What MBA Students Need to Know about CX, Data Science and Surveys

Customer Experience, Big Data Analytics and Surveys

Bob E. Hayes, PhD
Chief Research Officer
Email: bob@appuri.com
Web: www.appuri.com
Twitter: @bobehayes
• Author of three books on customer experience
management and analytics
• PhD in industrial-organizational psychology
• #1 blogger overall on CustomerThink
(http://customerthink.com/author/bobehayes/)
• #1 blogger on the topic of customer analytics
(http://customerthink.com/top-authors-category/)
• Top expert in Big Data and Data Science
• https://www.maptive.com/the-top-100-big-data-
experts/
• http://www.kdnuggets.com/2015/02/top-big-data-
influencers-brands.html

Appuri
Help businesses improve retention, advocacy
and growth
Chief Research Officer
Directing research on best practices in customer
analytics, data science and measurement
Business Over Broadway
Solve problems through the use of the scientific
method
Owner
Using data and analytics to help make decisions that are
based on fact, not hyperbole
What I do

 CX in a Big Data World
 Optimal Customer Survey
 Analytics of Survey Data
 Two-Question Survey
Contents

• A phenomenon about the
quantification of everything
• Different sides of Big Data:
1. Processing of Three Vs (volume, velocity,
variety)
2. Insights (data science, veracity)
3. Analytics (types, data source, machine
learning)
4. Data Integration (the sum of your data is
greater than some of your data)
5. Communication (visualization, storytelling)
6. Security/Privacy/Ethics (data use policy)
Big Data
Image from Domo (2016)

• Interest in Big Data topics is
growing dramatically
• Relatively speaking, interest in
customer experience shows
slight growth
• Good opportunity to
incorporate big data principles
(data science, machine
learning) into CX programs
Interest in Customer Experience and Big Data

• You have a lot of data about your
customers.
• Don’t rely on just surveys to
understand and predict customer
behaviors
Your Big Data
Data Format
Structured Unstructured
DataSource
Internal
Human-Generated
• Survey ratings
• Aptitudetesting
Machine-Generated
• Web metrics from Web logs
• Product purchase from sales
Records
• Process control measures
Human-Generated
• Emails, letters, text messages
• Audiotranscripts
• Customer comments
• Voicemails
• Corporate
video/ communications
• Pictures, illustrations
• Employeereviews
External
Human-Generated
• Number of Retweets, Facebook
likes, Google Plus+1s
• Ratings on Yelp
• Patient ratings
Machine-Generated
• GPS for tweets
• Time of
tweet/ updates/ postings
Human-Generated
• Content of social media
updates
• Comments in onlineforums
• Comments on Yelp
• Video reviews
• Pinterest images
• Surveillance video

• The goal is to know everything
about each customer
• Your analytics will result in better
predictive models for all
customers
• Lead to true CX personalization
Integrate Your Data

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/

Data Integration is Key to Extracting Value - Operational
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

A way of extracting insights from data
using the powers of computer science
and statistics applied to data from
a specific field of study
Goal: empirically-based insights that augment and
enhance human decisions and algorithms
What is Data Science?

Skills of Data Science
Area Skills*
Business
1.Product design and development
2.Project management
3.Business development
4.Budgeting
5.Governance & Compliance (e.g., security)
Technology
6.Managing unstructured data (e.g., noSQL)
7.Managing structured data (e.g., SQL, JSON, XML)
8.Natural Language Processing (NLP) and text mining
9.Machine Learning (e.g., decision trees, neural nets, Support Vector Machine, clustering)
10.Big and Distributed Data (e.g., Hadoop, Map/Reduce, Spark)
Math &
Modeling
11.Optimization (e.g., linear, integer, convex, global)
12.Math (e.g., linear algebra, real analysis, calculus)
13.Graphical Models (e.g., social networks)
14.Algorithms (e.g., computational complexity, Computer Science theory) and Simulations (e.g., discrete, agent-based, continuous)
15.Bayesian Statistics (e.g., Markov Chain Monte Carlo)
Programming
16.Systems Administration (e.g., UNIX) and Design
17.Database Administration (MySQL, NOSQL)
18.Cloud Management
19.Back-End Programming (e.g., JAVA/Rails/Objective C)
20.Front-End Programming (e.g., JavaScript, HTML, CSS)
Statistics
21.Data Management (e.g., recoding, de-duplicating, Integrating disparate data sources, Web scraping)
22.Data Mining (e.g. R, Python, SPSS, SAS) and Visualization (e.g., graphics, mapping, web-based data visualization) tools
23.Statistics and statistical modeling (e.g., general linear model, ANOVA, MANOVA, Spatio-temporal, Geographical Information System (GIS))
24.Science/Scientific Method (e.g., experimental design, research design)
25.Communication (e.g., sharing results, writing/publishing, presentations, blogging)
* List of skills adapted from Analyzing the Analyzers by Harlan D. Harris, Sean Patrick Murphy and Marck Vaisman

