In this presentation, Bob Hayes delivers an overview of his upcoming ebook "TCE - Total Customer Experience: Building Business Through Customer-Centric Measurement and Analytics." How can companies gain deeper customer insights to help them improve the customer experience and increase customer loyalty?

1. Big Data Thought Leadership Webinar
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www.cetas.net
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YouTube:
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© 2009 VMware Inc. All rights reserved

2. Introductions
David Morris, Host
Big Data Analytics Marketing – Cetas, By VMware
dmorris@vmware.com
@jdavidmorris
Today’s Thought Leadership Webinar:
Improving the Customer Experience Using Big Data,
Customer-Centric Measurement and Analytics
Please submit your questions at anytime throughout the webinar via the chat tool.
2

3. New Company
EMC VMware
Pivotal April 24th
• Greenplum
• Gemfire
• Cetas
• Pivotal Labs
3

4. April’s Big Data Thought Leader
Bob E. Hayes, Ph.D.
Chief Customer Officer – TCElab
President of Business Over Broadway
• Customer Satisfaction and Loyalty
Improvement expert
• 20 years experience consulting with
enterprise and midsize organizations
• New book: TCE: Total Customer Experience
– Building Business through Customer-
Centric Measurements and Analytics
bob@tcelab.com
@bobehayes
businessoverbroadway.com/blog
4

5. Improving the Customer Experience
Using Big Data, Customer-Centric How may we help?
info@tcelab.com
Measurement and Analytics Spring 2013
Bob E. Hayes, PhD

6. TCE: Total Customer Experience
TCE
Lab
1. Customer Experience
Management
2. Customer Loyalty
3. Optimal Customer
Survey
4. Value of Analytics
5. Big Data Customer-
Centric Approach
For more info on book:
http://bit.ly/tcebook
Copyright 2013 TCELab

7. TCE
Lab
Customer
Experience,
Customer
Experience
Management
and
Customer
Loyalty
Copyright 2013 TCELab

8. Customer Experience Management (CEM)
TCE
Lab
The process of
understanding and
managing your
customers’
interactions with
and perceptions
of your brand /
company
Copyright 2013 TCELab

9. TCE
Lab
Optimal Customer
Relationship Survey
Copyright 2013 TCELab

10. Customer Relationship Surveys
TCE
Lab
• Solicited feedback from customers about their
experience with company/brand
• Assess health of the customer relationship
• Conducted periodically (non-trivial time period)
• Common in CEM Programs
– Guide company strategy
– Identify causes of customer loyalty
– Improve customer experience
– Prioritize improvement efforts to maximize ROI
Copyright 2013 TCELab

11. Four Parts to Customer Surveys
TCE
Lab
1. Customer Loyalty – likelihood of
customers engaging in positive behaviors
2. Customer Experience – satisfaction with
important touch points
3. Relative Performance – your competitive
advantage
4. Additional Questions – Extra value-
added questions
Copyright 2013 TCELab

12. Customer Loyalty Types
TCE
Lab
The degree to which customers
experience positive feelings for
and engage in positive behaviors
toward a company/brand
Emotional Behavioral
(Advocacy) (Retention, Purchasing)
Love, Consider, Stay, Renew, Buy,
Forgive, Trust Buy more often,
Expand usage
Copyright 2013 TCELab

13. Customer Loyalty Measurement Framework
TCE
Lab
Loyalty
Types
Emo9onal
Behavioral
RETENTION
Measurement
Approach
• Churn
rates
Objec9ve
• Service
contract
renewal
rates
ADVOCACY
• Number/Percent
of
new
PURCHASING
customers
• Usage
Metrics
–
Frequency
of
use/
visit,
Page
views
• Sales
Records
-­‐
Number
of
products
purchased
RETENTION
(Survey Questions)
ADVOCACY
• Likelihood
to
renew
service
contract
Subjec9ve
• Overall
sa/sfac/on
• Likelihood
to
leave
• Likelihood
to
recommend
• Likelihood
to
buy
same
product
PURCHASING
• Level
of
trust
• Likelihood
to
buy
different/
• Willing
to
forgive
addi/onal
products
• Willing
to
consider
• Likelihood
to
expand
usage
1 Using RAPID Loyalty Approach - Overall satisfaction rated on a scale from 0 (Extremely Dissatisfied) to 10 (Extremely Satisfied). Other questions
are rated on a scale from 0 (Not at all likely) to 10 (Extremely likely). * Reverse coded so lower rates of these behaviors indicates higher levels of
Retention Loyalty. Copyright 2013 TCELab

