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How may we help?info@tcelab.comSpring 2013Big Data: What it Really Means for VoC andCustomer Experience ProfessionalsBob E...
“Big Data” is Everywherehttp://www.evl.uic.edu/cavern/rg/20040525_renambot/Viz/parallel_volviz/paging_outofcore_viz97.pdf
Three Vs of Big DataINFOGRAPHIC from Domo June 2012VolumeVelocityVarietyhttp://blogs.gartner.com/doug-laney/files/2012/01/...
Big Interest in Big Data: Google Trends0204060801001202010-01-03 -2010-01-092010-06-20 -2010-06-262010-12-05 -2010-12-1120...
Big Data DefinitionAn amalgamation of differentareas* that help us get ahandle on, insight fromand use out of data* includ...
Big Data Landscape – bigdatalandscape.com
Emerging Technologies Hype Cycle
Big Data for Business – Getting Value5 High Value Use Cases*1. ExplorationFinding, visualizing and understanding alldata t...
Analytics
Value from Analytics: MIT / IBM 2010 StudyTop-performingorganizationsuse analytics fivetimes more thanlower performershttp...
Value from Analytics: Accenture 2012 StudyCopyright 2013 TCELab1. Focus on Strategic Issues - only 39%said that the data t...
Data Integration is Key to Extracting Value96%72%51% 50%0%10%20%30%40%50%60%70%80%90%100%Percent of VOC executiveswho are ...
Data in Customer Experience Management1.Call handling time2.Number of calls untilresolution3.Response time1.Revenue2.Numbe...
Integrate Data to Answer Different Questions• Linkage analysis answers the questions:– What is the $ value of improving cu...
Integrating your Business DataCustomer Feedback Data SourcesRelationshipSurvey(satisfaction/loyalty tocompany)Transactiona...
Selecting Your First Big Data Project• Identify/Discover all your data• Define your problem / Establish acompelling use ca...
Patient Experience Example – US Hospitals• Identify all your data (Medicare)– Patient Experience, Health Outcomes,Process ...
Data Integration in US HealthcarePatientExperienceHealthOutcomesProcess(Operational)Financial• OverallSatisfaction• Likeli...
PX for US Hospitals
Survival Rate for US Hospitals
Medicare Spend for US Hospitals by State
Medicare Spend and Patient Experience
Data Veracity – Accuracy and Truthfulness• Have ahypothesis(es)• Know whereyour datacome from• Consider theeffect size• Av...
Problem of Common Method Variance• Correlations between variables are drivenby the method of measurement• Correlation betw...
Customer Loyalty Measurement FrameworkLoyalty TypesEmotional BehavioralMeasurementApproachObjectiveADVOCACY• Number/Percen...
Implications• Ask and answer bigger questions aboutyour customers– Explore all business data to understand theirimpact on ...
bob@tcelab.com@bobehayesbusinessoverbroadway.com/blogHow may we help?info@tcelab.comSpring 2013Big Data: What it Really Me...
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Big Data - What it Really Means for VOC and Customer Experience Professionals

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This is a talk I gave at VOCFusion on the topic of Big Data and how it applied to the world of Voice of the Customer and Customer Experience Management

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Big Data - What it Really Means for VOC and Customer Experience Professionals