Not all Data Scientists are Created Equal
• Different types of data scientists
possess different skills
• Biz Management – strong in
business skills
• Developer – strong in
technology/programming skills
• Researcher – strong in math/
statistics skills
• Creatives – average in all skills

1. Formulate
Questions
2. Generate
hypothesis/
hunch
3. Gather /
Generate data
4. Analyze data
/ Test
hypothesis
5. Take action /
Communicate
results
• Start with a problem statement.
• What are your hunches /
hypotheses?
• Be sure your hypotheses are
testable.
• You can use experimental or
observational approach to analyzing
data.
• Integrate your data silos to ask
bigger questions; connect the dots
and get a 360 degree view of your
customers.
• Employ Predictive analytics /
Inferential statistics to test hypotheses
• Employ machine learning to quickly
surface insights
• Implement your findings
• Use Prescriptive analytics to
guide course of action
From Questions to Actions: The Scientific Method

A Team Sport: Data Science Skills and the Scientific Method

Customer Analytics Maturity Matrix
Fundamental
Awareness
Intermediate
Advanced
Data used to describe
current state of customer
health
Data used to understand
why things happened
Data used to manage
specific customers
Data used to identify
drivers of customer
behaviors
Maturity Stage Process
World Class
Beginner
Data used to improve
systemic problems that
improve the health of all
customers
• Deploy algorithms developed by your
data scientists
• Employ sophisticated analytics to
uncover customer insights
(exploratory)
• Analyze data using machine learning to
identify drivers of churn
• Integrate customer insights into
existing sales / marketing automation
systems (e.g., risk scoring
Accounts/Contacts)
• Create dashboards to understand what
happened in the previous time periods
• Integrate data silos to create a Unified
Customer Profile - providing holistic,
360 degree, view of customers
PrescriptivePredictiveDiagnosticDescriptive
StrategicUseof
CustomerData
ManualProcessesAutomatedProcesses
TacticalUseof
CustomerData

Customer Analytics Maturity Matrix
* Preliminary results from recent study on customer analytics best practices. For a free assessment of your CX or Customer Success program,
take the study survey by clicking here: http://bit.ly/cabpa.

Asking the right questions leads to deeper customer insights:
1. How loyal are the customers to the company? Will customers be engaging
in different types of loyalty behaviors (e.g., recommend, buy different
products/services, expand usage, renew service contracts)?
2. How satisfied are the customers with the customer experience? Are
customers satisfied with different touch points (e.g., product, ease, support,
communication)?
3. How does the company rank against the competition? Do customers
think the company is the best/worst/typical in the industry?
4. What is the general sentiment of your customers?
5. Where would CX improvement efforts have the biggest ROI? If you
purchase the company, what do you need to fix first?
6. Customer Relationship Diagnostic (CRD)
Customer Relationship Surveys Help you Answer Important Questions

1. Retention – will customers stay/churn?
2. Advocacy – will customers recommend?
3. Purchasing – will customer expand relationship
Customer Loyalty Drives Business Growth/Value
Company growth/value is
impacted by three types of
customer behavior:

Customer Loyalty Questions
Type Definition Loyalty Questions
Retention
Loyalty
The degree to which customers will
remain as a customer/not leave to
competitor
1. Likelihood to switch to another company*
2. Likelihood to purchase from competitor*
3. Likelihood to renew service contract
Advocacy
Loyalty
The degree to which customers feel
positively toward/will advocate your
product / service / brand
4. Overall satisfaction
5. Likelihood to recommend (NPS)
6. Likelihood to purchase same product/service
Purchasing
Loyalty
The degree to which customers will
increase their purchasing behavior
7. Likelihood to purchase different/additional products/services
8. Likelihood to expand use of products across company
0 1051 2 3 4 6 7 8 9
Not at all
Likely
Extremely
Likely

