This document discusses how companies can use big data analytics to improve customer loyalty. It explains that big data refers to tools and processes for managing and analyzing large datasets. Integrating different sources of customer, operational, financial, and employee data is key to extracting value. Linkage analysis is used to determine how operational metrics, employee satisfaction, and financial outcomes are related to customer satisfaction and loyalty. The results can help companies identify specific drivers of customer loyalty and validate training and operational standards.

1. How may we help?
info@tcelab.com
Spring 2012
Using Big Data to Improve
Customer Loyalty
Bob E. Hayes, PhD

2. Overview
1. Big Data
2. Analytics
3. Data Integration
4. Customer Loyalty
5. Customer Experience Management
6. Linkage Analysis
7. Implications
Copyright 2012 TCELab

3. Big Interest in Big Data: Google Trends
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SearchVolumeIndex
Customer Experience
Big Data
Scale is based on the average worldwide traffic of customer experience in all years.
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4. 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 data
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5. Three Vs of Big Data
• Volume – amount of data is massive
• Velocity – speed at which data are
being generated is very fast
• Variety – different types of data like
structured and unstructured
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6. 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
1.Employee Loyalty
2.Satisfaction with
business areas
Operational
Partner Feedback
1.Partner Loyalty
2.Satisfaction with
partnering relationship
Customer
Feedback
Employee
Feedback
Financial
Copyright 2012 TCELab

7. Value from Analytics: MIT / IBM 2010 Study
Top-performing
organizations
use analytics five
times more than
lower performers
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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

8. 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
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9. Data Integration is Key to Extracting Value
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10. CEM and Customer Loyalty
• Customer loyalty is key to business
growth
• The process of understanding and
managing your customers’ interactions
with and perceptions of your brand /
company
Copyright 2012 TCELab

11. Linkage Analysis
• Linkage analysis answers the questions:
– What is the $ value of improving customer
satisfaction/loyalty?
– Which operational metrics have the biggest impact on
customer satisfaction/loyalty?
– Which employee/partner factors have the biggest impact on
customer satisfaction/loyalty?
Operational
Metrics
Transactional
Satisfaction
Relationship
Satisfaction/
Loyalty
Financial
Business
Metrics
Constituency
Satisfaction/
Loyalty
Copyright 2012 TCELab

12. Customer Feedback Data Sources
Relationship
(satisfaction/loyalty to company)
Transaction
(satisfaction with specific
transaction/interaction)
BusinessDataSources
Financial
(revenue, number of
sales)
•Link data at customer level
•Quality of the relationship (sat,
loyalty) impacts financial metrics
N/A
Operational
(call handling, response
time)
N/A
•Link data at transaction level
•Operational metrics impact
quality of the transaction
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
Integrating your Business Data
Copyright 2012 TCELab

13. 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

14. VoC / 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 2012 TCELab

15. ROI of Customer Loyalty
Disloyal (0-5) Loyal ( 6-8) Very Loyal (9-10)
PercentPurchasing
AdditionalSoftware
Customer Loyalty
55%
increase
Copyright 2012 TCELab

16. 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 2012 TCELab
Operational
Metrics
Transactional
Satisfaction

17. Operational / VoC 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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Copyright 2012 TCELab

18. Identify Operational Drivers
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19. 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
Copyright 2012 TCELab

20. Constituency Metrics
• Linkage analysis helps us understand how
constituency variables impact customer
satisfaction/loyalty
• Satisfaction Metrics
– Employee satisfaction with
business areas
– Partner satisfaction with
partner relationship
• Loyalty Metrics
– Employee/Partner loyalty
Employee
Metrics
Customer
Satisfaction/
Loyalty
Partner
Metrics
• Other Metrics
• Employee training metrics
• Partner certification status
Copyright 2012 TCELab

21. Constituency / VoC Linkage
Employee 1
Employee 2
Employee 3
Employee 4
Employee n
Employee
Satisfaction
with the Company
Customer
Satisfaction / Loyalty
x1
x3
x2
xn
x4
y1
y3
y2
yn
y4
.
.
.
.
.
.
.
.
.
yn represents average customer satisfaction scores across survey respondents for Employee n.
_xn represents employee satisfaction score for Employee n.
1 Analysis typically conducted for B2B customers where a given employee (sales representative, technical
account manager, sales manager) is associated with a given customer (account)
_
_
_
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_
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22. Identify Employee Drivers
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23. Validate Training Standards
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24. Implications
• Ask and answer bigger questions about
your customers
– Explore all business data to understand their
impact on customer experience / loyalty
• Build your company around your
customers
– Greenplum social network platform
– Cross-functional teams working together to
solve customer problems
• Use objective, “real,” loyalty metrics
Copyright 2012 TCELab

25. How may we help?
info@tcelab.com
Spring 2012
Using Big Data to Improve
Customer Loyalty
Bob E. Hayes, PhD
bob@tcelab.com
@bobehayes
businessoverbroadway.com/blog