14. Customer Experience
TCE
Lab
• Two
types
of
customer
experience
ques/ons
• Overall, how satisfied Extremely
Dissatisfied
Neither Satisfied
Nor Dissatisfied
Extremely
Satisfied
are you with… 0 1 2 3 4 5 6 7 8 9 10
Area
General
CX
Ques9ons
Specific
CX
Ques9ons
1. Reliability of product
2. Features of product
Product 1. Product Quality 3. Ease of using the product
4. Availability of product
1. Knowledge of your industry
Account 2. Sales / Account 2. Ability to coordinate resources
Management Management 3. Understanding of your business issues
4. Responds quickly to my needs
1. Timeliness of solution provided
Technical 2. Knowledge and skills of personnel
3. Technical Support 3. Effectiveness of solution provided
Support
4. Online tools and services
Copyright 2013 TCELab

15. Customer Experience
TCE
Lab
• Overall,
how
sa9sfied
are
you
with
each
area?
Extremely Neither Satisfied Extremely
Dissatisfied Nor Dissatisfied Satisfied
0 1 2 3 4 5 6 7 8 9 10
1. Ease of doing business
2. Sales / Account Management
3. Product Quality
4. Service Quality
5. Technical Support
6. Communications from the Company
7. Future Product/Company Direction
Copyright 2013 TCELab

16. CX Predicting Customer Loyalty
TCE
Lab
100%
1.
General
CX
Specific
CX
Ques/ons
Loyalty
Explained
by
CX
Ques9ons
90%
4%
ques9ons
explain
General
CX
Ques/ons
Percent
of
Variability
(R2)
in
80%
0%
customer
loyalty
differences
well.
70%
2%
Customer
60%
2.
Specific
CX
50%
4%
ques9ons
do
not
add
85%
much
to
our
predic9on
40%
74%
of
customer
loyalty
30%
60%
differences.
20%
42%
3.
On
average,
each
10%
Specific
CX
ques9on
0%
explains
<
.5%
of
Company
A
Company
B
Company
C
Company
D
variability
in
customer
7
General
CX
5
General
CX
6
General
CX
7
General
CX
loyalty.
0
Specific
CX
14
Specific
CX
27
Specific
CX
34
Specific
CX
General CX items reflected areas (e.g., product quality, ease of doing business, tech support) and additional specific CX items reflected specific
aspects of the general items (product reliability, tech support knowledge, account management’s ability to respond quickly).
R2 reflects percent of variance of customer loyalty that is explained when using general items in regression analysis . ∆R2 reflects the additional
percent of variance explained above what is explained by general items when using general items and specific items in a stepwise regression
analysis.
Copyright 2013 TCELab

17. Competitive Analytics
TCE
Lab
• Customer
experience
ques/ons
may
not
be
enough
to
improve
business
growth
– You
need
to
understand
your
rela/ve
performance
• HBR
study
(2011)1:
Top-­‐ranked
companies
receive
greater
share
of
wallet
compared
to
bofom-­‐ranked
companies
• Focus
on
increasing
purchasing
loyalty
(e.g.,
customers
buy
more
from
you)
Copyright 2013 TCELab

18. Relative Performance Assessment (RPA)
TCE
Lab
• Ask
customers
to
rank
you
rela/ve
to
the
compe/tors
in
their
usage
set
• What
best
describes
our
performance
compared
to
the
compe9tors
you
use?
Copyright 2013 TCELab

