This document summarizes a webinar presented by Bob E. Hayes, Ph.D on improving the customer experience using big data, customer-centric measurement, and analytics. The webinar covered topics such as customer experience management, measuring customer loyalty, conducting optimal customer surveys, understanding the value of analytics, and taking a big data approach centered around customers. Hayes emphasized the importance of understanding relative performance compared to competitors and using both general and specific customer experience questions to best predict customer loyalty.

1. © 2009 VMware Inc. All rights reserved
Big Data Thought Leadership Webinar
Web:
www.cetas.net
Twi)er:
@CetasAnaly/cs
Blog:
www.cetas.net/blog
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2. 2
Introductions
David Morris, Host
Big Data Analytics Marketing – Cetas, By VMware
dmorris@vmware.com
@jdavidmorris
Please submit your questions at anytime throughout the webinar via the chat tool.
Today’s Thought Leadership Webinar:
Improving the Customer Experience Using Big Data,
Customer-Centric Measurement and Analytics

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

4. 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

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

6. TCE
Lab
TCE: Total Customer Experience
Copyright 2013 TCELab
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

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

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

9. TCE
Lab
Copyright 2013 TCELab
Optimal Customer
Relationship Survey

10. TCE
Lab
Customer Relationship Surveys
Copyright 2013 TCELab
• 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

11. TCE
Lab
Four Parts to Customer Surveys
Copyright 2013 TCELab
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

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

13. TCE
Lab
Customer Loyalty Measurement Framework
Loyalty
Types
Emo9onal
Behavioral
Measurement
Approach
Objec9ve
ADVOCACY
• Number/Percent
of
new
customers
RETENTION
• Churn
rates
• Service
contract
renewal
rates
PURCHASING
• Usage
Metrics
–
Frequency
of
use/
visit,
Page
views
• Sales
Records
-­‐
Number
of
products
purchased
Subjec9ve
(SurveyQuestions)
ADVOCACY
• Overall
sa/sfac/on
• Likelihood
to
recommend
• Likelihood
to
buy
same
product
• Level
of
trust
• Willing
to
forgive
• Willing
to
consider
RETENTION
• Likelihood
to
renew
service
contract
• Likelihood
to
leave
PURCHASING
• Likelihood
to
buy
different/
addi/onal
products
• 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. TCE
Lab
Customer Experience
Copyright 2013 TCELab
• Two
types
of
customer
experience
ques/ons
• Overall, how satisfied
are you with…
Area
General
CX
Ques9ons
Specific
CX
Ques9ons
Product 1. Product Quality
1. Reliability of product
2. Features of product
3. Ease of using the product
4. Availability of product
Account
Management
2. Sales / Account
Management
1. Knowledge of your industry
2. Ability to coordinate resources
3. Understanding of your business issues
4. Responds quickly to my needs
Technical
Support
3. Technical Support
1. Timeliness of solution provided
2. Knowledge and skills of personnel
3. Effectiveness of solution provided
4. Online tools and services
0 1051 2 3 4 6 7 8 9
Extremely
Dissatisfied
Extremely
Satisfied
Neither Satisfied
Nor Dissatisfied

15. TCE
Lab
Customer Experience
Copyright 2013 TCELab
• Overall,
how
sa9sfied
are
you
with
each
area?
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
0 1051 2 3 4 6 7 8 9
Extremely
Dissatisfied
Extremely
Satisfied
Neither Satisfied
Nor Dissatisfied

16. TCE
Lab
CX Predicting Customer Loyalty
Copyright 2013 TCELab
74%
42%
60%
85%
0%
4%
2%
4%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Company
A
Company
B
Company
C
Company
D
Percent
of
Variability
(R2)
in
Customer
Loyalty
Explained
by
CX
Ques9ons
Specific
CX
Ques/ons
General
CX
Ques/ons
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.
1.
General
CX
ques9ons
explain
customer
loyalty
differences
well.
2.
Specific
CX
ques9ons
do
not
add
much
to
our
predic9on
of
customer
loyalty
differences.
3.
On
average,
each
Specific
CX
ques9on
explains
<
.5%
of
variability
in
customer
loyalty.
7
General
CX
5
General
CX
6
General
CX
7
General
CX
0
Specific
CX
14
Specific
CX
27
Specific
CX
34
Specific
CX

17. 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)
Competitive Analytics
Copyright 2013 TCELab

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

20. TCE
Lab
Understanding your Ranking
Copyright 2013 TCELab
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.

21. TCE
Lab
Additional Questions
Copyright 2013 TCELab
• 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?

22. TCE
Lab
Summary: Your Relationship Survey
Copyright 2013 TCELab
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?

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

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

26. TCE
Lab
Three Big Data Approaches
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. TCE
Lab
Value from Analytics: MIT / IBM 2010 Study
Top-performing
organizations
use analytics five
times more than
lower performers
Copyright 2013 TCELab
http://sloanreview.mit.edu/the-magazine/2011-
winter/52205/big-data-analytics-and-the-path-from-
insights-to-value/
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

28. TCE
Lab
Value from Analytics: Accenture 2012 Study
Copyright 2013 TCELab
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.

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

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

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

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

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

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

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

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

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

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

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

40. 40
Big Data Thought Leadership Webinar Series
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Register Today, as space is limited for this premium webinar
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Wednesday, May 15, 2013
10:00 am PT/ 1:00 pm ET
dmorris@vmware.com

41. 41
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42. 42
Find the recording of this
webinar and PDF at:
www.cetas.net/webinars

43. © 2009 VMware Inc. All rights reserved
Big Data Thought
Leadership Webinar Series
Web:
www.cetas.net
Twi)er:
@CetasAnaly/cs
Blog:
www.cetas.net/blog
YouTube:
www.youtube.com/CetasAnaly/cs
INSTANT INTELLIGENCE
Live
Webinar
Registra9on
and
Recorded
Webinars
available
at
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44. TCE
Lab
RAPID Loyalty Measurement
Index Definition Survey Questions
Reten9on
Loyalty
Index
(RLI)
The
degree
to
which
customers
will
remain
as
a
customer/not
leave
to
compe/tor
(0
–
low
loyalty
to
10
–
high
loyalty)
Likelihood
to
switch
to
another
company*
Likelihood
to
purchase
from
compe/tor*
Likelihood
to
stop
purchasing*
Advocacy
Loyalty
Index
(ALI)
The
degree
to
which
customers
feel
posi/vely
toward/will
advocate
your
product/service/brand
(0
–
low
loyalty
to
10
–
high
loyalty)
Overall
sa/sfac/on
Likelihood
to
choose
again
for
first
/me
Likelihood
to
recommend
(NPS)
Likelihood
to
purchase
same
product/service
Purchasing
Loyalty
Index
(PLI)
The
degree
to
which
customers
will
increase
their
purchasing
behavior
(0
–
low
loyalty
to
10
–
high
loyalty)
Likelihood
to
purchase
different
products/services
Likelihood
to
expand
usage
throughout
company
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.
• Assesses three components of customer loyalty
Copyright 2013 TCELab

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

46. TCE
Lab
Operational Metrics
• Linkage analysis helps us determine/identify the
operational factors that influence customer
satisfaction/loyalty
• Support Metrics
– 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
Opera/onal
Metrics
Transac/onal
Sa/sfac/on