Big Data - What it Really Means for VOC and Customer Experience Professionals
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This document discusses how big data can help customer experience (CX) professionals. It defines big data as large, complex data sets that are difficult to analyze using traditional methods. Big data has three characteristics - volume, velocity, and variety. The document outlines five high-value uses of big data for businesses: exploration, obtaining a 360-degree view of customers, security/intelligence, operational analysis, and augmenting data warehouses. It emphasizes the importance of integrating different data sources to gain insights and measure the impact of various factors like operations and employee/partner satisfaction on customer loyalty. The final sections provide a framework for customer loyalty measurement and discuss selecting an initial big data project.
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Big Data - What it Really Means for VOC and Customer Experience Professionals
- 1. How may we help? info@tcelab.com Spring 2013 Big Data: What it Really Means for VoC and Customer Experience Professionals Bob E. Hayes, PhD
- 2. “Big Data” is Everywhere http://www.evl.uic.edu/cavern/rg/20040525_renambot/Viz/parallel_volviz/paging_outofcore_viz97.pdf
- 3. Three Vs of Big Data INFOGRAPHIC from Domo June 2012 Volume Velocity Variety http://blogs.gartner.com/doug- laney/files/2012/01/ad949-3D-Data- Management-Controlling-Data- Volume-Velocity-and-Variety.pdf
- 4. Big Interest in Big Data: Google Trends 0 20 40 60 80 100 120 2010-01-03 - 2010-01-09 2010-06-20 - 2010-06-26 2010-12-05 - 2010-12-11 2011-05-22 - 2011-05-28 2011-11-06 - 2011-11-12 2012-04-22 - 2012-04-28 2012-10-07 - 2012-10-13 2013-03-24 - 2013-03-30 SearchVolumeIndex Customer Experience Big Data Scale is based on the average worldwide traffic of Customer Experience and Big Data from January 2010 to April 2013.
- 5. Big Data Definition An amalgamation of different areas* that help us get a handle on, insight from and use out of data * includes technology (Storage, Data Management, BI Reporting) and analytics
- 6. Big Data Landscape – bigdatalandscape.com
- 7. Emerging Technologies Hype Cycle
- 8. Big Data for Business – Getting Value 5 High Value Use Cases* 1. Exploration Finding, visualizing and understanding all data to improve business knowledge to make better decisions 2. 360 degree view of customer Unified view that incorporates both internal and external sources of customer data 3. Security and Intelligence Detect threat & fraud, Governance & Risk Management, Monitor cyber-security 4. Operational Analysis Leveraging machine data to improve results, Reduce resource costs 5. Data Warehouse Augmentation Adding technology to data warehouse to increase operational efficiencies and explore more data. * Based on IBM’s review of over 100 real use cases: www.ibmbigdatahub.com/podcast/top-5-big-data-use-cases
- 9. Analytics
- 10. Value from Analytics: MIT / IBM 2010 Study Top-performing organizations use analytics five times more than lower performers http://sloanreview.mit.edu/the-magazine/2011- winter/52205/big-data-analytics-and-the-path-from- insights-to-value/
- 11. Value from Analytics: Accenture 2012 Study Copyright 2013 TCELab 1. Focus on Strategic Issues - only 39% said that the data they generate is "relevant to the business strategy" 2. Measure Right Customer Metrics - only 20% were very satisfied with the business outcomes of their existing analytics programs 3. Integrate Business Metrics - Half of the executives indicated that data integration remains a key challenge to them. http://www.accenture.com/us-en/Pages/insight-analytics-action.aspx
- 12. Data Integration is Key to Extracting Value 96% 72% 51% 50% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Percent of VOC executives who are satisfied with program Customer loyalty percentile rank (within industry) PercentofVOCExecutives/ CustomerLoyaltyPercentileRank Ops Linkage Analysis No Ops Linkage Analysis
- 13. Data in Customer Experience Management 1.Call handling time 2.Number of calls until resolution 3.Response time 1.Revenue 2.Number of products purchased 3.Customer tenure 4.Service contract renewal 5.Number of sales transactions 6.Frequency of purchases 1.Customer Loyalty 2.Relationship satisfaction 3.Transaction satisfaction 1.Employee Loyalty 2.Satisfaction with business areas Operational Partner Feedback 1.Partner Loyalty 2.Satisfaction with partnering relationship Customer Feedback Employee Feedback Financial
- 14. Integrate Data to Answer Different Questions • Linkage analysis answers the questions: – What is the $ value of improving customer satisfaction/loyalty? – Which operational metrics have the biggest impact on customer satisfaction/loyalty? – Which employee/partner factors have the biggest impact on customer satisfaction/loyalty? Operational Metrics Transactional Satisfaction Relationship Satisfaction/ Loyalty Financial Business Metrics Constituency Satisfaction/ Loyalty
- 15. Integrating your Business Data 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
- 16. Selecting Your First Big Data Project • Identify/Discover all your data • Define your problem / Establish a compelling use case – Establish ROI • Select people before technology • Don’t introduce too many new skills Based on: http://www.ibmbigdatahub.com/blog/selecting-your-first-big-data-project
- 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 patient experience? – Does amount of hospital spend impact patient satisfaction/loyalty?
