This document provides an overview of big data and its applications. It discusses the three Vs of big data - volume, velocity, and variety. It also defines big data and outlines some key areas related to big data like technology, analytics, and data capture/storage/management. Trends showing increasing interest in both customer experience and big data are presented. Five high value use cases of big data for businesses are outlined. The document also discusses what customers and vendors think are important aspects of big data. Studies finding relationships between analytics use and business performance as well as the importance of data integration are referenced. Overall, the document presents a high-level introduction to big data concepts, applications, and considerations.
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Big Data and Customer Experience
1. Big Data and Customer Experience How may we help?
info@tcelab.com
Bob E. Hayes, PhD Winter 2014
2.
3. Three Vs of Big Data
Volume
Velocity
Variety
http://blogs.gartner.com/douglaney/files/2012/01/ad949-3D-DataManagement-Controlling-DataVolume-Velocity-and-Variety.pdf
INFOGRAPHIC from Domo June 2012
4. 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
5. 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.
6. Big Data Landscape – bigdatalandscape.com
Apps
Infrastructure
Technologies
7. 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: www.ibmbigdatahub.com/podcast/top-5-big-data-use-cases
8. 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 - http://bit.ly/1dfQDPP
9. 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 - http://bit.ly/1aG1R5P
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/2011winter/52205/big-data-analytics-and-the-path-frominsights-to-value/
11. 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
12. 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
13. 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
14. 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
15. 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
16. 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.
18. Value of Social Sharing
• Determine the social commerce value of a
social media shares
http://blog.eventbrite.com/social_commerce_a_look_from_our_london_office/
19. 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
20. 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
22. 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
23. 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
24. 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.
26. 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
27. 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?
28. 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
29. PX for US Hospitals
Map of US Hospitals and their Patient Experience Ratings - http://bit.ly/XqTbVF
30. Survival Rate for US Hospitals
Map of US Hospitals and their Health Outcome Metrics - http://bit.ly/XX4QQ4
34. Medicare Spend and Patient Experience
From: A Good Patient Experience Does not Start with Medical Spending http://bit.ly/TPFDmR
35. 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 http://bit.ly/1giZGCA
36. Data Veracity
The accuracy and truthfulness
of the data and the analytic
outcomes of those data
From: In Data We Trust http://bit.ly/17BZWOe
40. 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 - http://bit.ly/TYfkuQ
41. 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
42. Big Data Online Resources
• KDnuggets.com
• SmartDataCollective.com
• AllAnalytics.com
•
•
•
•
IBMBigDataHub.com
SAS.com/big-data
SAPBigData.com
Oracle.com/bigdata
46. 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.
48. 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
www.businessoverbroadway.com/is-the-importance-of-customer-experience-over-inflated
49. 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.
http://www.accenture.com/us-en/Pages/insight-analytics-action.aspx
50. 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.
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.