Consider Objective Loyalty Metrics
Measurement Approach
Objective Subjective
(Survey Questions)
LoyaltyTypes
Emotional
ADVOCACY
• Number/Percent of new customers
• Social media engagement - Likes/Shares
ADVOCACY Intentions
• Overall satisfaction
• Recommend
• Buy same product
• Level of trust
• Willing to forgive
• Willing to consider
RETENTION Intentions
• Renew service contract
• Stay or Leave
PURCHASING Intentions
• Buy different/additional products
• Likelihood to expand usage
Behavioral
RETENTION
• Churn rates
• Service contract renewal rates
PURCHASING
• Usage – Frequency of use, Page views
• Sales Records - Number of products
purchased

1. Sum of all experiences a customer has with a supplier of
goods or services, over the duration of their relationship with
that supplier
2. The quality of the customer experience
is measured through satisfaction ratings
3. Understand drivers of customer loyalty
Better customer experience leads to higher levels of customer
loyalty
Satisfaction with the Customer Experience

General vs. Specific CX Questions

General CX Questions Predict Loyalty Well; Specific Questions Add Little

 Overall, how satisfied are you with each area?
1. Ease of doing business
2. Sales / Account Management
3. Product Quality
4. Service Quality
5. Technical Support
6. Communications from the Company
7. Future Product/Company Direction
General Customer Experience Questions

CX has greater impact on advocacy loyalty
.00
.10
.20
.30
.40
.50
.60
.70
.80
.90
Ease of doing
business
Overall Product
Quality
Responsiveness to
Service Needs
Responsiveness to
Technical Problems
Ability to Resolve
Technical Problems
Communications
from the Company
Future
Product/Company
Direction
ImpactonLoyaltyMetric
(correlationbetweenbusinessattributes
andloyaltymetric)
Advocacy Loyalty
Purchasing Loyalty
Retention Loyalty
1 Importance measured by correlation between business attribute and customer loyalty metric. Ranking conducted within a specific loyalty metric.

 Customer experience questions may not be enough
to improve business growth
 You need to understand your relative performance
 HBR study (2011)1: Top-ranked companies receive
greater share of wallet compared to bottom-
ranked companies
 If customers think you’re the best, they will deepen
their buying relationship with you
Benchmarking: Competitive Analytics
1 Keiningham, Timothy L., Lerzan Aksoy, Alexander Buoye, and Bruce Cooil (2011), “Customer Loyalty Isn’t Enough. Grow Your Share of Wallet.”
Harvard Business Review. vol. 89 (October).

Loyalty Benchmarks (B2B)
0
1
2
3
4
5
6
7
8
9
10
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Mean
PercentofRespondents
Very Loyal (ratings of 9 or 10) Loyal (ratings of 6-8) Disloyal (ratings of 0-5) Mean
Overall Satisfaction
Recommend
Continue
Purchasing / Using
Select Vendor
Again
Means not calculated for overall sat for Comp D.
Comp D used a 1 to 6 satisfaction scale; 1-3 =
Disloyal; 4-5 = Loyal; 6 = Very loyal.
Comp A provides consulting services, analytics solutions and customized products for financial companies.
Comp B provides solutions that help healthcare providers automate key business processes.
Comp C helps businesses monitor and optimize Storage Area Networks (SANs).
Comp D provides security and data protection solutions.
Comp E specializes in developing hardware systems and enterprise software products.
Comp F provides solutions for precision electrical measurement and test of advanced semiconductor devices.

