Big Data and Customer Experience


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These are the slides from a talk I gave at a Customer Experience Professionals Association event in San Mateo on February 26, 2014.

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  • Ask audience, “What do you think Big Data is?”
  • 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.
  • 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
  • Here is how a company can set up their data to identify customer-centric self- service metrics. For each service transaction, we have a corresponding satisfaction rating of that experience and the objective metrics behind that experience. We can then run analyses to show how the objective metrics are related to customer satisfaction with that experience.
  • Slide 11: Identifying Customer-Centric MetricsHere is the result of some hypothetical data that shows how objective metrics impact customer satisfaction with the experience. We see that customers who spent more time on the site were less satisfied with the experience than customers who spent less time on the site. Further, returning customers to the support site reported higher satisfaction with the experience compared to new customers to the support site.Manage customer relationships using objective operational metrics: By understanding and identifying which objective metrics are predictive of customer satisfaction with self service, you can use the objective metrics to help design the service experience to improve customer satisfaction. Predict customer satisfaction without surveys: Transactional survey response rates are typically low, around 10%. So, how do we know about the other 90% of customers who do not complete a survey? Based on our linkage analysis of these 10%, we can apply the predictive model to the other 90% to estimate customer satisfaction based solely on the objective metrics. Using web analytics of online behavior patterns, companies might be able to profile customers who are predicted to be dissatisfied and intervene during the transaction to either improve their service experience or ameliorate its negative impact.The bottom line is that you don’t need to rely solely on customer satisfaction ratings; if you haven’t yet, consider including objective metrics in your service measurement strategy.
  • 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
  • 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.
  • 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.
  • Big Data and Customer Experience

    1. 1. Big Data and Customer Experience How may we help? Bob E. Hayes, PhD Winter 2014
    2. 2. Three Vs of Big Data Volume Velocity Variety INFOGRAPHIC from Domo June 2012
    3. 3. Big Data Definition An amalgamation of different areas* that help us get a handle on, insight from and use out of data * includes technology (Data Capture, Storage & Management, BI Reporting) and analytics
    4. 4. Big Interest in Big Data: Google Trends Customer Experience Big Data Scale is based on the average worldwide traffic of Customer Experience and Big Data from January 2004 to January 2014.
    5. 5. Big Data Landscape – Apps Infrastructure Technologies
    6. 6. 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 5. Data Warehouse Augmentation Leveraging machine data to improve results, Reduce resource costs Adding technology to data warehouse to increase operational efficiencies and explore more data. * Based on IBM’s review of over 100 real use cases:
    7. 7. What do customers think about Big Data? 1. Investing - $8M 2. But not seeing improvements in decision-making 3. As data source, internal company sources rule over social media 4. Non-technical factors are key to Big Data success From: Big Data Trends for 2014 -
    8. 8. What do Big Data vendors think? 1.Analytics 2. Hadoop / Open Source 3. People 4. Data Integration 5. Privacy / Security 6. Applications 7. Data Veracity, Cloud, SQL From: The Big Picture of Big Data for 2014 -
    9. 9. Value from Analytics: MIT / IBM 2010 Study Top-performing organizations use analytics five times more than lower performers
    10. 10. Percent of VOC Executives / Customer Loyalty Percentile Rank Data Integration is Key to Extracting Value 100% 90% Ops Linkage Analysis No Ops Linkage Analysis 96% 80% 70% 72% 60% 50% 40% 51% 50% 30% 20% 10% 0% Percent of VOC executives Customer loyalty percentile who are satisfied with rank (within industry) program
