
Support
3
Mobile
Communities
Web
Communities (Blogs, Forums)
Collaborative
Wisdom
Gamification

29. MOBILE COMMUNITIES
Building provider-to-customer virtual
communities using mobile phone technology.
The provider role is to facilitate the
conversations between the community.
Building on the current high penetration of
smartphones and also the growth of data
plans which even make it easier for
customers to adapt, and find resolutions to
there issues on the go.

30. COLLABORATIVE WISDOM
Tapping the into the wisdom of the crowd and
giving the chance for lead customers to help
other customers where lead customers
acting as brand customer care extension.
This will also build on the mobile technology
as means for customers to support customers
using native interfaces like voice, image and
also text.

31. GAMIFICATION
Building on the strength of games in
engagement, gamifiaciton is applying game
thinking, design and tools in non-game
contexts for the sake of achieving certain
business objectives.
Gamifaciton can be thought as means to
increase the motivation with in its target
audience, which can be used as way to
incentivize customers in crowdsourcing
activities.

32. Smart Care
4
Sensing
Networks
Social
Sentiment
Big Data
Personal
Census
Timeline
Narratives
Predictive
Hints
Native
Conversations

33. NATIVE CONVERSATIONS
Removing the language barriers for
customer service by utilizing the digital
translation services to provide native
conversations.
These conversations can be between
provider-to-customer and customer-to-customer,
where the translation is just a way
to make the conversation more native.

34. PREDICTIVE HINTS
Understanding what customer wants by
monitoring both little and big data and
then proactively giving them advices about
how they can preform / manage their services
better based on on their behaviors.
Its all about taking the extra mile and moving
from responsive design to proactive design
when it comes to customer service.

35. SENSING NETWORKS
Today, we are equipped with an increasing
number of technologies that are equipped
with sensors that collect data passively.
These sensors gives us the possibility to
collect data about customer and their
contexts, without altering their daily
routine.
The aim is to generating information and
insights that can helps take more care of
customers.

36. PERSONAL CENSUS
Individuals using mobile technology to
share in a participatory manner about their
moods, service contexts, prospections about
services. etc.
These collected data are aggregated in a
collective manner to give insights about the
crowd attitudes and behaviors towered us.
Which can help in better understanding
where to focus care efforts in the future.

37. SOCIAL SENTIMENT
With the amount of data individuals share in
social media, data can be collected and
analyzed to understand the sentiment of
the crowd.
This can be used to gather insights that are
relevant to the brand or for competitors,
which can lead to actions.
Actions can vary from remedy of problems to
new market opportunities.

38. TIMELINE NARRATIVES
Understanding how events have un-folded is a
key for to formulate responses, especially
when situations are complex.
Analyzing conversations with customers in
support communities and social media a
timely manner can provide valuable insights
into how and when things occurred.

39. BIG DATA
Big data is all about the collection of huge
amount of data from both structured and un-structured
sources, for the sake of analyzing
it and finding collective patterns and
trends in it.
It can be used to help the decision making
process by given insights from old data that
have not bee noticed before.

40. LIKE IT?
WE CAN HELP YOU :)
CONTACT US: hello@sa3dni.net
www.sa3dni.net