  1. 1. How may we help?info@tcelab.comSpring 2013Big Data: What it Really Means for VoC andCustomer Experience ProfessionalsBob E. Hayes, PhD
  2. 2. “Big Data” is Everywherehttp://www.evl.uic.edu/cavern/rg/20040525_renambot/Viz/parallel_volviz/paging_outofcore_viz97.pdf
  3. 3. Three Vs of Big DataINFOGRAPHIC from Domo June 2012VolumeVelocityVarietyhttp://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf
  4. 4. Big Interest in Big Data: Google Trends0204060801001202010-01-03 -2010-01-092010-06-20 -2010-06-262010-12-05 -2010-12-112011-05-22 -2011-05-282011-11-06 -2011-11-122012-04-22 -2012-04-282012-10-07 -2012-10-132013-03-24 -2013-03-30SearchVolumeIndexCustomer ExperienceBig DataScale is based on the average worldwide traffic of Customer Experience and Big Datafrom January 2010 to April 2013.
  5. 5. Big Data DefinitionAn amalgamation of differentareas* that help us get ahandle on, insight fromand use out of data* includes technology (Storage, Data Management, BI Reporting) and analytics
  6. 6. Big Data Landscape – bigdatalandscape.com
  7. 7. Emerging Technologies Hype Cycle
  8. 8. Big Data for Business – Getting Value5 High Value Use Cases*1. ExplorationFinding, visualizing and understanding alldata to improve business knowledge to makebetter decisions2. 360 degree viewof customerUnified view that incorporates both internaland external sources of customer data3. Security andIntelligenceDetect threat & fraud, Governance & RiskManagement, Monitor cyber-security4. OperationalAnalysisLeveraging machine data to improve results,Reduce resource costs5. Data WarehouseAugmentationAdding technology to data warehouse toincrease operational efficiencies and exploremore data.* Based on IBM’s review of over 100 real use cases: www.ibmbigdatahub.com/podcast/top-5-big-data-use-cases
  9. 9. Analytics
  10. 10. Value from Analytics: MIT / IBM 2010 StudyTop-performingorganizationsuse analytics fivetimes more thanlower performershttp://sloanreview.mit.edu/the-magazine/2011-winter/52205/big-data-analytics-and-the-path-from-insights-to-value/
  11. 11. Value from Analytics: Accenture 2012 StudyCopyright 2013 TCELab1. Focus on Strategic Issues - only 39%said that the data they generate is"relevant to the business strategy"2. Measure Right Customer Metrics - only20% were very satisfied with the businessoutcomes of their existing analyticsprograms3. Integrate Business Metrics - Half of theexecutives indicated that data integrationremains a key challenge to them.http://www.accenture.com/us-en/Pages/insight-analytics-action.aspx
  12. 12. Data Integration is Key to Extracting Value96%72%51% 50%0%10%20%30%40%50%60%70%80%90%100%Percent of VOC executiveswho are satisfied withprogramCustomer loyalty percentilerank (within industry)PercentofVOCExecutives/CustomerLoyaltyPercentileRankOps Linkage AnalysisNo Ops Linkage Analysis
  13. 13. Data in Customer Experience Management1.Call handling time2.Number of calls untilresolution3.Response time1.Revenue2.Number of productspurchased3.Customer tenure4.Service contractrenewal5.Number of salestransactions6.Frequency ofpurchases1.Customer Loyalty2.Relationshipsatisfaction3.Transaction satisfaction1.Employee Loyalty2.Satisfaction withbusiness areasOperationalPartner Feedback1.Partner Loyalty2.Satisfaction withpartnering relationshipCustomerFeedbackEmployeeFeedbackFinancial
  14. 14. Integrate Data to Answer Different Questions• Linkage analysis answers the questions:– What is the $ value of improving customersatisfaction/loyalty?– Which operational metrics have the biggest impact oncustomer satisfaction/loyalty?– Which employee/partner factors have the biggest impact oncustomer satisfaction/loyalty?OperationalMetricsTransactionalSatisfactionRelationshipSatisfaction/LoyaltyFinancialBusinessMetricsConstituencySatisfaction/Loyalty
  15. 15. Integrating your Business DataCustomer Feedback Data SourcesRelationshipSurvey(satisfaction/loyalty tocompany)TransactionalSurvey(satisfaction with specifictransaction/interaction)Social Media/Communities(sentiment / shares / likes)BusinessDataSourcesFinancial(revenue, number ofsales)• Link data at customerlevel• Quality of therelationship (sat, loyalty)impacts financial metricsN/A• Link data at customer level• Quality of relationship(sentiment / likes / shares)impacts financial metricsOperational(call handling, responsetime)N/A• Link data at transactionlevel• Operational metrics impactquality of the transaction• Link data at transactionlevel• Operational metrics impactsentiment / likes/ sharesConstituency(employee / partnerfeedback)• Link data at constituencylevel• Constituency satisfactionimpacts customersatisfaction with overallrelationship• Link data at constituencylevel• Constituency satisfactionimpacts customersatisfaction with interaction• Link data at constituencylevel• Constituency satisfactionimpacts customersentiment / likes / shares
  16. 16. Selecting Your First Big Data Project• Identify/Discover all your data• Define your problem / Establish acompelling use case– Establish ROI• Select people before technology• Don’t introduce too many new skillsBased on: http://www.ibmbigdatahub.com/blog/selecting-your-first-big-data-project
  17. 17. Patient Experience Example – US Hospitals• Identify all your data (Medicare)– Patient Experience, Health Outcomes,Process Metrics, Financial• Define your Problem– What can hospitals do to improve patientexperience?– Does amount of hospital spend impact patientsatisfaction/loyalty?
  18. 18. Data Integration in US HealthcarePatientExperienceHealthOutcomesProcess(Operational)Financial• OverallSatisfaction• Likelihood torecommend• 8 dimensions• Nurse comm.• Doctor comm.• Room Quiet• MortalityRate• Re-admissionRate• SafetyMeasures• MedicareSpend• US Federal Government (Medicare) tracksseveral metrics across US Hospitals
  19. 19. PX for US Hospitals
  20. 20. Survival Rate for US Hospitals
  21. 21. Medicare Spend for US Hospitals by State
  22. 22. Medicare Spend and Patient Experience
  23. 23. Data Veracity – Accuracy and Truthfulness• Have ahypothesis(es)• Know whereyour datacome from• Consider theeffect size• Avoid cherrypicking results/ Be aware ofbiases
  24. 24. Problem of Common Method Variance• Correlations between variables are drivenby the method of measurement• Correlation between customer experienceand recommending behavior:– CX and Likelihood to recommend: r = .52– CX and Number of friends/colleagues: r = .28• Consider using objective loyalty metricswww.businessoverbroadway.com/is-the-importance-of-customer-experience-over-inflated
  25. 25. Customer Loyalty Measurement FrameworkLoyalty TypesEmotional BehavioralMeasurementApproachObjectiveADVOCACY• Number/Percent of newcustomersRETENTION• Churn rates• Service contract renewal ratesPURCHASING• Usage Metrics – Frequency ofuse/ visit, Page views• Sales Records - Number ofproducts purchasedSubjective(SurveyQuestions)ADVOCACY• Overall satisfaction• Likelihood to recommend• Likelihood to buy same product• Level of trust• Willing to forgive• Willing to considerRETENTION• Likelihood to renew service contract• Likelihood to leavePURCHASING• Likelihood to buy different/additional products• Likelihood to expand usage1 Using RAPID Loyalty Approach - Overall satisfaction rated on a scale from 0 (Extremely Dissatisfied) to 10 (Extremely Satisfied). Other questions arerated 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 RetentionLoyalty. Copyright 2013 TCELab
  26. 26. Implications• Ask and answer bigger questions aboutyour customers– Explore all business data to understand theirimpact on customer experience / loyalty• Build your company around yourcustomers– Greenplum social network platform– Cross-functional teams working together tosolve customer problems• Use objective, “real,” loyalty metrics
  27. 27. bob@tcelab.com@bobehayesbusinessoverbroadway.com/blogHow may we help?info@tcelab.comSpring 2013Big Data: What it Really Means for VoC andCustomer Experience ProfessionalsBob E. Hayes, PhD

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