 What best describes our performance compared to the
competitors you use?
Bootstrap Benchmarking: Relative Performance

CRD: The Survey
Customer Loyalty and Customer
Experience Indices / Measures1 Addresses Business Growth Survey Questions Scores
Customer Loyalty (RAPID)
Retention Loyalty Index (RLI)
Advocacy Loyalty Index (ALI)
Purchasing Loyalty Index (PLI)
RLI: Will your customers remain with /
not leave you?
ALI: Will your customers promote you?
PLI: Will your customers invest in
additional product / service offerings?
RLI: Renew service contract, Use
competitor2
ALI: Overall Satisfaction, Recommend,
Continue purchasing / using
PLI: Purchase additional services,
Expand usage
Scores for each index can
range from 0 (low loyalty) to
10 (high loyalty)
General Customer Experience
(GENCX)
Ease of doing business, Account Mgmt,
Product Quality, Customer Service, Tech
Support Communications from Company,
Future Product/Company Direction
Are your customers receiving a great
customer experience?
Customers provide satisfaction rating
for each of the 7 business areas.
Scores can range from 0 (high
dissatisfaction) to 10 (high
satisfaction)
Relative Performance Assessment
(RPACX)
Are you ahead of the competition? How does your company perform
relative to the competition?
Scores can range from 0 (low
ranking) to 100 (high ranking)
Customer Sentiment (CSI) Do your customers have a generally
positive or negative opinion of your
brand?
What one word best describes this
company?3
Scores can range from 0
(negative sentiment) to 100
(positive sentiment)
1 Indices are the average rating. RAPID ratings are calculated by averaging over the questions in a specific index. Each index has been shown to have a high degree of reliability.2
Reverse coded so higher ratings reflect high retention loyalty. 3 Scaled using sentiment lexicon.

1. Descriptive: What happened? Mean, Standard Deviation, Frequencies
2. Predictive: What will happen? Correlation, Regression, Clustering
3. Prescriptive: What should I do? Combination of Descriptive and
Predictive and Business Rules (logic)
Three Types of Analytics
1% 3%
25%
54%
17%
0%
10%
20%
30%
40%
50%
60%
Terrible Poor Fair Good Excellent
46% 47% 42%
34% 34% 33% 28%
44% 37% 41%
46% 46% 47%
48%
10% 17% 16% 20% 20% 20% 24%
0%
20%
40%
60%
80%
100%
PercentofCustomers Very Satisfied Satisfied Dissatisfied
GENCX
Index
75

Executive Dashboard: Customer Loyalty
57%
34%
43%
25% 28%
51%
31%
53%
46%
38%
44%
33%
12% 13% 11%
38%
29%
15%
0%
20%
40%
60%
80%
100%
Same Sat Rec Add Expand Renew
PercentofCustomers
Very Loyal Loyal At Risk
Reten-
tion
(Renew)
Advocacy
(Buy same Sat, Rec)
Purchasing
(Buy add, expand)
Advocacy
79
Retention
79
Purchasing
66
RAPID
Index
75
• Majority of customers report high
levels of loyalty across different
loyalty types / lowest for Purchasing
Loyalty
• Loyalty improves over prior years
76 79 79
74 75
79
55
60
66
25
50
75
100
2011 2012 2013
CustomerLoyalty
Retention Advocacy Purchasing

Executive Dashboard: Customer Experience
46% 47% 42%
34% 34% 33% 28%
44%
37% 41%
46% 46% 47%
48%
10%
17% 16% 20% 20% 20% 24%
0%
20%
40%
60%
80%
100%
PercentofCustomers
Very Satisfied Satisfied Dissatisfied
GENCX
Index
75
Account
Manage-ment
65
Ease of Doing
Business
69
Communi-
cation
69
Direction and
Future
78
Customer
Service
77
Technical
Support
77
Product
Quality
79
Majority of customers report
they are satisfied with
Company ABC

Analytics Workflow
Data
Predictive
what will happen?
Descriptive
what happened?
Prescriptive
what should I do?
Decisions
Actions

Need to know two things about each CX touch point:
1. Level of customer satisfaction or performance (descriptive)
2. Importance to / Impact on customer loyalty (predictive)
Descriptive and Predictive Analytics for your Customer Survey
CX Touch Point
Performance1
(Average CS
Rating)
Impact2 on
Advocacy
Loyalty
Ease of doing business 6.90 .72
Overall Product Quality 6.65 .83
Responsiveness to Service Needs 7.08 .62
Responsiveness to Technical Problems 7.19 .62
Ability to Resolve Technical Problems 7.03 .59
Communications from the Company 6.68 .64
Future Product/Company Direction 5.69 .64
1 Performance of each attribute is the average rating for each attribute across all respondents. Possible scores range from 0 (Extremely Dissatisfied)
to 10 (Extremely Satisfied); 2 Impact is the correlation between specific CX Touch Point and Advocacy Loyalty Index.

