dr. bob hayes big data and the total customer experience
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© 2009 VMware Inc. All rights reserved
Big Data Thought Leadership Webinar
Web: www.cetas.net Twi)er: @CetasAnaly/cs Blog: www.cetas.net/blog YouTube: www.youtube.com/CetasAnaly/cs
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
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EMC VMware
Pivotal
• Greenplum • Gemfire • Cetas • Pivotal Labs
New Company
April 24th
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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
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
TCELabTCE: 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
TCELab
Copyright 2013 TCELab
Customer Experience, Customer Experience Management
and Customer Loyalty
TCELabCustomer Experience Management (CEM)
The process of understanding and managing your customers’ interactions with and perceptions of your brand / company
Copyright 2013 TCELab
TCELab
Copyright 2013 TCELab
Optimal Customer Relationship Survey
TCELabCustomer 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
TCELabFour 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
TCELabCustomer 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
TCELabCustomer Loyalty Measurement Framework
Loyalty Types Emo9onal Behavioral
Measuremen
t App
roach
Objec9v
e
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
Subjec9v
e (S
urve
y Q
uest
ions
)
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
TCELabCustomer 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 10 5 1 2 3 4 6 7 8 9
Extremely Dissatisfied
Extremely Satisfied
Neither Satisfied Nor Dissatisfied
TCELabCustomer 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 10 5 1 2 3 4 6 7 8 9
Extremely Dissatisfied
Extremely Satisfied
Neither Satisfied Nor Dissatisfied
TCELabCX 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
Percen
t of V
ariability (R
2 ) in
Custom
er
Loyalty
Explained
by CX
Que
s9on
s 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
TCELab
• 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
TCELabRelative 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
TCELabRPA 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
Percen
t of V
ariability (R
2 ) in Cu
stom
er
Loyalty
Explained
by Gen
eral CX Que
s9on
s and
Re
la9v
e Pe
rforman
ce Assessm
ent (RP
A)
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
TCELabUnderstanding 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.
TCELabAdditional 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?
TCELabSummary: 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?
TCELab
Copyright 2013 TCELab
Big Data, Analy/cs and Integra/on
TCELabBig 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
TCELabBig Data Landscape – bigdatalandscape.com
Copyright 2013 TCELab
TCELabThree 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
TCELabValue 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
TCELabValue 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.
TCELabDisparate 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
TCELabData Integration is Key to Extracting Value
Copyright 2013 TCELab
TCELabLinkage 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
TCELab
Customer Feedback Data Sources Relationship
Survey (satisfaction/loyalty to
company)
Transactional Survey
(satisfaction with specific transaction/interaction)
Social Media/ Communities
(sentiment / shares / likes)
Business D
ata Sources
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
TCELabCustomer 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
TCELabDetermine ROI of Increasing Customer Loyalty
Disloyal (0-5) Loyal ( 6-8) Very Loyal (9-10)
Perc
ent P
urch
asin
g A
dditi
onal
Sof
twar
e
Customer Loyalty
55% increase
Copyright 2013 TCELab
TCELabOperational / 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
TCELabIdentify Operational Drivers of Satisfaction
Copyright 2013 TCELab
TCELabIdentify Operational Standards
1 call 2-‐3 calls 4-‐5 calls 6-‐7 calls 8 or more calls
Sat w
ith SR
Number of Calls to Resolve SR
1 change 2 changes 3 changes 4 changes 5+ changes
Sat w
ith S
R
Number of SR Ownership Changes
Copyright 2013 TCELab
TCELab3 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
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
Big Data Thought Leadership Webinar Series
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Karl M. Kapp “Gamification: Leveraging Game Strategies to Drive Business” Wednesday, May 15, 2013 10:00 am PT/ 1:00 pm ET
dmorris@vmware.com
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Find the recording of this webinar and PDF at:
www.cetas.net/webinars
© 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
www.cetas.net/webinars
TCELabRAPID 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
TCELabFinancial 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
TCELabOperational 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
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