- 18. Data Integration in US Healthcare Patient Experience Health Outcomes Process (Operational) Financial • Overall Satisfaction • Likelihood to recommend • 8 dimensions • Nurse comm. • Doctor comm. • Room Quiet • Mortality Rate • Re-admission Rate • Safety Measures • Medicare Spend • US Federal Government (Medicare) tracks several metrics across US Hospitals
- 19. PX for US Hospitals
- 20. Survival Rate for US Hospitals
- 21. Medicare Spend for US Hospitals by State
- 22. Medicare Spend and Patient Experience
- 23. Data Veracity – Accuracy and Truthfulness • Have a hypothesis(es) • Know where your data come from • Consider the effect size • Avoid cherry picking results / Be aware of biases
- 24. Problem of Common Method Variance • Correlations between variables are driven by the method of measurement • Correlation between customer experience and recommending behavior: – CX and Likelihood to recommend: r = .52 – CX and Number of friends/colleagues: r = .28 • Consider using objective loyalty metrics www.businessoverbroadway.com/is-the-importance-of-customer-experience-over-inflated
- 25. Customer Loyalty Measurement Framework Loyalty Types Emotional Behavioral MeasurementApproach Objective 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 Subjective (SurveyQuestions) ADVOCACY • Overall satisfaction • 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/ additional 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
- 26. Implications • Ask and answer bigger questions about your customers – Explore all business data to understand their impact on customer experience / loyalty • Build your company around your customers – Greenplum social network platform – Cross-functional teams working together to solve customer problems • Use objective, “real,” loyalty metrics
- 27. bob@tcelab.com @bobehayes businessoverbroadway.com/blog How may we help? info@tcelab.com Spring 2013 Big Data: What it Really Means for VoC and Customer Experience Professionals Bob E. Hayes, PhD
Notes de l'éditeur
- The term, Big Data, was coined by Michael Cox and David Ellsworth in a 1997 article for a conference on visualization. The term was more about the size of the data sets that taxed the memory of computer systems. When data didn’t fit in memory, the solution was to get more resources. It is the first article in the ACM digital library to use the term “big data.”
- Three Vs of Big data were first mentioned by Doug Laney in 2001 Volume – amount of data is massiveVelocity – speed at which data are being generated is very fastVariety– different types of data like structured and unstructuredVeracity – data must be accurate and truthfulVolume – humans create 2.5 quintillion bytes of data daily, 90% of today’s data has been generated in the past two years. created and machine createdVelocity – a study by 360i found that brands receive 350,000 Facebook likes per minute Velocity – 600,000 tweets per hour
- The term,Big Data, is more of an amalgamation of different areas that help us try to get a handle on, insight from and use out of data. The focus needs to be on the data and less about the big. Data has always been big. I wrote my first book on a Mac Plus. Each chapter required its own floppy disk.
- Kate Crawford, a Microsoft researcher and MIT professor, said that 2013 will be the year in which we reach the peak of the Big Data hype.According to Gartner’s hype cycle of emerging technologies,Big Data is headed toward it’s peak of inflated expectations and won’t reach the plateau of productivity for 2 to 5 years. The Plateau of Productivity represents the time when the technology finally delivers predictable value. The promise of Big Data, of course, is a treasure trove of high value across many industries – including healthcare. Everything from predictive and prescriptive analytics to population health, disease management, drug discovery and personalized medicine (delivered with much greater precision and higher efficacy) to name but a few.
- Accenture surveyed 600 executives from the US and UK about their use of analytics. They found that the adoption of analytics is up and continues to grow, ROI remains elusive. Strategies usually are about making decisions. And when we make a decision, we typically eliminate an alternative course of action. Tactics are usually much more flexible. Strategies are about “what” we choose to do. Tactics are about “how” we choose to do it. It is often easier to change the “how” we do things than the “what”.Strategies are the investments of resources that build and grow an organization. Tactics are the day to day actions that get us to our goals.
- When they linked their customer feedback to operational metrics, they got more value from the data as measured by executive satisfaction with the program and higher customer loyalty rankings within their industry.
- Linkage analysis helps answer important questions that help senior management better manage its business. 1. What is the $ value of improving customer satisfaction/loyalty?2. Which operational metrics have the biggest impact on customer satisfaction/loyalty?3. Which employee/partner factors have the biggest impact on customer satisfaction/loyalty?The bottom line is that linkage analysis helps the company understand the causes and consequences of customer satisfaction and loyalty and thereby helping senior manager better manage its business.Understand the causes and consequences of customer satisfaction/loyalty
- Avoid putting people on projects who are vested in the old way of doing things.
- Kate Crawford from MITBe concerned about common method variance – things are correlated only because they are measured by the same thing
- Customer Loyalty Measurement ApproachesObjectiveTime spent on web siteNumber products/servicespurchasedRenewed contractSubjective (self-reported)Likelihood to recommendLikelihood to continue purchasingLikelihood to renew contractHere is a figure that illustrates how different loyalty metrics fit into the larger customer loyalty measurementframework of loyalty types and measurement approaches. It is important to point out that the subjective measurement approach is not synonymous with emotional loyalty. Survey questions can be used to measure both emotional loyalty (e.g., overall satisfaction) as well as behavioral loyalty (e.g., likelihood to leave, likelihood to buy different products). In my prior research on measuring customer loyalty, I found that you can reliably and validly measure the different types of loyalty using survey questions.– I conducted several studies a few years ago, examining different types of customer loyalty questions. As the business models suggested, I found that loyalty questions generally fall into three types of loyalty behaviors. The table here includes these three types (Retention, Advocacy and Purchasing) and some survey questions for each type of loyalty.likelihood to switch providers (retention)likelihood to renew service contract (retention)likelihood to recommend (advocacy)overall satisfaction (advocacy)likelihood to purchase different solutions from <Company Name> (purchasing)likelihood to expand use of <Company Name’s> products throughout company (purchasing)These questions can be used as individual measures. If you are using multiple questions, you can calculate an average of the questions within a given type of loyalty. So, if you were using all six of these questions, you could calculate three scores, one for retention, one for advocacy and one for purchasing loyalty, each an average score of their corresponding questions.