This presentation provides an overview of some of the content of my new book, TCE: Total Customer Experience. In the presentation, I discuss customer experience management, customer loyalty, the optimal customer survey, the value of analytics and using a Big Data customer-centric approach to improve the value of all your business data

1. 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

2. 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

3. Copyright 2013 TCELab
Customer Experience,
Customer Experience Management
and Customer Loyalty

4. Customer Experience Management (CEM)
The process of
understanding and
managing your
customers’
interactions with
and perceptions
of your brand /
company
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5. Copyright 2013 TCELab
Optimal Customer
Relationship Survey

6. Customer Relationship Surveys
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• 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

7. Four Parts to Customer Surveys
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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

8. 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
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9. Customer Loyalty Measurement Framework
Loyalty Types
Emotional Behavioral
MeasurementApproach
Objective
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
Subjective
(SurveyQuestions)
ADVOCACY
• Overall satisfaction
• 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/
additional 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

10. Customer Experience
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• Two types of customer experience questions
• Overall, how satisfied
are you with…
Area General CX Questions Specific CX Questions
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

11. Customer Experience
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• Overall, how satisfied 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

12. CX Predicting Customer Loyalty
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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
PercentofVariability(R2)inCustomer
LoyaltyExplainedbyCXQuestions
Specific CX Questions
General CX Questions
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
questions explain
customer loyalty
differences well.
2. Specific CX
questions do not add
much to our
prediction of customer
loyalty differences.
3. On average, each
Specific CX question
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

13. • Customer experience questions may not be
enough to improve business growth
– You need to understand your relative performance
• HBR study (2011)1: Top-ranked companies
receive greater share of wallet compared to
bottom-ranked companies
• Focus on increasing purchasing loyalty (e.g.,
customers buy more from you)
Competitive Analytics
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14. Relative Performance Assessment (RPA)
• Ask customers to rank you relative to the competitors
in their usage set
• What best describes our performance compared to
the competitors you use?
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15. RPA Predicting Customer Loyalty
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69% 72%
18% 16% 14%
1%
2%
8% 7%
1%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Overall
Satisfaction
Recommend Purchase
different/new
solutions
Expand usage Renew
Subscription
PercentofVariability(R2)inCustomer
LoyaltyExplainedbyGeneralCXQuestionsand
RelativePerformanceAssessment(RPA)
Loyalty Questions
1 RPA Question
7 General CX Questions
 What best describes our performance compared to
the competitors you use?
1. General CX questions
explain purchasing
loyalty differences well.
2. Relative Performance
Assessment improved
the predictability of
purchasing loyalty by
almost 50%
3. Improving company’s
ranking against the
competition will
improve purchasing
loyalty and share of
wallet

16. Understanding your Ranking
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1. Correlate RPA score with customer experience
measures
2. Analyze customer comments about the reasons
behind their ranking
– Why did you think we are better/worse than the
competition?
– Which competitors are better than us and why?
• What to improve?
– Product Quality was top driver of Relative 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.

17. Additional Questions
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• Out of necessity or driven by specific business need
• Segmentation Questions
– 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 solution (What is the % improvement
in efficiency / productivity / customer satisfaction)
– Perceived value (How satisfied are you with the value
received?)
• Open-ended questions for improvement areas
– If you were in charge of our company, what improvements,
if any, would you make?

18. Summary: Your Relationship Survey
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1. Measure different types of customer loyalty
(N = 4-6)
2. Consider the number of customer experience
questions in your survey (N = 7)
– General CX questions point you in the right direction.
3. Measure your relative performance (N = 3)
– Understand and Improve/Maintain your competitive advantage
4. Consider additional questions (N = 5)
– How will you use the data?

19. Copyright 2013 TCELab
Big Data, Analytics and Integration

20. 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
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21. Big Data Landscape – bigdatalandscape.com
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22. 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

23. 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

24. Value from Analytics: Accenture 2012 Study
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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.

25. Disparate Sources of Business Data
1.Call handling time
2.Number of calls until
resolution
3.Response time
1.Revenue
2.Number of products
purchased
3.Customer tenure
4.Service contract
renewal
5.Number of sales
transactions
6.Frequency of
purchases
1.Customer Loyalty
2.Relationship satisfaction
3.Transaction satisfaction
4.Sentiment
1.Employee Loyalty
2.Satisfaction with
business areas
Operational
Partner Feedback
1.Partner Loyalty
2.Satisfaction with
partnering relationship
Customer
Feedback
Employee
Feedback
Financial
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26. Data Integration is Key to Extracting Value
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27. Linkage Analysis
Operational
Metrics
Transactional
Satisfaction
Relationship
Satisfaction/
Loyalty
Financial
Business
Metrics
Constituency
Satisfaction/
Loyalty
Copyright 2013 TCELab

28. 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

29. 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.
.
.
.
.
.
.
.
.
.
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30. Determine ROI of Increasing Customer Loyalty
Disloyal (0-5) Loyal ( 6-8) Very Loyal (9-10)
PercentPurchasing
AdditionalSoftware
Customer Loyalty
55%
increase
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31. 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.
.
.
.
.
.
.
.
.
.
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32. Identify Operational Drivers of Satisfaction
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33. Identify Operational Standards
1 call 2-3 calls 4-5 calls 6-7 calls 8 or more calls
SatwithSR
Number of Calls to Resolve SR
1 change 2 changes 3 changes 4 changes 5+ changes
SatwithSR
Number of SR Ownership Changes
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34. 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

35. 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

36. RAPID Loyalty Measurement
Index Definition Survey Questions
Retention
Loyalty
Index (RLI)
The degree to which customers will
remain as a customer/not leave to
competitor (0 – low loyalty to 10 –
high loyalty)
Likelihood to switch to another company*
Likelihood to purchase from competitor*
Likelihood to stop purchasing*
Advocacy
Loyalty
Index (ALI)
The degree to which customers feel
positively toward/will advocate your
product/service/brand (0 – low loyalty
to 10 – high loyalty)
Overall satisfaction
Likelihood to choose again for first time
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

37. 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
Relationship
Satisfaction/
Loyalty
Financial
Business
Metrics
Copyright 2013 TCELab

38. 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)
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Operational
Metrics
Transactional
Satisfaction