    11. 11. Data in Customer Experience Management Operational 1. Call handling time 2. Number of calls until resolution 3. Response time Partner Feedback 1. Partner Loyalty 2. Satisfaction with partnering relationship Customer Feedback 1. Customer Loyalty 2. Relationship satisfaction 3. Transaction satisfaction Employee Feedback 1. Employee Loyalty 2. Satisfaction with business areas Financial 1. Revenue 2. Number of products purchased 3. Customer tenure 4. Service contract renewal 5. Number of sales transactions 6. Frequency of purchases
    12. 12. 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 Constituency Satisfaction/ Loyalty Financial Business Metrics
    13. 13. Integrating your Business Data Customer Feedback Data Sources Financial (revenue, number of sales) Operational (call handling, response time) Transactional Survey (satisfaction/loyalty to company) Business Data Sources Relationship Survey (satisfaction with specific transaction/interaction) • Link data at customer level • Link data at transaction level • Quality of the • Satisfaction with the relationship (sat, loyalty) transaction impacts impacts financial metrics up/cross-selling • Link data at transaction level N/A (employee / partner feedback) (sentiment / shares / likes) • Link data at customer level • Quality of relationship (sentiment / likes / shares) impacts financial metrics • Link data at transaction level • Operational metrics impact • Operational metrics impact quality of the transaction sentiment / likes/ shares • Link data at constituency • Link data at constituency level level Constituency Social Media/ Communities • Link data at constituency level • Constituency satisfaction • Constituency satisfaction • Constituency satisfaction impacts customer impacts customer impacts customer satisfaction with overall satisfaction with interaction sentiment / likes / shares relationship
    14. 14. Financial Metrics / Real Loyalty Behaviors • Linkage analysis helps us determine if our customer feedback metrics predict real and measurable business outcomes Financial • Retention Relationship / – Customer tenure – Customer defection rate – Service contract renewal Social Media Business Metrics • Purchasing • Advocacy – Number of new customers – Revenue • Number of products purchased • Number of sales transactions • Frequency of purchases
    15. 15. VoC / Financial Linkage Customer Feedback Financial Metric for a specific customer (account) for a specific customer (account) x1 Customer (Account) 1 y1 x2 Customer (Account) 2 y2 x3 Customer (Account) 3 y3 x4 . . . xn Customer (Account) 4 . . . y4 . . . Customer (Account) n yn xn represents customer feedback for customer n. yn represents the financial metric for customer n.
    16. 16. Percent Purchasing Additional Software Value of Customer Loyalty 55% increase Disloyal (0-5) Loyal ( 6-8) Customer Loyalty Very Loyal (9-10)
    17. 17. Value of Social Sharing • Determine the social commerce value of a social media shares
    18. 18. Operational Metrics • Linkage analysis helps us determine/identify the operational factors that influence customer satisfaction/loyalty Operational Transactional • Support Metrics Metrics / Social Media – – – – – – – – 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 2012 TCELab
    19. 19. Operational / VoC Linkage Operational Metric Customer Feedback for a specific customer’s interaction for a specific customer’s interaction x1 Customer 1 Interaction y1 x2 Customer 2 Interaction y2 x3 Customer 3 Interaction y3 x4 . . . xn Customer 4 Interaction . . . y4 . . . Customer n Interaction yn xn represents the operational metric for customer interaction n. yn represents the customer feedback for customer interaction n. Copyright 2012 TCELab
    20. 20. Identify Operational Drivers Copyright 2012 TCELab
    21. 21. Identify Operational Standards Sat with SR Number of Calls to Resolve SR 1 call 2-3 calls 4-5 calls 6-7 calls 8 or more calls Sat with SR Number of SR Ownership Changes 1 change 2 changes 3 changes 4 changes 5+ changes Copyright 2012 TCELab
    22. 22. Web Service Metrics • Different objective service metrics – Self-service rate – Resolution time – Needed assistance – Time on site – Web analytics (% returning visitors; % visits from 3rd party) • Which matter to customers? – Correlate above metrics with service satisfaction and loyalty – “Customer-centric” metrics
    23. 23. Linkage Analysis • Data model associates customer feedback with objective metrics for each Web self service interaction Objective metric Customer feedback for a given Web self service interaction for a given Web self service interaction x1 Self service Interaction 1 x2 Self service Interaction 2 y2 x3 Self service Interaction 3 y3 x4 . . . xn Self service Interaction 4 Self service Interaction n y1 y4 . . . yn xn represents the objective metric for Web self service interaction for customer n. yn represents the customer feedback for Web self service interaction for customer n.
