Dr. Bob Hayes Big Data and the Total Customer Experience


Published on

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?

Published in: Technology
  • Be the first to comment

  • Be the first to like this

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

Dr. Bob Hayes Big Data and the Total Customer Experience

  1. 1. Big Data Thought Leadership WebinarWeb:    www.cetas.net  Twi)er:    @CetasAnaly/cs  Blog:    www.cetas.net/blog  YouTube:  www.youtube.com/CetasAnaly/cs   © 2009 VMware Inc. All rights reserved
  2. 2. IntroductionsDavid Morris, HostBig Data Analytics Marketing – Cetas, By VMwaredmorris@vmware.com@jdavidmorris Today’s Thought Leadership Webinar: Improving the Customer Experience Using Big Data, Customer-Centric Measurement and Analytics Please submit your questions at anytime throughout the webinar via the chat tool.2
  3. 3. New Company EMC VMware Pivotal April 24th •  Greenplum •  Gemfire •  Cetas •  Pivotal Labs3
  4. 4. 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/blog4
  5. 5. Improving the Customer Experience Using Big Data, Customer-Centric How may we help? info@tcelab.com Measurement and Analytics Spring 2013 Bob E. Hayes, PhD
  6. 6. TCE: Total Customer Experience TCE Lab 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 Copyright 2013 TCELab
  7. 7. TCE Lab       Customer  Experience,  Customer  Experience  Management   and  Customer  Loyalty   Copyright 2013 TCELab
  8. 8. Customer Experience Management (CEM) TCE LabThe process ofunderstanding andmanaging yourcustomers’interactions withand perceptionsof your brand /company Copyright 2013 TCELab
  9. 9. TCE Lab Optimal CustomerRelationship Survey Copyright 2013 TCELab
  10. 10. Customer Relationship Surveys TCE Lab•  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 Copyright 2013 TCELab
  11. 11. Four Parts to Customer Surveys TCE Lab1.  Customer Loyalty – likelihood of customers engaging in positive behaviors2.  Customer Experience – satisfaction with important touch points3.  Relative Performance – your competitive advantage4.  Additional Questions – Extra value- added questions Copyright 2013 TCELab
  12. 12. Customer Loyalty Types TCE Lab The degree to which customers experience positive feelings for and engage in positive behaviors toward a company/brand Emotional Behavioral (Advocacy) (Retention, Purchasing) Love, Consider, Stay, Renew, Buy, Forgive, Trust Buy more often, Expand usage Copyright 2013 TCELab
  13. 13. Customer Loyalty Measurement Framework TCE Lab Loyalty  Types   Emo9onal   Behavioral   RETENTION Measurement  Approach   •  Churn  rates   Objec9ve   •  Service  contract  renewal  rates   ADVOCACY •  Number/Percent  of  new   PURCHASING customers   •  Usage  Metrics  –  Frequency  of   use/  visit,  Page  views   •  Sales  Records  -­‐  Number  of   products  purchased   RETENTION (Survey Questions)   ADVOCACY   •  Likelihood  to  renew  service  contract   Subjec9ve   •  Overall  sa/sfac/on   •  Likelihood  to  leave   •  Likelihood  to  recommend   •  Likelihood  to  buy  same  product   PURCHASING   •  Level  of  trust   •  Likelihood  to  buy  different/   •  Willing  to  forgive   addi/onal  products   •  Willing  to  consider   •  Likelihood  to  expand  usage  1 Using RAPID Loyalty Approach - Overall satisfaction rated on a scale from 0 (Extremely Dissatisfied) to 10 (Extremely Satisfied). Other questionsare 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 ofRetention Loyalty. Copyright 2013 TCELab
  14. 14. Customer Experience TCE Lab•  Two  types  of  customer  experience  ques/ons  •  Overall, how satisfied Extremely Dissatisfied Neither Satisfied Nor Dissatisfied Extremely Satisfied are you with… 0 1 2 3 4 5 6 7 8 9 10 Area   General  CX  Ques9ons   Specific  CX  Ques9ons   1. Reliability of product 2. Features of product Product 1. Product Quality 3. Ease of using the product 4. Availability of product 1. Knowledge of your industry Account 2. Sales / Account 2. Ability to coordinate resources Management Management 3. Understanding of your business issues 4. Responds quickly to my needs 1. Timeliness of solution provided Technical 2. Knowledge and skills of personnel 3. Technical Support 3. Effectiveness of solution provided Support 4. Online tools and services Copyright 2013 TCELab
  15. 15. Customer Experience TCE Lab•  Overall,  how  sa9sfied  are  you  with  each  area?   Extremely Neither Satisfied Extremely Dissatisfied Nor Dissatisfied Satisfied 0 1 2 3 4 5 6 7 8 9 10 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 Copyright 2013 TCELab