0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
5.25 5.75 6.25 6.75 7.25 7.75
ImpactonAdvocacyLoyalty
andAdvocacyLoyaltyIndex)
Performance on Business Attribute
(Customer Rating)
Loyalty Driver Matrix: Prescriptive Analytics
Advocacy Loyalty Index is the average of the
following four questions (Overall Satisfaction,
Recommend, Select vendor again, Continue using).
Where should we invest?
1. For each CX touch
point, plot:
performance by
impact on loyalty
2. Apply business rule to
plot (prescribes
course of action)

Prescriptive Analytics – Loyalty Driver Matrix
Examine each CX touch point’s
performance and impact on
loyalty simultaneously.
1. Key Drivers – Invest in areas to
increase Customer Loyalty.
2. Hidden Drivers – Use features in
marketing to grow customer base.
3. Visible Drivers – Consider
features in marketing to grow
customer base.
4. Weak Drivers – Monitor as lowest
priority for investment.

Making Improvements: Predicting Advocacy Loyalty
Advocacy Loyalty Index is the average of the
following four questions (Overall Satisfaction,
Recommend, Select vendor again, Continue
using).
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
5.25 5.75 6.25 6.75 7.25 7.75
ImpactonAdvocacyLoyalty
andAdvocacyLoyaltyIndex)
(Customer Rating)
To improve
advocacy loyalty,
you may consider
focusing on
following areas:
1. Overall Product
Quality

CX Touch Point Performance1
(Average Rating)
Impact2 on
Advocacy
Loyalty
ImproveLeverage Consider
to 10 (Extremely Satisfied); 2 Impact is the correlation between specific Business Attribute sand Advocacy Loyalty Index.
 Improving Advocacy Loyalty
Improving and Marketing ACME

Example: Driver Analysis for Purchasing Loyalty
CX Touch Point
Performance1
(Average CS
Rating)
Impact2 on
Purchasing
Loyalty
1 Performance of each attribute is the average rating for each attribute across all
respondents. Possible scores range from 0 (Extremely Dissatisfied) to 10 (Extremely
Satisfied).
2 Impact is the correlation between specific CX Touch Point and Purchasing Loyalty.

Example: Driver Analysis for Retention Loyalty
CX Touch Point
Performance1
(Average CS
Rating)
Impact2 on
Retention
Loyalty
1 Performance of each attribute is the average rating for each attribute across all
respondents. Possible scores range from 0 (Extremely Dissatisfied) to 10 (Extremely
Satisfied).
2 Impact is the correlation between specific CX Touch Point and Retention Loyalty.

CX Touch Point
Performance1
(Average
Rating)
Impact2 on
Purchasing
Loyalty
Responsiveness to Technical
Problems
7.19 .33
to 10 (Extremely Satisfied); 2 Impact is the correlation between specific Business Attribute sand Purchasing Loyalty Index.
 Improving Purchasing Loyalty

Making Improvements: Predicting Purchasing Loyalty
Purchasing Loyalty Index is the average of the
following two questions (Purchase different or
new, Expand usage).
To improve
purchasing loyalty,
you may consider
focusing on
following areas:
1. Communications
from the Company
2. Overall Product
Quality
3. Future Product/
Company Direction
0.20
0.25
0.30
0.35
0.40
0.45
0.50
5.25 5.75 6.25 6.75 7.25 7.75
ImpactonPurchasingLoyalty
andPurchasingLoyaltyIndex)
(Customer Rating)

to 10 (Extremely Satisfied); 2 Impact is the correlation between specific Business Attribute sand Customer Loyalty.
 Improving Retention Loyalty
CX Touch Point Performance1
(Average Rating)
Impact2 on
Retention
Loyalty

Making Improvements: Predicting Retention Loyalty
To improve retention
loyalty, you may
consider focusing on
following areas:
1. Overall Product
Quality
2. Future Product/
Company Direction
Retention Loyalty Index is the average
of the following question (Stop using)
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
5.25 5.75 6.25 6.75 7.25 7.75
ImpactonRetentionLoyalty
andRetentionLoyaltyIndex)
(Customer Rating)

1. What one word best describes this
company/product/service?
2. If you were in charge of this company, what
improvements, if any would you make?

