How organizations can leverage the capabilities of cognitive analytics to deliver superior service innovation for their clients.

1. Great
Expectations
Cognitive Analytics for the
Service Economy
Connected
www.freedigitalphotos.net
Photo by patrisyu

2. 2
Content
• The Industrial Economy
• Disruptions to the Industrial Economy
• Services as a Key Differentiator – A Smartphone Perspective
• Service Transformation Challenges for Electronic Companies
• Why Cognitive Analytics
• Some Use Cases of Cognitive Analytics
• Summary and Conclusion

3. 3
What was the Industrial Economy

4. 4
The Industrial Economy
► Part Standardization
► Assembly Modularity
► Vertical integration
Early Industrialization was built on the concept of building scale with
minimal differentiation
“You can have any color, so long
as it is black”
- Henry Ford, on the T-Model
Source: The Software Defined Supply Chain, IBM WP

5. 5
Industrie 4.0 – What’s Changing?

6. 6
The Four Transformation waves in the Industrial Economy
Source: http://www.engineersjournal.ie/wp-content/uploads/2014/05/Domhnall_Carrol-006.jpg
Importance of Scale, Standardization and the Global Supply Chain
becoming less important in Industry 4.0

7. 7
Disruptive Technologies Enabling Industry 4.0
Intelligent Robotics 3D Manufacturing
Low Power Wide
Area Networks
Cognitive Analytics
Cheap, Adaptive Robots
which can work alongside
humans and can be
“taught” tasks without the
need of programming.
Allows for smaller set-up and
operating costs. Disruption in
areas requiring smaller volumes
with uncertain demand
Industrial internet will allow
M2M communication and
control
Advanced analytics and
machine learning algorithms
which can detect patterns from
structured and unstructured
sources
Open Source H/W
and S/W
Maturing of Open Source
S/W and H/W platforms
means low licensing and
development costs for
products
Disruptions are lowering the entry
barrier into product manufacturing
which will erode product
differentiation in the long run

8. 8
How is manufacturing responding
to Industrie 4.0?

9. 9
Service and Customer Innovation are key focus areas for
global manufacturing
• By 2017, 70% of global manufacturers will
offer connected products
• By 2016, only 20% of manufacturers will
have an integrated approach to
delivering service
• By 2017, 75% of leading manufacturers be
doubling the investments in customer-
facing technology
Note: The size of the bubble indicates complexity/cost to address
Source: IDC, 2014
6 of the top 10 imperatives are around
service and customer innovation which
reiterates the importance in developing
differentiated capabilities in those areas

10. 10
How are the new companies
differentiating themselves?

11. 11
Disruption Through Service – Xiaomi Case Study
• Xiaomi is a Chinese Mobile Company founded in 2010
• Started out as a software company with a UI, MIUI based on the
Android OS
• Has grown its sales volumes from 7.2 mil in 2012 to over 30* mil in 2014
• Apart from phones its product portfolio includes*:
– an online digital content store, which includes apps, games, wallpapers and
themes, and ebooks and is one of the top android stores
– applications and services such as MiTalk (an IM service) and MiDrive (cloud
storage)
– other hardware such as the MiBox (a smart set-top box for the TV), the MI Wi-
Fi (Wi-Fi router), MI Charger (portable phone charger) and the MiTV (a 47-
inch smart TV)
– accessories such as earphones, screen guards and a USB cable
*http://www.analysysmason.com/Research/Content/Comments/Xiaomi-smartphones-Feb2014-RDMD0-RDRP0/