  16. 16. CX Predicting Customer Loyalty TCE Lab 100%   1.  General  CX   Specific  CX  Ques/ons  Loyalty  Explained  by  CX  Ques9ons   90%   4%   ques9ons  explain   General  CX  Ques/ons   Percent  of  Variability  (R2)  in   80%   0%   customer  loyalty   differences  well.   70%     2%   Customer     60%   2.  Specific  CX   50%   4%   ques9ons  do  not  add   85%   much  to  our  predic9on   40%   74%   of  customer  loyalty   30%   60%   differences.   20%   42%     3.  On  average,  each   10%   Specific  CX  ques9on   0%   explains  <  .5%  of   Company  A   Company  B   Company  C   Company  D   variability  in  customer   7  General  CX   5  General  CX   6  General  CX   7  General  CX   loyalty.   0  Specific  CX     14  Specific  CX     27  Specific  CX     34  Specific  CX     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. Copyright 2013 TCELab
  17. 17. Competitive Analytics TCE Lab•  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)   Copyright 2013 TCELab
  18. 18. Relative Performance Assessment (RPA) TCE Lab•  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
  19. 19. RPA Predicting Customer Loyalty TCE Lab §  What  best  describes  our  performance  compared  to   the  compe9tors  you  use?   100%   1.  General  CX  ques9ons  Loyalty  Explained  by  General  CX  Ques9ons  and   90%   explain  purchasing   Rela9ve  Performance  Assessment  (RPA)   Percent  of  Variability  (R2)    in  Customer     loyalty  differences  well.   80%   2%     1%   1  RPA  Ques/on   70%   2.  Rela9ve  Performance   7  General  CX  Ques/ons   Assessment  improved   60%   the  predictability  of   50%   purchasing  loyalty  by   40%   almost  50%   69%   72%   30%   8%     7%   1%   3.  Improving  company’s   20%   ranking  against  the   10%   18%   16%   14%   compe99on  will   0%   improve  purchasing   Overall   Recommend   Purchase   Expand  usage   Renew   Sa/sfac/on   different/new   Subscrip/on   loyalty  and  share  of   solu/ons   wallet   Loyalty  Ques9ons   Copyright 2013 TCELab
  20. 20. Understanding your Ranking TCE Lab1.  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.   Copyright 2013 TCELab
  21. 21. Additional Questions TCE Lab•  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?   Copyright 2013 TCELab
  22. 22. Summary: Your Relationship Survey TCE Lab1.  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?   Copyright 2013 TCELab
  23. 23. TCE Lab      Big  Data,  Analy/cs  and  Integra/on     Copyright 2013 TCELab
  24. 24. Big Data TCE Lab•  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
  25. 25. TCEBig Data Landscape – bigdatalandscape.com Lab Copyright 2013 TCELab
  26. 26. Three Big Data Approaches TCE Lab1.  Interactive Exploration - good for discovering real-time patterns from your data as they emerge2.  Direct Batch Reporting - good for summarizing data into pre-built, scheduled (e.g., daily, weekly) reports3.  Batch ETL (extract-transform-load) - good for analyzing historical trends or linking disparate data Copyright 2012 TCELab
  27. 27. Value from Analytics: MIT / IBM 2010 Study TCE Lab 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 2013 TCELab
  28. 28. Value from Analytics: Accenture 2012 Study TCE Lab1.  Measure Right Customer Metrics - only 20% were very satisfied with the business outcomes of their existing analytics programs2.  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. Copyright 2013 TCELab
  29. 29. Disparate Sources of Business Data TCE Lab Customer Operational Feedback Financial1. Call  handling  /me   1. Customer  Loyalty  2. Number  of  calls  un/l   2. Rela/onship  sa/sfac/on   resolu/on   3. Transac/on  sa/sfac/on   1. Revenue  3. Response  /me   4. Sen/ment   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   transac/ons  2. Sa/sfac/on  with   2. Sa/sfac/on  with   6. Frequency  of   partnering  rela/onship   business  areas   purchases   Copyright 2013 TCELab
  30. 30. Data Integration is Key to Extracting Value TCE Lab Copyright 2013 TCELab
  31. 31. Linkage Analysis TCE Lab Opera/onal   Transac/onal   Metrics   Sa/sfac/on   Rela/onship   Financial   Sa/sfac/on/   Business   Loyalty   Metrics   Cons/tuency   Sa/sfac/on/   Loyalty   Copyright 2013 TCELab
