1. Big data and analytics can help companies better understand customers using large datasets from various sources to improve customer loyalty. 2. Linking customer feedback data to operational, financial, and constituency metrics through linkage analysis can identify key drivers of customer satisfaction and loyalty. 3. This allows companies to prioritize areas for improvement and measure the impact on real customer behaviors and business outcomes.

1. Using Big Data to Improve
How may we help?
Customer Loyalty info@tcelab.com
Spring 2012
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. Search Volume Index
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Big Data
July 2007
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Jan 2008
March 2008
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July 2008
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Customer Experience
Nov 2008
Jan 2009
Copyright 2012 TCELab
March 2009
May 2009
July 2009
Sept 2009
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Jan 2010
March 2010
May 2010
July 2010
Sept 2010
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Jan 2011
March 2011
Big Interest in Big Data: Google Trends
May 2011
July 2011
Sept 2011
Nov 2011
Scale is based on the average worldwide traffic of customer experience in all years.
Jan 2012
March 2012

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
Copyright 2012 TCELab

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
Customer
Operational Feedback
Financial
1. Call handling time 1. Customer Loyalty
2. Number of calls until 2. Relationship
resolution satisfaction 1. Revenue
3. Response time 3. Transaction satisfaction 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 transactions
2. Satisfaction with 2. Satisfaction with 6. Frequency of
partnering relationship business areas purchases
Copyright 2012 TCELab

7. Value from Analytics: MIT / IBM 2010 Study
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 2012 TCELab

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
Copyright 2012 TCELab

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 Transactional
Metrics Satisfaction
Relationship Financial
Satisfaction/ Business
Loyalty Metrics
Constituency
Satisfaction/
Loyalty
Copyright 2012 TCELab

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

14. VoC / Financial Linkage
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 2012 TCELab

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

16. Operational Metrics
• Linkage analysis helps us determine/identify the
operational factors that influence customer
satisfaction/loyalty
Operational Transactional
• Support Metrics Metrics Satisfaction
– 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

17. Operational / VoC Linkage
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.
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18. Identify Operational Drivers
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19. Identify Operational Standards
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 2012 TCELab

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

21. Constituency / VoC Linkage
Employee Customer
Satisfaction Satisfaction / Loyalty
with the Company
_
x1 Employee 1 y1
_
x2 Employee 2 y2
_
x3 Employee 3 y3
_
x4 Employee 4 y4
. . .
. . .
. . .
_
xn Employee n yn
1Analysis typically conducted for B2B customers where a given employee (sales representative, technical
account manager, sales manager) is associated with a given customer (account)
_n represents employee satisfaction score for Employee n.
x
yn represents average customer satisfaction scores across survey respondents for Employee n.
Copyright 2012 TCELab

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