56
Approaches to Measuring Attitudes
Structured
Data generated to measure
specific construct
How satisfied are you with
company?
Customer-generated
Unstructured
Data are given to us. We
take what we can get
emails, social media, support calls,
movie reviews, tweet content, transcripts
of comments
Algorithm-generated
IntentSourceScore

What one word best describes company’s products/service?
Word cloud based on 944
respondents who answered
the question, “What one word
best describes the company’s
products / services?”
Font size of words corresponds
to the frequency of words
used by customers. Larger
words are used more
frequently by customers than
smaller words.

Judgment-Based
• Two subject matter experts independently rate list of words from
customer survey
0 (negative sentiment) to 10 (positive sentiment)
• High agreement between experts (rater)
Lexicon Mean SD 1 2 3 4
1. Rater 1 First Rating 6.49 2.32
2. Rater 2 First Rating 6.44 1.93 .87
3. Rater 1 Second Rating 6.35 2.39 .98
4. Rater 2 Second Rating 6.42 1.95 .99 .90
5. Average Sentiment 6.39 2.11 .96 .96 .98 .97
N = 251. All correlations are statistically significant at the p < .01 level.
Average sentiment: Based on based on the average second sentiment ratings of each rater.
Bold correlations represent inter-rater agreement.
Italic correlations represent intra-rater agreement.
Descriptive
Statistics
Correlations

Empirically-Based
• Examine four corpora (a collection of written text) with accompanying ratings
• Data from four review sites*
OpenTable, IMDB, Goodreads, Amazon/TripAdvisor
* See Christopher Potts: http://web.stanford.edu/~cgpotts/talks/potts-wordnetmods.pdf

Description of Four Lexicon Sources

Calculating Sentiment for Each Word
• Re-scale values from 1 to 5 -> 0 to 10
• Calculate sentiment of each word (adjective)
“excellent” sentiment value of 8.39
“good” sentiment value of 6.69

Distribution of Words’ Sentiment Values

Descriptive Statistics and Correlations of Sentiment Values of Words

Reliability of Customer Sentiment Index
• B2B Technology Company
Customer Survey – one word, ratings
• Context is important
Mean SD N 1 2 3 4
1. CSI - Expert 7.09 1.84 894
2. CSI - OpenTable 7.12 1.18 766 .77
3. CSI - IMDB 6.78 .86 786 .60 .78
4. CSI - Goodreads 6.30 1.23 757 .62 .74 .93
5. CSI - Amason/Tripadvisor 7.65 .97 623 .65 .83 .77 .68
Correlations among CSI scores
Mean SD N
CSI
Expert
CSI
OT
CSI
IMDB
CSI
GR
CSI
A/TA
Overall Satisfaction 7.60 1.99 1595 .57 .48 .33 .30 .43
Recommend 7.91 1.96 1585 .56 .49 .35 .31 .42
Purchase same / similar 8.22 2.26 1527 .34 .31 .24 .24 .21
Purchase additional / different 6.32 2.83 1508 .18 .16 .10 .09 .12
Expand use 6.80 2.59 1523 .22 .22 .18 .16 .18
Renew service contract 7.90 2.53 1159 .30 .25 .19 .18 .21
Ease of doing business 7.37 2.17 1204 .55 .49 .37 .35 .45
Account Management 6.98 2.32 1189 .42 .35 .26 .25 .36
Product Quality 7.93 1.89 1297 .50 .44 .27 .27 .42
Service / Repair 7.67 2.15 1092 .41 .37 .29 .30 .30
Technical Support 7.73 2.29 1253 .43 .37 .29 .29 .33
Communications from
Company
7.37 2.13 1282 .51 .45 .34 .32 .39
Direction and future
products/services
7.45 1.96 1165 .48 .41 .26 .26 .37
All correlations statistically significant at the p < .05 level.
All measures are on a scale from 0 (low loyalty/satisfaction) to 10 (high loyalty/satisfaction).
Based on respondents (N = 1619) of annual customer survey of a B2B technology company.
Loyalty
Satisfactionwiththe
CustomerExperience
Correlations of CSI scores with Loyalty/CX Metrics

