Analyze opinions, perceptions, emotions, and intentions to level up your customer insights. MeaningCloud webinar, April 29, 2020. More info and webinar contents https://www.meaningcloud.com/blog/text-analytics-customer-feedback-action MeaningCloud https://www.meaningcloud.com

1. Transform your
customer
feedback into
action with deep
text analytics
April 29, 2020
MEANINGCLOUD – 2020
Webinar

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MEANINGCLOUD - 2020
We hope you are safe and well

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Presenter
How to participate
• Send questions using the chat feature, or
• Click the “Raise your hand” button to speak and we will enable your mic
• Afterwards, you’ll be able to access a recording of the webinar and its contents as
tutorials on our blog
Before we get started…
Antonio Matarranz
CMO

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Why this webinar?
Analyzing unstructured customer
feedback is a must
How to make it more actionable
and valuable?

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Agenda
• Leveraging unstructured customer feedback: benefits
and challenges
• Text analytics to the rescue... but with limitations
• How to use deep text analytics to extract more
actionable insights
– Pre-made Insights
– Adaptation
– Development
• Conclusions and Q&A

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How to “focus on the customer”, really?
Cambiar en Encabezado / Pie de Pg 6
Define “segment of 1”
Iniciatives
• Voice of the Customer (VoC)
• Customer Insights
• Customer Experience Management

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Unstructured (and unsolicited) customer feedback
Interviews
• Audio recordings
• Transcripts
Surveys
• Open-ended
questions
Contact center
• Speech
• Email
• Chat
• Bot
• Social
Social media
• Social networks
• Blogs
• Forums
• Review sites
• Communities

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Why analyze it?
VALUESpontaneous
Sincere, in the
customer’s
words
Contextual
Enables to
explore and
discover
Viral
capability

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What are the challenges?
Volume
Millions of
interactions per
month
Velocity
Need for fast
response
Variety Text, audio, image…

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The reality of unstructured feedback

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Text analytics to the rescue
Unstrutured text Text analytics Structured data
Insights
Scaling the analysis of unstructured feedback

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How to use text analytics in multichannel scenarios
Speech to
Text
Conversion
Text
Analytics
Analytics &
Visualization
Phone
Email, chat,
social, bots
Structured
data
Data
Insights
Conversion: Speech → Text
Text Analytics: Text → Structured Data
Text
Contact center

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How to use text analytics in multichannel scenarios
Harvesting content from
social networks, blogs,
forums, review sites…
Text analytics
Language understanding
Actionable
insights
Media monitoring

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A “typical” example
Type Value
Entity TeleCom (company)
General theme Telecommunication services
General polarity Negative
“I'm fed up with these TeleCom people. My mobile is
constantly breaking down and I want to cancel the service.
But their support center is always busy. They're horrible!”
Telecom-
munication
services
Entities, general themes, sentiment are valuable, but...

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The big challenge is bridging the chasm between analysis
and value
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Transform analysis into something actionable and valuable
Data
Value
Data Analytics Decision Action Value

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How to bridge the chasm?
Relevant topics
• Our brands, products…
Actionable categories
• Our functions,
departments…
Targeted sentiment
• Our attributes,
components…
Root causes
• Specific to our business
Drivers
• Satisfaction, quality,
purchase
Discovery
• Emerging, “off the radar”
topics
Emotions
• Joy, sadness, surprise…
Customer journey
• Information, consideration,
evaluation, purchase…

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Text analytics: much more is needed
• Flexible
development
• Easy embedding
and consumption
• Adaptation to
the customer’s
context
• Short time-to-
benefit
Pre-built
solutions
Customi-
zation
Agility
Integra-
bility

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MeaningCloud: Meaning as a Service
Standard APIs (SaaS and on-premises)
Use it free at www.meaningcloud.com

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MeaningCloud levels up your customer insights
Pre-built
insights
Adaptation and
customization
Development of
new insights

20. Pre-built customer insights

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Attribute-/aspect-based sentiment analysis
Yesterday I had the opportunity to test the latest
models of Samsung and Apple. The Samsung is
more reliable although its screen needs to be
improved. The design of the iPhone is unbeatable
but it is too expensive.
• “Neutral” aggregated sentiment?
• Sentiment objects are different
PepePerez @Jperez – 1h
I love the new iPhone.
The limitations of document-level sentiment analysis
We need aspect-based sentiment analysis
Type Value
Entity iPhone
General sentiment

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Topic-oriented sentiment analysis
• Identify sentiment (positive/negative/neutral polarity or lack thereof)
– At the document level
– At sentence level
– Associated with mentioned topics (entities/concepts)
– Domain-specific topics definable in dictionaries
The Samsung is more reliable and the iPhone is too expensive
The hotel rooms are comfortable, but the landscape is horrible
Standard API
Tutorial, Recorded webinar

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Emotions: the hidden key to profitability
➢(Negative) emotions are more durable
➢(Negative) Emotions are more shared
➢(Negative) emotions shape the
experience
➢Emotional motivators influence
behavior
➢Customers' emotional connection with
the brand is (25-100%) more
profitable than satisfaction
Joy, Confidence, Fear, Surprise, Sadness…

