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.
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
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
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
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
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.