Validity of Customer Sentiment Index
Mean SD N
CSI
Expert
CSI
OT
CSI
IMDB
CSI
GR
CSI
A/TA
Overall Satisfaction 7.60 1.99 1595 .57 .48 .33 .30 .43
Recommend 7.91 1.96 1585 .56 .49 .35 .31 .42
Purchase same / similar 8.22 2.26 1527 .34 .31 .24 .24 .21
Purchase additional / different 6.32 2.83 1508 .18 .16 .10 .09 .12
Expand use 6.80 2.59 1523 .22 .22 .18 .16 .18
Renew service contract 7.90 2.53 1159 .30 .25 .19 .18 .21
Ease of doing business 7.37 2.17 1204 .55 .49 .37 .35 .45
Account Management 6.98 2.32 1189 .42 .35 .26 .25 .36
Product Quality 7.93 1.89 1297 .50 .44 .27 .27 .42
Service / Repair 7.67 2.15 1092 .41 .37 .29 .30 .30
Technical Support 7.73 2.29 1253 .43 .37 .29 .29 .33
Communications from
Company
7.37 2.13 1282 .51 .45 .34 .32 .39
Direction and future
products/services
7.45 1.96 1165 .48 .41 .26 .26 .37
All correlations statistically significant at the p < .05 level.
All measures are on a scale from 0 (low loyalty/satisfaction) to 10 (high loyalty/satisfaction).
Based on respondents (N = 1619) of annual customer survey of a B2B technology company.
Loyalty
Satisfactionwiththe
CustomerExperience
Correlations of CSI scores with Loyalty/CX Metrics

Relationship between CSI and Recommend
B2B Survey B2C Survey

What improvements would you make?
30%
16% 15%
8% 7% 6% 6% 5% 4% 4% 3% 3% 2%
Data are based on 598 respondents who answered the following question: If you were in charge of company, what improvements, if
any, would you make? Categories of improvement areas do not include nine categories because they were mentioned less than 2%
of the times by the respondents.
Frequency distribution based on 598 respondents who
answered the question, “If you were in charge of the
company, what improvements, if any, would you make?”

What improvements would you make?
7.24
6.89 6.85 6.81 6.80 6.80 6.80 6.79 6.77
6.38
5.98 5.88
5.36
CustomerSentiment
Data are based on 598 respondents who answered the following question: If you were in charge of company, what improvements, if any,
would you make? Categories of improvement areas do not include nine categories because they were mentioned less than 2% of the time
by the respondents.
Consider these touch points (arrowed) as a starting point to make improvements; customers who mention
these improvement areas report significantly lower sentiment than customers who do not mention these
areas.

Apply results across the company
1. Sales, Marketing & Service
• use popular words in sales/marketing efforts to improve how collateral
resonates with them
2. Product Management
• use sentiment index in design thinking process to improve “testing” step
3. Operations
• identify business areas/processes that need attention

Applications of Two-Question Survey
• Mobile Surveys
• Extract more information from single word
Apply different lexicons (e.g., anxiety, strength)
• Simplify feedback process
Shorter surveys benefit customers
Fewer dashboard metrics facilitate executive reports

Executive Dashboard: Customer Sentiment Index
CS
Index
70
What one word best describes this company?
• 77% of customers have
positive sentiment
• 6% of customers have
negative sentiment
4.9%
1.6%
16.4%
25.0%
52.0%
Very Negative
Sentiment - 0 through
2.5
Slightly Negative
Sentiment - 2.6
through 4.5
Neutral Sentiment -
4.6 through 5.5
Slightly Positive
Sentiment - 5.6
through 7.5
Very Positive
Sentiment - 7.6
through 10

Executive Dashboard: Customer Sentiment
What one word best
describes this
company?
What improvements,
if any, would you make?
CS
Index
70

Further Reading
To learn more about
customer-centric
measurement and
analytics, RAPID
Loyalty measurement,
CX measurement and
problems with the NPS,
check out these books.

What MBA Students Need to Know about CX, Data Science and Surveys

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