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Emotion recognition
• Identify expressed emotions: Joy, Trust, Fear,
Surprise, Sadness, Disgust, Anger,
Anticipation
• Based on Plutchik's Wheel of Emotions
• It complements Sentiment analysis
Vertical Pack

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Analyze expressed intention to predict the future
➢Predict behavior
➢Detect the customer journey and
personalize the response to it
➢Discover business opportunities
➢Give better service
➢Retain customers
➢Foster recommendation
Request information, Purchase, Recommend, Cancel…

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Intention in the customer journey
• identify a set of basic intentions throughout the customer journey: Information,
Advice, Purchase, Support, Recommendation, Complaint, or Cancellation
• Detect Purchase / Churn signals
Customer journey
Informa-
tion
Advice Purchase Support
Recom-
mendation
Complaint
Cancella-
tion
Vertical Pack

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➢Quality lies in the customer’s
perceptions
➢Quality is multidimensional
➢The perception of quality
produces satisfaction
Quality is a multidimensional perception
Conformity
Functions
Aesthetics
Service
Features

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Multidimensional satisfaction (Voice of the Customer)
Industry / General
Company
Company1
+
-
Company2
+
-
Channel
Web
+
-
Phone
+
-
Customer
service
Information
+
-
Maintenance
+
-
Quality
Functionality
+
-
Efficiency
+
-
Product
Product1
+
-
Product2
+
-
Operation
Activation
+
-
Cancellation
+
-
Satisfaction
+
-
• General and by industry: Banking, Retail, Telco...
• Dimension and attribute hierarchy
• Polarity by attributes and overall satisfaction dimension
Vertical Pack
Tutorial, Recorded webinar

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“I'm fed up with these TeleCom people. My mobile is
constantly breaking down and I want to cancel the service.
But their support center is always busy. They're horrible!”
What we have achieved
Type Value
Entity TeleCom (company)
General theme Telecommunication services
General polarity Negative
Emotion Annoyance
Product Cellular phone
Attribute – Reliability Negative
Intention Cancellation
Service – Support Negative
General satisfaction Negative
Telecom-
munication
services

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Discovering the topic structure of comments
• Group similar comments
– Aggregate according to
significant themes
– Relations between groups
– Detect duplicates
• Discovering topics that emerge
from the collection
– "New voice" of the customer
OJO: ING y
español

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Text clustering
Aggregate similar texts and discover meaningful themes
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New producto opportunity
Dissatisfaction cause
• No pre-defined taxonomy required
(unsupervised learning)
• Text-oriented processing
• Grouping of texts based on
• Adherence to a topic
• Content similarity
Standard API

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Measuring the corporate reputation of the company
• Market assessment through a
set of relevant corporate
dimensions and variables
• Sources:
– Surveys (typically)
– News
– Social

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Corporate reputation
Standard API
• Inspired by industry standards:
RepTrak (Reputation Institute), Merco
• Entity - Dimension - Variable - Polarity
analysis
• Sophisticated analysis involving topic
extraction, multi-level theme
classification and sentiment
• Available for Spanish.
Want other languages?
Contact us

34. Adaptation and
customization

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Opinions
The sentence “The highest interest
rate in industry!” is…
▪ Positive, if talking about
savings
▪ Negative, if talking about
mortgages
Customized linguistic resources improve accuracy
Mentions
▪ Names of banks and financial
companies, e.g., Citibank, BBVA
▪ Product names, e.g., Your Way
Account. Compass Account…
Themes
Example: analysis of a bank’s customer opinions
Products
Accounts
Checking
Savings
Borrowing
Credit
Mortgage
Channel
Office
Phone
Internet

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For every analysis task | Without coding
Graphical customization tools
• Entities
• Concepts
Dictionaries
• Polarity of expressions
Sentiment models
• Machine learning
• Rules
Classification models
• Semantic rules
Categorization models
Topic extraction
Sentiment analysis
Document classification
Deep categorization
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Standard Tools

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Agile model development process
Machine-Learning (ML)
Categorization
Semantic Rule-Based
Categorization
Rule ModelML Model
Input text Intermediate
categories
Categories
Model Training
Model Editor
Training texts
Rule editor
Automatic
categorization engine
Classifier training engine
Classifier engine
Fast model development and high
precision from the beginning
Transparency, refinement and adaptation

38. Development of new
insights

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Brand associations
• What entities/concepts people
usually mention when talking
about our brand
• “Semantic footprint” of the
brand
• Fine-grained, individual
perceptual analysis

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Perception analysis
• How customers perceive our
brand, with respect to
predefined, relevant
attributes, and compared to
competitors
• Aggregated, competitive
perceptual map
• Foundation for positioning
analysis

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Brand personality
• Identification of human
characteristics that are
attributed to a brand: sincerity,
excitement, competence,
sophistication, ruggedness…
• Example: Aaker’s brand
personality model

42. Conclusions

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Conclusions
Unstructured customer feedback is
more valuable than we might think
The right tools to extract all that
value are becoming available

44. Q & A time

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Stay tuned to our blog and emails
We’ll be posting a recording of the webinar and
its contents as tutorials soon

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MEANINGCLOUD - 2020 www.meaningcloud.com
Automating the extraction of Meaning from any information source.
+1 (646) 403-31043537 36th Street
New York, NY 11106
amatarranz@meaningcloud.com
Thank you for your attention!