  32. 32. Integrating your Business Data TCE Lab Customer Feedback Data Sources Relationship Transactional Social Media/ Survey Survey Communities (satisfaction/loyalty to (satisfaction with specific (sentiment / shares / likes) company) transaction/interaction) • Link data at customer level • Link data at customer level FinancialBusiness Data Sources • Quality of the N/A • Quality of relationship (revenue, number of sales) relationship (sat, loyalty) (sentiment / likes / shares) impacts financial metrics impacts financial metrics • Link data at transaction • Link data at transaction level level Operational (call handling, response N/A • Operational metrics impact • Operational metrics impact time) quality of the transaction sentiment / likes/ shares • Link data at constituency • Link data at constituency • Link data at constituency level level level Constituency • Constituency satisfaction • Constituency satisfaction • Constituency satisfaction (employee / partner feedback) impacts customer impacts customer impacts customer satisfaction with overall satisfaction with interaction sentiment / likes / shares relationship Copyright 2013 TCELab
  33. 33. Customer Feedback / Financial Linkage TCE Lab 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 2013 TCELab
  34. 34. TCEDetermine ROI of Increasing Customer Loyalty Lab Percent Purchasing Additional Software 55% increase Disloyal (0-5) Loyal ( 6-8) Very Loyal (9-10) Customer Loyalty Copyright 2013 TCELab
  35. 35. Operational / Customer Feedback Linkage TCE Lab 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." Copyright 2013 TCELab
  36. 36. Identify Operational Drivers of Satisfaction TCE Lab Copyright 2013 TCELab
  37. 37. Identify Operational Standards TCE Lab 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 2013 TCELab
  38. 38. 3 Implications of Big Data in CEM TCE Lab1.  Ask/Answer bigger questions2.  Build company around the customer3.  Predict real customer loyalty behaviors Copyright 2012 TCELab
  39. 39. For more info on book: bob@tcelab.com http://bit.ly/tcebook @bobehayes businessoverbroadway.com/blogImproving the Customer Experience Using Big Data, Customer-Centric How may we help? info@tcelab.com Measurement and Analytics Spring 2013 Bob E. Hayes, PhD
  40. 40. Big Data Thought Leadership Webinar SeriesMay’s Big Data Thought Leader: Karl M. Kapp “Gamification: Leveraging Game Strategies to Drive Business” Wednesday, May 15, 2013 10:00 am PT/ 1:00 pm ET Register Today, as space is limited for this premium webinar www.cetas.net/webinars dmorris@vmware.com40
  41. 41. Sign-up today for FREE Analytics @ www.cetas.net ! Cetas Big Data Analytics Free Trial Monetize Your Big Data Today!41
  42. 42. Find the recording of this webinar and PDF at: www.cetas.net/webinars42
  43. 43. Big Data Thought Leadership Webinar Series INSTANT INTELLIGENCE Live  Webinar  Registra9on  and  Recorded   Webinars  available  at     www.cetas.net/webinars  Web:  www.cetas.net  Twi)er:  @CetasAnaly/cs  Blog:  www.cetas.net/blog  YouTube:  www.youtube.com/CetasAnaly/cs   © 2009 VMware Inc. All rights reserved
  44. 44. RAPID Loyalty Measurement TCE Lab •  Assesses three components of customer loyalty Index Definition Survey Questions The  degree  to  which  customers  will   Likelihood  to  switch  to  another  company*   Reten9on     remain  as  a  customer/not  leave  to   Likelihood  to  purchase  from  compe/tor*   Loyalty   compe/tor  (0  –  low  loyalty  to  10  –  Index  (RLI)   Likelihood  to  stop  purchasing*   high  loyalty)   The  degree  to  which  customers  feel   Overall  sa/sfac/on   Advocacy   posi/vely  toward/will  advocate  your   Likelihood  to  choose  again  for  first  /me   Loyalty   product/service/brand  (0  –  low  loyalty   Likelihood  to  recommend  (NPS)  Index  (ALI)   to  10  –  high  loyalty)   Likelihood  to  purchase  same  product/service  Purchasing   The  degree  to  which  customers  will   Likelihood  to  purchase  different  products/services   Loyalty   increase  their  purchasing  behavior  (0  –   Likelihood  to  expand  usage  throughout  company  Index  (PLI)   low  loyalty  to  10  –  high  loyalty)   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. Copyright 2013 TCELab
  45. 45. Financial Metrics / Real Loyalty Behaviors TCE Lab•  Linkage analysis helps us determine if our customer feedback metrics predict real and measurable business outcomes•  Retention Rela/onship   Financial   Sa/sfac/on/   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 Copyright 2013 TCELab
  46. 46. Operational Metrics TCE Lab•  Linkage analysis helps us determine/identify the operational factors that influence customer satisfaction/loyalty Opera/onal   Transac/onal  •  Support Metrics Metrics   Sa/sfac/on   –  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