    24. 24. Identifying Customer-Centric Metrics
    25. 25. 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:
    26. 26. 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?
    27. 27. Data Integration in US Healthcare • US Federal Government (Medicare) tracks several metrics across US Hospitals Patient Experience • Overall Satisfaction • Likelihood to recommend • 8 dimensions • Nurse comm. • Doctor comm. • Room Quiet Health Outcomes • Mortality Rate / Survival Rate • Re-admission Rate Process Financial (Operational) • Safety • Medicare Measures Spend
    28. 28. PX for US Hospitals Map of US Hospitals and their Patient Experience Ratings -
    29. 29. Survival Rate for US Hospitals Map of US Hospitals and their Health Outcome Metrics -
    30. 30. Patient Experience and Healthcare
    31. 31. Survival Rates Related to Loyalty Survival rate – heart attack 98.50 98.48 98.46 98.44 98.42 98.40 98.38 98.36 No Loyalty Moderate Loyalty High Loyalty
    32. 32. Medicare Spend for US Hospitals by State
    33. 33. Medicare Spend and Patient Experience From: A Good Patient Experience Does not Start with Medical Spending
    34. 34. The Data Scientist Data Science Skills 1. Quantitative 2. Computer Engineering 3. Business Acumen 4. Communication 5. Scientific Method From: The One Hidden Skill You Need to Unlock the Value of Your Data
    35. 35. Data Veracity The accuracy and truthfulness of the data and the analytic outcomes of those data From: In Data We Trust
    36. 36. Have a hypothesis(es)
    37. 37. Be aware of Biases / Avoid Cherry Picking
    38. 38. Know the Sample Size / Data Source
    39. 39. Know your Customer Metrics 1.What’s the definition? 2.How is the metric calculated? 3.What are the measurement properties? 4.How useful is it? From: Four things you need to know about your customer metrics -
    40. 40. Big Data Implications for CX • 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, Alpine Chorus – Cross-functional teams working together to solve customer problems • Use objective, “real,” loyalty metrics
    41. 41. Big Data Online Resources • • • • • • •
    42. 42. @bobehayes Big Data and Customer Experience How may we help? Bob E. Hayes, PhD Winter 2014
    43. 43. Big Data Market Continues Growth
    44. 44. Gartner’s Emerging Technologies Hype Cycle Gartner's 2013 Hype Cycle for Emerging Technologies -
    45. 45. Getting Value from Big Data • Marketing – Faster Reporting & Analytics, Predict Customer Behaviors, Sentiment/Social Media Analysis, Improve Campaign Effectiveness • IT – Real-time Streaming Data, Hadoop Analytics, Fast Development & Deployment, Enterprise-wide Integration • Finance – Threat & Fraud Detection, Reduced Resource Costs, Governance & Risk Management, Future-Proof Versatility • Infrastructure – High Availability, Analyze Machine Data, Archiving and Monitoring, Simplified Data Management Based on IBM Big Data Smart Sixteen Big Data Bracket.
    46. 46. The Patient Experience
    47. 47. Importance of CX is Over-inflated • Correlations between loyalty ratings and CX satisfaction ratings are driven by the fact that we use the same measurement method to measure each (ratings on bipolar scale in web survey) • 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
    48. 48. Value from Analytics: Accenture 2012 Study 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.
    49. 49. Customer Loyalty Measurement Framework Loyalty Types Objective (Survey Questions) Subjective Measurement Approach Emotional Behavioral RETENTION • Leave / Stay • Service contract renewal ADVOCACY • Number/Percent of new customers • • • • • • ADVOCACY Overall satisfaction Likelihood to recommend Likelihood to buy same product Level of trust Willing to forgive Willing to consider PURCHASING • Usage Metrics – Frequency of use/ visit, Page views • Sales Records - Number of products purchased 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.
    50. 50. 9 Levers of Analytics Success