12. 12
Price Disruption: Comparison of Mi Phones with Competitors
Models Mi 4 Samsung Galaxy S5 Apple iPhone 6
Display 5.0-inch full HD (1920x1080p) 5.1-inch Super AMOLED
display with full HD
4.7-inch LED-backlit IPS
screen with Retina HD
OS Android v4.4.3 KitKat with MIUI v6.0 Android v4.4.2 KitKat OS iOS v8.0
Chipset 2.5GHz Qualcomm Snapdragon
801 series quad-core CPU
2.5GHz Qualcomm
Snapdragon 801 series
quad-core CPU
1.4GHz Dual-core Cyclone
CPU
GPU Adreno 330 Adreno 330 PowerVR GX6450 (quad-
core graphics)
RAM 3GB LP-DDR3 993MHz 2GB DDR3 1GB
Storage Capacity 16GB [Not expandable] 16GB, expandable via
micro SD card
16GB/64GB/128GB[Not
expandable]
Camera Main: 13- megapixel
Front: 8-megapixel
Main: 16-megapixel
Front: 2.1-megapixel
Main: 8-megapixel
Front: 1.2-megapixel
Battery 3080 mAh (Li-Ion Polymer) 2800 mAh battery Talk time: up to 14 hours
(3G)
Network 3G LTE Cat.4 (150/50Mbps) 3G & 4G-LTE
Price* $ 330 ~$850 $891 -$1191
*All prices in India at time of launch
Pricing of Xiaomi is less then half of the competition but with similar
specs
Source: http://www.ibtimes.co.in/xiaomi-mi4-vs-samsung-galaxy-s5-vs-apple-iphone-6-specification-comparison-622035

13. 13
Capacity and Channel Disruption: Sell small batches online
and through pre-booking
Flipkart India page screen shot
Xiaomi Mi4 Flipkart Flash Sale to Go Live on 03 March
Xiaomi India Facebook page screen shot
http://www.bgr.in/news/xiaomi-mi-4-goes-out-of-stock-in-15-seconds-
next-sale-on-february-17/
Sell in small batches of 30,000 and 100,000 through pre-booking process
and no formal advertisement or promotion flash sales

14. 14
Customer Engagement Disruption – Deeper Engagement
0
5
10
15
20
25
30
MIUI Apple/Google
Average Downloads per Quarter per device (in mil)
Xiaomi users download apps at almost twice
the rate of other Android users and iOS users
http://www.tech-thoughts.net/2013/09/xiaomi-international-
growth-data-usage-carriers.html#.VYuQ7WMpnW5
Extremely active set of users who regularly
take part in forums, blogs etc and even
compete on popularity
“Fans” can vote on features and specs they
want
MIUI is frequently updates ( almost weekly)
and available for upgrade

15. 15
What is different about Xiaomi?
Xiaomi Apple
Catering to lower end of the emerging markets Catering to a higher end of the markets in both
developed and emerging economies
Competes through high specs at low prices Competes on product differentiation
Very small production capacity relies on small
batches sold through limited online vendors
Vertically integrated across product design, OS
and supply chain with large vendor capacity
Software product upgrade releases almost every
week
Releases upgrades once or twice a year
Sells online and in small batches through pre-
registration
Sells across multiple channels including own retail
chain
Makes revenue from cross-selling and up-selling of
content
Makes about 9% revenue from content. Primarily
revenue through hardware
Releases a new product every 16 months on
average with few very few models
Releases a new model of the product every year
with multiple product-lines and SKU’s
“ People just don’t get it. The
mobile phone itself is only the
carrier” – Lei Jun, Xiaomi
Xiaomi looks at “hardware as a
distribution” device rather than as
a product in itself while Apple aims
at product differentiation primarily

16. 16
Takeaways from the Discussion
• Service can be source of competitive advantage
• Hardware getting more commoditized with differentiation coming from
the software layer
• Brands are built not by an advertisement (telling) but through customer
experience which comes as being part of the tribe
• Micro segments are what matters, not the average

17. 17
Why Service Differentiation in
Electronics?

18. 18
Consumers are demanding considerable improvement in service
quality
Source: Consumer Survey (2010) by IBM Institute for Business Value (IBV)
Service quality satisfaction level
Scope for
improvement
44%
46%
52%
53%
54%
56%
56%
58%
0% 50% 100%
Interaction with device manufacturers
Interaction with service provider
Device interoperability
Account management
Usage support
Provision of user information
Stable provision of services
Number of services
A large opportunity for service improvement also points to
significant opportunity for newcomers to gain market share

19. 19
Electronics companies are largely interested in pursuing services, to
drive differentiated offerings
Companies are driven by
defensive strategic rationales to
raise a bar for the competitors by
adding more values on H/W,
acting upon customers’ demand
Financial benefits such as
additional revenue, high margin
and stable cash flow are not clear
yet, as most of companies are at
the early stage
Why pursuing service?
“It is getting harder to maintain the leadership
position in the industry if you stick to products only.
We need to reconfigure the H/W to meet customers’
needs and provide solutions and services that
customers want to enhance their experience around
the products.” Consumer electronics, AsiaSource: Industry Interview (N= 32) by IBM Institute for Business Value (IBV)
To achieve
competitiveness
by differentiating
offerings
To deepen
customer
relationship
To increase
perceived value in
consumer’s minds
To meet consumer
needs & demands
To create new
revenue streams
88%
38%
53%
66%
25%