19. RPA Predicting Customer Loyalty
TCE
Lab
§ What
best
describes
our
performance
compared
to
the
compe9tors
you
use?
100%
1.
General
CX
ques9ons
Loyalty
Explained
by
General
CX
Ques9ons
and
90%
explain
purchasing
Rela9ve
Performance
Assessment
(RPA)
Percent
of
Variability
(R2)
in
Customer
loyalty
differences
well.
80%
2%
1%
1
RPA
Ques/on
70%
2.
Rela9ve
Performance
7
General
CX
Ques/ons
Assessment
improved
60%
the
predictability
of
50%
purchasing
loyalty
by
40%
almost
50%
69%
72%
30%
8%
7%
1%
3.
Improving
company’s
20%
ranking
against
the
10%
18%
16%
14%
compe99on
will
0%
improve
purchasing
Overall
Recommend
Purchase
Expand
usage
Renew
Sa/sfac/on
different/new
Subscrip/on
loyalty
and
share
of
solu/ons
wallet
Loyalty
Ques9ons
Copyright 2013 TCELab

20. Understanding your Ranking
TCE
Lab
1. Correlate
RPA
score
with
customer
experience
measures
2. Analyze
customer
comments
about
the
reasons
behind
their
ranking
– Why
did
you
think
we
are
befer/worse
than
the
compe//on?
– Which
compe/tors
are
befer
than
us
and
why?
• What
to
improve?
– Product
Quality
was
top
driver
of
Rela/ve
Performance
Assessment
– Open-­‐ended
comments
by
customers
who
gave
low
RPA
rankings
were
primarily
focused
on
making
the
product
easier
to
use
while
adding
more
customizability.
Copyright 2013 TCELab

21. Additional Questions
TCE
Lab
• Out
of
necessity
or
driven
by
specific
business
need
• Segmenta/on
Ques/ons
– How
long
have
you
been
a
customer?
– What
is
your
role
in
purchasing
decisions?
– What
is
your
job
level?
• Specific
topics
of
interest
to
senior
management
– Perceived
benefits
of
solu/on
(What
is
the
%
improvement
in
efficiency
/
produc/vity
/
customer
sa/sfac/on)
– Perceived
value
(How
sa/sfied
are
you
with
the
value
received?)
• Open-­‐ended
ques/ons
for
improvement
areas
– If
you
were
in
charge
of
our
company,
what
improvements,
if
any,
would
you
make?
Copyright 2013 TCELab

22. Summary: Your Relationship Survey
TCE
Lab
1. Measure
different
types
of
customer
loyalty
(N
=
4-­‐6)
2. Consider
the
number
of
customer
experience
ques/ons
in
your
survey
(N
=
7)
– General
CX
ques/ons
point
you
in
the
right
direc/on.
3. Measure
your
rela/ve
performance
(N
=
3)
– Understand
and
Improve/Maintain
your
compe//ve
advantage
4. Consider
addi/onal
ques/ons
(N
=
5)
– How
will
you
use
the
data?
Copyright 2013 TCELab

23. TCE
Lab
Big
Data,
Analy/cs
and
Integra/on
Copyright 2013 TCELab

24. Big Data
TCE
Lab
• Big Data refers to the tools and
processes of managing and utilizing
large datasets.
• An amalgamation of different areas that
help us try to get a handle on, insight from
and use out of large, quickly-expanding,
diverse data
Copyright 2013 TCELab

25. TCE
Big Data Landscape – bigdatalandscape.com Lab
Copyright 2013 TCELab

26. Three Big Data Approaches
TCE
Lab
1. Interactive Exploration - good
for discovering real-time patterns from
your data as they emerge
2. Direct Batch Reporting - good
for summarizing data into pre-built,
scheduled (e.g., daily, weekly) reports
3. Batch ETL (extract-transform-load) -
good for analyzing historical trends or
linking disparate data
Copyright 2012 TCELab

27. Value from Analytics: MIT / IBM 2010 Study
TCE
Lab
Top-performing
organizations
use analytics five
times more than
lower performers
Number one obstacle to
the adoption of analytics
in their organizations was
a lack of understanding
of how to use analytics to
improve the business
http://sloanreview.mit.edu/the-magazine/2011-
winter/52205/big-data-analytics-and-the-path-from-
insights-to-value/
Copyright 2013 TCELab

