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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

Dr. Bob Hayes Big Data and the Total Customer Experience

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In this presentation, Bob Hayes delivers an overview of his upcoming ebook "TCE - Total Customer Experience: Building Business Through Customer-Centric Measurement and Analytics." How can companies gain deeper customer insights to help them improve the customer experience and increase customer loyalty?

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Page 1: Dr. Bob Hayes Big Data and the Total Customer Experience

© 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  

Page 2: Dr. Bob Hayes Big Data and the Total Customer Experience

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Introductions

David Morris, Host Big Data Analytics Marketing – Cetas, By VMware

[email protected]

@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

Page 3: Dr. Bob Hayes Big Data and the Total Customer Experience

3

EMC VMware

Pivotal

•  Greenplum •  Gemfire •  Cetas •  Pivotal Labs

New Company

April 24th

Page 4: Dr. Bob Hayes Big Data and the Total Customer Experience

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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

[email protected] @bobehayes businessoverbroadway.com/blog

Page 5: Dr. Bob Hayes Big Data and the Total Customer Experience

How may we help? [email protected] Spring 2013

Improving the Customer Experience Using Big Data, Customer-Centric

Measurement and Analytics Bob E. Hayes, PhD

Page 6: Dr. Bob Hayes Big Data and the Total Customer Experience

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

Page 7: Dr. Bob Hayes Big Data and the Total Customer Experience

TCELab

Copyright 2013 TCELab

     

Customer  Experience,  Customer  Experience  Management  

and  Customer  Loyalty  

Page 8: Dr. Bob Hayes Big Data and the Total Customer Experience

TCELabCustomer Experience Management (CEM)

The process of understanding and managing your customers’ interactions with and perceptions of your brand / company

Copyright 2013 TCELab

Page 9: Dr. Bob Hayes Big Data and the Total Customer Experience

TCELab

Copyright 2013 TCELab

Optimal Customer Relationship Survey

Page 10: Dr. Bob Hayes Big Data and the Total Customer Experience

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

Page 11: Dr. Bob Hayes Big Data and the Total Customer Experience

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

Page 12: Dr. Bob Hayes Big Data and the Total Customer Experience

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

Page 13: Dr. Bob Hayes Big Data and the Total Customer Experience

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

Page 14: Dr. Bob Hayes Big Data and the Total Customer Experience

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

Page 15: Dr. Bob Hayes Big Data and the Total Customer Experience

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

Page 16: Dr. Bob Hayes Big Data and the Total Customer Experience

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    

Page 17: Dr. Bob Hayes Big Data and the Total Customer Experience

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

Page 18: Dr. Bob Hayes Big Data and the Total Customer Experience

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

Page 19: Dr. Bob Hayes Big Data and the Total Customer Experience

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  

Page 20: Dr. Bob Hayes Big Data and the Total Customer Experience

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.  

Page 21: Dr. Bob Hayes Big Data and the Total Customer Experience

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?  

Page 22: Dr. Bob Hayes Big Data and the Total Customer Experience

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?  

Page 23: Dr. Bob Hayes Big Data and the Total Customer Experience

TCELab

Copyright 2013 TCELab

     

Big  Data,  Analy/cs  and  Integra/on    

Page 24: Dr. Bob Hayes Big Data and the Total Customer Experience

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

Page 25: Dr. Bob Hayes Big Data and the Total Customer Experience

TCELabBig Data Landscape – bigdatalandscape.com

Copyright 2013 TCELab

Page 26: Dr. Bob Hayes Big Data and the Total Customer Experience

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

Page 27: Dr. Bob Hayes Big Data and the Total Customer Experience

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

Page 28: Dr. Bob Hayes Big Data and the Total Customer Experience

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.

Page 29: Dr. Bob Hayes Big Data and the Total Customer Experience

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

Page 30: Dr. Bob Hayes Big Data and the Total Customer Experience

TCELabData Integration is Key to Extracting Value

Copyright 2013 TCELab

Page 31: Dr. Bob Hayes Big Data and the Total Customer Experience

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

Page 32: Dr. Bob Hayes Big Data and the Total Customer Experience

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

Page 33: Dr. Bob Hayes Big Data and the Total Customer Experience

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

Page 34: Dr. Bob Hayes Big Data and the Total Customer Experience

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

Page 35: Dr. Bob Hayes Big Data and the Total Customer Experience

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

Page 36: Dr. Bob Hayes Big Data and the Total Customer Experience

TCELabIdentify Operational Drivers of Satisfaction

Copyright 2013 TCELab

Page 37: Dr. Bob Hayes Big Data and the Total Customer Experience

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

Page 38: Dr. Bob Hayes Big Data and the Total Customer Experience

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

Page 39: Dr. Bob Hayes Big Data and the Total Customer Experience

[email protected] @bobehayes businessoverbroadway.com/blog

How may we help? [email protected] 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

Page 40: Dr. Bob Hayes Big Data and the Total Customer Experience

40

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Page 41: Dr. Bob Hayes Big Data and the Total Customer Experience

41

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Page 42: Dr. Bob Hayes Big Data and the Total Customer Experience

42

Find the recording of this webinar and PDF at:

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Page 43: Dr. Bob Hayes Big Data and the Total Customer Experience

© 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  

Page 44: Dr. Bob Hayes Big Data and the Total Customer Experience

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

Page 45: Dr. Bob Hayes Big Data and the Total Customer Experience

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

Page 46: Dr. Bob Hayes Big Data and the Total Customer Experience

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