Big Data has Big Implications for Customer Experience Management
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Transcript of Big Data has Big Implications for Customer Experience Management
Overview
1. Big Data2. Analytics3. Data Integration4. Customer Loyalty5. Customer Experience Management6. Linkage Analysis7. Implications
Copyright 2012 TCELab
Big Interest in Big Data: Google Trends
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Customer ExperienceBig Data
Sear
ch V
olum
e In
dex
Scale is based on the average worldwide traffic of customer experience in all years.
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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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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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Disparate Sources of Business Data
1. Call handling time2. Number of calls until
resolution3. Response time
1. Revenue2. Number of products
purchased3. Customer tenure4. Service contract
renewal5. Number of sales
transactions6. Frequency of
purchases
1. Customer Loyalty2. Relationship satisfaction3. Transaction satisfaction
1. Employee Loyalty2. Satisfaction with
business areas
Operational
Partner Feedback
1. Partner Loyalty2. Satisfaction with
partnering relationship
CustomerFeedback
EmployeeFeedback
Financial
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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
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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Data Integration is Key to Extracting Value
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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
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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
Customer Feedback Data SourcesRelationship
(satisfaction/loyalty to company)
Transaction(satisfaction with specific transaction/interaction)
Business Data Sources
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
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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
VoC / Financial Linkage
Customer(Account) 1
Customer(Account) 2
Customer (Account) 3
Customer(Account) 4
Customer(Account) n
Customer Feedbackfor a specific
customer (account)
Financial Metricfor 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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ROI of Customer Loyalty
Disloyal (0-5) Loyal ( 6-8) Very Loyal (9-10)
Customer Loyalty
Perc
ent P
urch
asin
gA
dditi
onal
Sof
twar
e
55%increase
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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
Operational / VoC Linkage
Customer 1Interaction
Customer 2Interaction
Customer 3Interaction
Customer 4Interaction
Customer nInteraction
Operational Metricfor 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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Identify Operational Drivers
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Identify Operational Standards
1 call 2-3 calls 4-5 calls 6-7 calls 8 or more calls
Number of Calls to Resolve SRSa
t with
SR
1 change 2 changes 3 changes 4 changes 5+ changes
Number of SR Ownership Changes
Sat w
ith S
R
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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
Constituency / VoC Linkage
Employee 1
Employee 2
Employee 3
Employee 4
Employee n
Employee Satisfactionwith the Company
CustomerSatisfaction / 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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Identify Employee Drivers
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Validate Training Standards
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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
How may we [email protected] 2012
Using Big Data to Improve Customer Loyalty
Bob E. Hayes, PhD
[email protected]@bobehayesbusinessoverbroadway.com/blog