28. Value from Analytics: Accenture 2012 Study
TCE
Lab
1. Measure Right Customer Metrics - only
20% were very satisfied with the business
outcomes of their existing analytics
programs
2. Focus on Strategic Issues - only 39%
said that the data they generate is
"relevant to the business strategy"
3. Integrate Business Metrics - Half of the
executives indicated that data integration
remains a key challenge to them.
Copyright 2013 TCELab

29. Disparate Sources of Business Data
TCE
Lab
Customer
Operational Feedback
Financial
1. Call
handling
/me
1. Customer
Loyalty
2. Number
of
calls
un/l
2. Rela/onship
sa/sfac/on
resolu/on
3. Transac/on
sa/sfac/on
1. Revenue
3. Response
/me
4. Sen/ment
2. Number
of
products
purchased
3. Customer
tenure
Employee 4. Service
contract
Partner Feedback renewal
Feedback
5. Number
of
sales
1. Partner
Loyalty
1. Employee
Loyalty
transac/ons
2. Sa/sfac/on
with
2. Sa/sfac/on
with
6. Frequency
of
partnering
rela/onship
business
areas
purchases
Copyright 2013 TCELab

30. Data Integration is Key to Extracting Value
TCE
Lab
Copyright 2013 TCELab

31. Linkage Analysis
TCE
Lab
Opera/onal
Transac/onal
Metrics
Sa/sfac/on
Rela/onship
Financial
Sa/sfac/on/
Business
Loyalty
Metrics
Cons/tuency
Sa/sfac/on/
Loyalty
Copyright 2013 TCELab

32. Integrating your Business Data
TCE
Lab
Customer Feedback Data Sources
Relationship Transactional Social Media/
Survey Survey Communities
(satisfaction/loyalty to (satisfaction with specific
(sentiment / shares / likes)
company) transaction/interaction)
• Link data at customer
level • Link data at customer level
Financial
Business Data Sources
• Quality of the N/A • Quality of relationship
(revenue, number of
sales) relationship (sat, loyalty) (sentiment / likes / shares)
impacts financial metrics impacts financial metrics
• Link data at transaction • Link data at transaction
level level
Operational
(call handling, response N/A • Operational metrics impact • Operational metrics impact
time) quality of the transaction sentiment / likes/ shares
• Link data at constituency • Link data at constituency • Link data at constituency
level level level
Constituency • Constituency satisfaction • Constituency satisfaction • Constituency satisfaction
(employee / partner
feedback) impacts customer impacts customer impacts customer
satisfaction with overall satisfaction with interaction sentiment / likes / shares
relationship
Copyright 2013 TCELab

33. Customer Feedback / Financial Linkage
TCE
Lab
Customer Feedback Financial Metric
for a specific for a specific
customer (account)" customer (account)"
Customer"
x1" (Account) 1" y1"
Customer
x2" (Account) 2"
y2"
Customer "
x3" (Account) 3"
y3"
Customer"
x4" (Account) 4" y4"
." ." ."
." ." ."
." ." ."
Customer"
xn" (Account) n"
yn"
xn represents customer feedback for customer n."
yn represents the financial metric for customer n."
Copyright 2013 TCELab

34. TCE
Determine ROI of Increasing Customer Loyalty Lab
Percent Purchasing
Additional Software
55%
increase
Disloyal (0-5) Loyal ( 6-8) Very Loyal (9-10)
Customer Loyalty
Copyright 2013 TCELab

35. Operational / Customer Feedback Linkage
TCE
Lab
Operational Metric Customer Feedback
for a specific for a specific
customer’s interaction" customer’s interaction"
Customer 1"
x1" Interaction" y1"
Customer 2"
x2" Interaction"
y2"
Customer 3"
x3" Interaction"
y3"
Customer 4"
x4" Interaction" y4"
." ." ."
." ." ."
." ." ."
Customer n"
xn" Interaction"
yn"
xn represents the operational metric for customer interaction n."
yn represents the customer feedback for customer interaction n."
Copyright 2013 TCELab

36. Identify Operational Drivers of Satisfaction
TCE
Lab
Copyright 2013 TCELab

37. Identify Operational Standards
TCE
Lab
Number
of
Calls
to
Resolve
SR
Sat
with
SR
1
call
2-­‐3
calls
4-­‐5
calls
6-­‐7
calls
8
or
more
calls
Number of SR Ownership Changes
Sat with SR
1 change 2 changes 3 changes 4 changes 5+ changes
Copyright 2013 TCELab

