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    Bobhayestcebigdatawebinar03272013 130417142258-phpapp01 Bobhayestcebigdatawebinar03272013 130417142258-phpapp01 Presentation Transcript

    • © 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
    • 3 EMC VMware Pivotal •  Greenplum •  Gemfire •  Cetas •  Pivotal Labs New Company April 24th
    • 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/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
    • TCE Lab TCE: 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
    • TCE Lab Copyright 2013 TCELab       Customer  Experience,   Customer  Experience  Management   and  Customer  Loyalty  
    • TCE Lab Customer Experience Management (CEM) The process of understanding and managing your customers’ interactions with and perceptions of your brand / company Copyright 2013 TCELab
    • TCE Lab Copyright 2013 TCELab Optimal Customer Relationship Survey
    • TCE Lab Customer 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
    • TCE Lab Four 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
    • TCE Lab Customer 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
    • TCE Lab Customer Loyalty Measurement Framework Loyalty  Types   Emo9onal   Behavioral   Measurement  Approach   Objec9ve   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   Subjec9ve   (SurveyQuestions)   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
    • TCE Lab Customer 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 1051 2 3 4 6 7 8 9 Extremely Dissatisfied Extremely Satisfied Neither Satisfied Nor Dissatisfied
    • TCE Lab Customer 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 1051 2 3 4 6 7 8 9 Extremely Dissatisfied Extremely Satisfied Neither Satisfied Nor Dissatisfied
    • TCE Lab CX 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   Percent  of  Variability  (R2)  in   Customer     Loyalty  Explained  by  CX  Ques9ons   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    
    • 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)   Competitive Analytics Copyright 2013 TCELab
    • TCE Lab Relative 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
    • TCE Lab RPA 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   Percent  of  Variability  (R2)    in  Customer     Loyalty  Explained  by  General  CX  Ques9ons  and   Rela9ve  Performance  Assessment  (RPA)   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  
    • TCE Lab Understanding 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.  
    • TCE Lab Additional 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?  
    • TCE Lab Summary: 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?  
    • TCE Lab Copyright 2013 TCELab       Big  Data,  Analy/cs  and  Integra/on    
    • TCE Lab 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 large, quickly-expanding, diverse data Copyright 2013 TCELab
    • TCE Lab Big Data Landscape – bigdatalandscape.com Copyright 2013 TCELab
    • TCE Lab 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
    • TCE Lab Value 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
    • TCE Lab Value 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.
    • TCE Lab Disparate 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
    • TCE Lab Data Integration is Key to Extracting Value Copyright 2013 TCELab
    • TCE Lab Linkage 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
    • TCE Lab Customer Feedback Data Sources Relationship Survey (satisfaction/loyalty to company) Transactional Survey (satisfaction with specific transaction/interaction) Social Media/ Communities (sentiment / shares / likes) BusinessDataSources 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
    • TCE Lab Customer 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
    • TCE Lab Determine ROI of Increasing Customer Loyalty Disloyal (0-5) Loyal ( 6-8) Very Loyal (9-10) PercentPurchasing AdditionalSoftware Customer Loyalty 55% increase Copyright 2013 TCELab
    • TCE Lab Operational / 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
    • TCE Lab Identify Operational Drivers of Satisfaction Copyright 2013 TCELab
    • TCE Lab Identify Operational Standards 1  call   2-­‐3  calls   4-­‐5  calls   6-­‐7  calls   8  or  more   calls   Sat  with  SR   Number  of  Calls  to  Resolve  SR   1 change 2 changes 3 changes 4 changes 5+ changes SatwithSR Number of SR Ownership Changes Copyright 2013 TCELab
    • TCE Lab 3 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 May’s Big Data Thought Leader: Register Today, as space is limited for this premium webinar www.cetas.net/webinars 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
    • 41 Cetas Big Data Analytics Free Trial Monetize Your Big Data Today! Sign-up today for FREE Analytics @ www.cetas.net !
    • 42 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  
    • TCE Lab RAPID 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
    • TCE Lab 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 Rela/onship   Sa/sfac/on/   Loyalty   Financial   Business   Metrics   Copyright 2013 TCELab
    • TCE Lab 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) Copyright 2013 TCELab Opera/onal   Metrics   Transac/onal   Sa/sfac/on