20. 20
Principles of engagement in the connection economy
“ We are moving from the industrial economy
to the connection economy”
- Seth Godin, Marketing Guru
1 Customers have a choice not to engage, -give them a reason to
why they should
2 Mass market is no longer relevant –it’s the edges where the action is
3 The only work that matters is the work that matters – don’t ask if you will
succeed, ask if you will matter to the customer
https://www.youtube.com/watch?v=sKXZgTzEyWY

21. 21
Why is Service Differentiation
difficult for product companies?

22. 22
3 Challenges for Generating a Differentiated Service
1 Capability Issue
Fundamental difference
between product and service
means new capabilities
required
2 Ubiquity of Data
Data explosion means that it is
a question of being able to
detect the signal from the noise
3 Customer Expectation
Customer expects value which
is clearly defined, differentiated
and personalized

23. 23
1.The fundamental differences between manufacturing and
service can create difficulties in delivering experiences
Manufacturing vs. Service
Intimacy with channels
(e.g. on & offline retailers, N/W
operators)
One-time transaction
H/W manufacturing and delivery are
separable – Consumption happens
after the production
Activities are relatively linear and
easily definable in modules
Source: IBV analysis
Intimacy with end-users & Strong
incumbent positions
On-going relation
Service production and delivery are
inseparable - Consumption happens
at the very same moment as
production, making the production
and delivery of equal importance
Activities are complex with multi-
functional traits
Need to recognize the difference and create new approaches
“Service requires creativity, flexibility and more focus on individual case,
whereas manufacturing is straightforward and very R&D-focused.”
CEO,Consumer electronics, China

24. 24
As companies will be able to interact directly with customers
and have visibility into customer experiences…
Set Up Use Get Help
Upgrade/
Replace
Product
Research
Purchase
PRE-SALE POST-SALE
Basic user information
Usage issues
Product
information
Basic user
information
Purchase history
Usage data
(e.g. purchase,
location,
usage duration,
frequency)
Installation
information
Billing
information
Usage
issues
Product
upgrade
data
Product information
(Registration) - optional
Product
only
Product
+
Service
More types of data on users and their usage
More interaction points & depth
No time lag in collecting data
Source: IBV analysis
SALE

25. 25
2.There is an explosion in data in all forms and all sources
• Data will grow to 40 zeta bytes by 2020, a 50
fold increase from 2010
• 80% of this data will be unstructured
• 30B pieces of content are shared everyday on
Facebook
• Over 181 mil public blogs in 2011 compared to
35 mil in 2006
http://dazeinfo.com/2012/03/10/number-of-blogs-up-from-35-million-in-
2006-to-181-million-by-the-end-of-2011/https://blog.kissmetrics.com/facebook-statistics/
• 40B of connected devices by 2020
• 99% of devices which will be connected
someday are yet unconnected
http://www.forbes.com/sites/quora/2013/01/07/how-many-things-are-
currently-connected-to-the-internet-of-things-iot/

26. 26
3.Engagement Decisions are getting more complex
Mass Media
Web
Mobile
Social
Store
Purchase
► Explosion in touch points
► Information is dynamic
► Information is unstructured
► Information is “contextual”
Weather Patterns Economic Patterns Demographic Patterns
External Factors
A seller needs to understand both the internal and external context of a
customer at a transactional level

27. 27
Key Takeaways from the Discussion
• There is an urgent need for building service capabilities
• Customer data is voluminous and fragmented across service logs,
blogs, social media
• Systems have to put in place which is able to integrate data across
these channels and extract meaningful insights from them
• Key to success is to be able to use these insights in providing customers
with personalized services in a form which is most convenient to the
customer

28. What is Cognitive Computing?

29. 29
A simplified view of Cognitive Computing
Big Data – Provide
Nuggets of information
where humans need to
“connect the dots”
Cognitive Computing–
Era where machines
will begin “connecting
the dots”