38. 3 Implications of Big Data in CEM
TCE
Lab
1. Ask/Answer bigger questions
2. Build company around the customer
3. Predict real customer loyalty behaviors
Copyright 2012 TCELab

39. For more info on book:
bob@tcelab.com http://bit.ly/tcebook
@bobehayes
businessoverbroadway.com/blog
Improving the Customer Experience
Using Big Data, Customer-Centric How may we help?
info@tcelab.com
Measurement and Analytics Spring 2013
Bob E. Hayes, PhD

40. Big Data Thought Leadership Webinar Series
May’s Big Data Thought Leader:
Karl M. Kapp
“Gamification: Leveraging Game
Strategies to Drive Business”
Wednesday, May 15, 2013
10:00 am PT/ 1:00 pm ET
Register Today, as space is limited for this premium webinar
www.cetas.net/webinars
dmorris@vmware.com
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41. Sign-up today for FREE Analytics @ www.cetas.net !
Cetas Big Data Analytics Free Trial
Monetize Your Big Data Today!
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42. Find the recording of this
webinar and PDF at:
www.cetas.net/webinars
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43. Big Data Thought
Leadership Webinar Series
INSTANT INTELLIGENCE
Live
Webinar
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and
Recorded
Webinars
available
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Web:
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Twi)er:
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Blog:
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YouTube:
www.youtube.com/CetasAnaly/cs
© 2009 VMware Inc. All rights reserved

44. RAPID Loyalty Measurement
TCE
Lab
• Assesses three components of customer loyalty
Index Definition Survey Questions
The
degree
to
which
customers
will
Likelihood
to
switch
to
another
company*
Reten9on
remain
as
a
customer/not
leave
to
Likelihood
to
purchase
from
compe/tor*
Loyalty
compe/tor
(0
–
low
loyalty
to
10
–
Index
(RLI)
Likelihood
to
stop
purchasing*
high
loyalty)
The
degree
to
which
customers
feel
Overall
sa/sfac/on
Advocacy
posi/vely
toward/will
advocate
your
Likelihood
to
choose
again
for
first
/me
Loyalty
product/service/brand
(0
–
low
loyalty
Likelihood
to
recommend
(NPS)
Index
(ALI)
to
10
–
high
loyalty)
Likelihood
to
purchase
same
product/service
Purchasing
The
degree
to
which
customers
will
Likelihood
to
purchase
different
products/services
Loyalty
increase
their
purchasing
behavior
(0
–
Likelihood
to
expand
usage
throughout
company
Index
(PLI)
low
loyalty
to
10
–
high
loyalty)
Likelihood
to
upgrade
1 Overall satisfaction rated on a scale from 0 (Extremely Dissatisfied) to 10 (Extremely Satisfied). Other questions are rated on a scale from 0
(Not at all likely) to 10 (Extremely likely). * Reverse coded so lower rates of these behaviors indicates higher levels of Retention Loyalty.
Copyright 2013 TCELab

45. Financial Metrics / Real Loyalty Behaviors
TCE
Lab
• Linkage analysis helps us determine if our
customer feedback metrics predict real and
measurable business outcomes
• Retention Rela/onship
Financial
Sa/sfac/on/
Business
– Customer tenure Loyalty
Metrics
– Customer defection rate
– Service contract renewal • Purchasing
• Advocacy • Number of products
– Number of new customers purchased
– Revenue • Number of sales
transactions
• Frequency of purchases
Copyright 2013 TCELab

46. Operational Metrics
TCE
Lab
• Linkage analysis helps us determine/identify the
operational factors that influence customer
satisfaction/loyalty
Opera/onal
Transac/onal
• Support Metrics Metrics
Sa/sfac/on
– First Call Resolution (FCR)
– Number of calls until resolution
– Call handling time
– Response time
– Abandon rate
– Average talk time
– Adherence & Shrinkage
– Average speed of answer (ASA)
Copyright 2013 TCELab