30. 30
Gaps in current Computing Methodologies
Supervised Learning
• Current computing can only
predict/prescribe what it has
been taught
• Is severely restrictive in most
situations requiring simplifying
rules
Structured Data
• Focused on structured data which is
just 20% of the data available in
organizations
• Cannot handle unstructured data
without heavy simplifications
Machine ‘like’ Intelligence
• Human intelligence requires the
thinking and understanding of
process and problems in a certain
way which machines today can’t
[cognitive dissonance]

31. 31
Cognitive Computing is responding to these challenges
http://blog.stephenwolfram.com/2011/01/jeopardy-ibm-and-
wolframalpha/
Goal is the same:
• How do you minimize
“cognitive dissonance”?
i.e how do you develop a
system which is able to
understand a problem in
the same way as humans
Cog.nih.tiv (n). Having the property of
reasoning, deduction, induction, and an
awareness of immediate as well as latent
cause and effect

32. 32
High Level Watson Architecture*
1. Query or topic is posed to
Watson
2. Watson parses the query
3. Multiple hypothesis are
generated based on 2
4. On each of the hypothesis
competing models are run
5. Results are synthesized
using advanced scoring
methodology
6. Final ranking to possible
answers along with
confidence levels are
provided
11 22
33 44
55 66
Watson Relies on:
• Evidence and prior results (answers)
on the topic/industry which has been
fed to it aforehand
• DeepQA architecture which enables
massive parallelization of hypothesis
testing and scoring from the content
Source: http://researcher.watson.ibm.com/researcher/view_group_subpage.php?id=2159
High Level Architecture of DeepQA

33. Why is Cognitive Computing
critical to Service Differentiation?

34. 34
Friends’ social posts are more influential in determining purchasing
choices than direct retailer communications
Source: IBM IBV 2013 survey, n= 30,554. Q40 decision influencers
Influencers of purchase behavior
59%
57%
52%
51%
44%
41%
Friends post/pins about items purchased
Friends post/pins about retailers shopped
Retailers emailed sales announcements
Retailers sales announcements via social
Retailers recommendations on own website
Retailers recommendation on social sites
Chart shows percent of shoppers finding the communication “somewhat” or
“very influential'
Data is available but fragmented. Suppliers need capability to intervene
with relevant information at the right time on the right channel

35. 35
Half of consumers are neutral about SoLoMo* initiatives and are still
waiting for a reason to engage with retailers
Retailers using current location (GPS)
negative neutral positive
28 49 23
23 51 26
22 50 28
20 50 30
17 52 31
Associates able to pull up my browsing
history
Retailers texting me
Retailers analyzing my posts to recommend
new items
Retailers contacting me via social networks
Source: IBM IBV 2013 survey, n= 30,554. Q31 InfoShare *SoLoMo = Social Location Mobile
Interest in SoLoMo
Providers need to pinpoint customer needs on a real-time basis to be
valued to a customer

36. 36
How is Cognitive Computing relevant to service differentiation?
Biggest barrier to
enriching customer
experience is
fragmented data
outside and within the
organization. Cognitive
Computing would help
by data integration and
providing insights when
and where its required
Business users and
algorithms often speak
in different terms.
Cognitive algorithms
would be able to
understand and help
users design processes
faster and closer to
consumer requirements
Existing models for
analytics would be
able to use attributes
from unstructured data
and that would
improve the model
accuracies

37. Where is Cognitive Computing
being applied today?

38. 38
Branches of Cognitive Computing
Provide expert domain expertise
to human user
Make evidence based contextual
decisions autonomously
Discovers new patterns which is currently
not detectable by humans
Decision
DiscoveryEngagement
22
11 33
Source: Your cognitive future, How next-gen computing changes the way we live and work, IBV WP
Final Goal is to provide computers with the ability to make own decisions
and discover new patterns

39. 39
FutureFutureFutureFuturePresentPresentPresentPresentPastPastPastPast
True Q&A
Systems
e.g. Watson
Engagement
Advisor
e.g. ELIZA
Vertical
Specific True
Q&A Systems
Pseudo Dialogue
Programs
FAQ
Assistants
e.g. NextIT
Alme
Source: IBM, Institute of Business Value, Evolution of cognitive: Where we’ve been and where we’re going
Significant progress has already been made in the “Engagement”
capability

40. 40
Assisting users retrieve insights through Q&A
Watson
Explorer
Explore
Analyze
Interpret
Customer support R&D Marketing & Sales
Corporate
Governance
Security
Secure access to
internal and external
information
360-degree
information
applications
Advanced NLP and
content mining
Cognitive services
Advantages of Explorer over conventional searches
• Contextual search – results dependent on user needs
• Allows search in natural language
• Allows real-time insights into unstructured data feeds
Implementation of Watson Explorer (WEX) Enterprise Edition

41. 41
Allowing for knowledge which is available at users fingertips will
transform the customer experience
Watson Explorer 360-degree information
application
Personality Insights service
provides the user with a
more detailed profile of the
client
Data from enterprise
systems such as CRM,
DBMS, CMS and SCM
Analytics, in context
Activity feed for up-to-the-
moment information
Data-driven alerts
Content analytics to
reveal insights from
unstructured data
Collaboration and
information sharing
Source:Implementation of Watson Explorer (WEX) Enterprise Edition

42. 42
Enrich existing Advanced Analytics Models by allowing
scientists to tap into real-time unstructured data sources (1/2)
Trend + Seasonality + Employee but, sometimes a miss Forces may be in play
?
Can we spot the residuals and, determine causation Trend + Seasonal + State Space
?
Consider an example of forecasting which traditionally relies on time
series data.....
Explorer can help us detect and evaluate attributes from unstructured
sources which can help predict patterns at an individual SKU level
Source: Implementation of Watson Explorer (WEX) Enterprise Edition

43. 43
Enrich existing Advanced Analytics Models by allowing
scientists to tap into real-time unstructured data sources (2/2)
Unstructured
Extract and structure relevant
context from unformatted
sources.
i.e. PDF, newspapers
Streaming
External Data
Sources gathered across
multiple platforms that
simulate and forecast local
conditions.
i.e. Eventful, NOAA, Census
Real-Time Monitoring
Ability to react to forces
instantly creating additional
flexibility.
Structured
Structured
Internal Data
Transactions
Store profiles
Advertising
Social
Social Media (GNIP, Board
Reader)
National Newspapers
Local Newspapers
Weather Blogs
Source:Computing & Analytics for a Retail Supply Chain

44. How is Cognitive Computing
likely to be consumed in future?

45. 45
Watson Development Cloud is putting the capabilities of
cognitive computing in the public space
A collection of REST API’s and SDK’s which allow developers to build
apps which rely on cognitive computing
These Apps will allow for easier integration of Cognitive Capabilities
into current industry solutions

46. 46
Summary
• Product differentiation likely to reduce with several disruptive
technologies arising
• Service will be the new battlefield in an increasing number of
electronics sub-industries
• A key challenge in service differentiation will be the ability to ingest and
develop insights from a multitude of customer data
• Cognitive computing will help organizations tap into unstructured data
• There are 3 main branches of cognitive computing (engagement,
discover, decision)
• Engagement is the most mature of the 3 branches with several use
cases
• With the announcement of Watson development cloud, the costs of
development is likely to come down significantly while helping
adoption

47. 47
Thank You

48. 48
Citations
• Robotics icon- Created by AhaSoft from Noun Project
• 3D Printing icon - Created by iconsmind from Noun Project
• Analytics icon - Created by Ollie Taylor from Noun Project
• Internet icon - Created by Shelten Davis from Noun Project
• Opensource icon – Created by Bjorn Andersson from Noun Project
• Dog icon – Created by Norman Ying from Noun Project
• Book icon - Created by Jon Testa from Noun Project
• Brain icon – Created by Marcus Micheals from Noun Project
• Weather icon – Created by Mateo Zlatar from Noun Project
• Economics icon – Created by Juan Pablo Bravo from Noun Project
• Family icon – Created by Luis Prado from Noun Project
• TV icon – Created by Mike Ashley from Noun Project
• Web icon – Created by Martin Vanco from Noun Project
• Mobile icon – Created by Hhari Davodpour from Noun Project
• Store icon – Created by Edward Boatman from Noun Project
• Network icon – Created by Gilbert Bages from Noun Project
• Cart icon – Created by Hello Marry from Noun Project
• Data Pic - https://commons.wikimedia.org/wiki/File:DARPA_Big_Data.jpg
• Social Media Pic:
https://commons.wikimedia.org/wiki/File:Social_networking_services.jpg
• Internet of Things Pic:
https://commons.wikimedia.org/wiki/File:Internet_de_las_Cosas.jpg