About the webinar It only takes one bad interaction for a customer to abandon a service or product. Businesses are no longer just competing with other companies’ products, they’re competing with a customer’s last service experience. All contact centers worldwide are looking for new and strategic ways to increase operational performance, reduce cost and still provide high-touch customer experiences that improve customer loyalty and highlight ways to increase revenue and productivity. Through this webinar, we will understand how AI can augment the effort, focus and problem-solving abilities of human agents so that they can tackle more complex or creative tasks. With an abundance of data from logs, emails, chat and voice recordings, contact centers can ingest this data to provide contextual customer service at the right time with the right way providing satisfactory customer service and retain the brand value. What you'll learn: - How organizations are leveraging AI & Machine learning in Customer Service - Live Demo of AI & ML in Customer Service - Best practices to automate machine learning models To explore more, visit: https://skyl.ai/form?p=start-trial

1. AI for Customer Service
How to Improve Contact
Center Efficiency with
Machine Learning?

2. Technology leader with 20+ years expertise in Product Development, Business strategy and
Artificial Intelligence acceleration. Active contributor in the New York AI community
Extensively worked with global organizations in BFSI, Healthcare, Manufacturing, Retail and
Ecommerce to define and implement AI strategies
Nisha Shoukath
Co-founder, People10 & Skyl.ai
The Speaker

3. Shruti Tanwar
Lead - Data Science
Extensive experience building future tech products using Machine Learning and
Artificial Intelligence.
Areas of expertise includes Deep Learning, Data Analysis, full stack development
and building world class products in ecommerce, travel and healthcare sector.
The Speaker

4. CTO & Software Architect with 15 years of experience working at the
forefront of cutting-edge technology leading innovative projects
Areas of expertise include Architecture design, rapid product
development, Deep Learning and Data Analysis
The Panelist
Bikash Sharma
CTO and Co-founder at Skyl.ai

5. All dial-in participants will be muted to enable the
presenters to speak without interruption
Getting familiar with ‘Zoom’
Questions can be submitted via Zoom Questions chat
window and will be addressed at the end during Q&A
The recording will be emailed to you after the webinar
Please familiarize yourself with the Zoom ‘Control Panel’ on your screen

6. Live Demo
of AI & ML in
Customer Service
...In the next 45 minutes
How organizations
are leveraging AI &
Machine learning in
Customer Service
Best practices to
automate machine
learning models
1 2 3

7. A quick intro about Skyl.ai
Machine Learning automation platform for unstructured data
Guided Machine Learning Workflow
Build & deploy ML models faster on
unstructured data
Collaborative Data Collection & Labelling
Easy-to-use & scalable AI SaaS platform

8. POLL #1
At what stage of Machine learning adoption
your organization is at?
⊚ Exploring - Curious about it
⊚ Planning - Creating AI/ML strategy
⊚ Experimenting - Building proof of concepts
⊚ Scaling up - Some departments are using it
⊚ In production - Using it in product features
⊚ Transforming - AI/Ml driven business

9. How organizations are
leveraging AI & Machine
learning in Customer Service
01

10. Artificial intelligence (AI)
is the ability of a computer to think
and learn like a human
( understand sentiment, keywords,
context etc, and respond appropriately…)
Understanding the fundamentals
Machine learning (ML)
Train models using algorithms to learn
and improve from data without explicit
programming
Natural Language Processing (NLP)
Branch of machine learning that helps
computers understand, interpret and
manipulate human language
(keyword extraction, etc..)

11. ● Improve efficiency
● Provide personalized & Intuitive customer care
● Simplify jobs
How to use AI to improve customer service?
Quick & around the
clock answers to
customer
questions/complaints
Faster case closure by
agents providing stellar
customer experience
Insights discovery
into customer
needs

12. AI for Customer Service
Examples

13. Automated Call/Email Routing
Laura
Amy
Sam
Jessika
Intelligent call routing to assign
calls to relevant agents
Identify customer issues with
social listening and ticketing
Scan and redirect Emails to
the right office/department
Assign queries to relevant customer support

14. Faster Resolution of Cases
Intent discovery to know the
context of the query
Extract contextual data from the knowledge base
“Hi! I What
documents are
needed to open my
bank account?
Priority
High
Inquiry Category
Question
Case Detail
Account Opening
Sentiment
Neutral
Sure. Please see
the document
checklist here. Automated response for user
queries and complaints

15. Virtual agents
Automate informational & transactional cases
Ask for suggestions
Report an issue
Schedule a service call
Transfer complex/unusual
cases to human agents with
contextual data

16. Customer Service Analytics
Improve customer service & satisfaction with insights
Advanced call/chat analytics
to bring faster insight into
customer needs
Analyze text fields in surveys
and reviews to find insights
from customer feedback

17. POLL #2
State your role in the AI initiatives/ projects
in your organization
⊚ We don’t have any AI projects yet
⊚ Practitioner - Data Science/ Engineering background
⊚ Sponsor / Executive
⊚ Product Manager
⊚ Project Manager
⊚ Student
⊚ Others

18. Live Demo of AI & ML in
Customer Service
02

19. 8 stages of Machine Learning workflow

20. Live Demo on automated routing of customer service
inquiries using NLP

21. Best practices to automate
machine learning models
03

22. POLL #3
Some challenges that you are facing while
implementing AI & Machine Learning
⊚ Not started yet, so no challenges`
⊚ Data collection
⊚ Data Labeling
⊚ Large volumes of data
⊚ Identifying the right data set to train
⊚ Lack of knowledge of ML tools
⊚ Lack of end to end platform
⊚ Lack of expertise
⊚ Choosing the right algorithms

23. Data Collection - Flexible options
(CSV bulk upload, APIs, Mobile capture, Form based…)

24. Data Labeling - Simple 4 steps process
(collaboration jobs, guided workflow…)

25. Data Labeling - Real-time early visibility
(class balance, missing data…)

26. Data Labeling - Early Visibility
(data frequency, data intuition, outliers, trends, labeling accuracy…)

27. Data Labeling with Effective Collaboration
(Job allocation, trend, statistics, interactive messaging…)
Manage collaborator
progress, activity,
interactive messaging
Analyse trends and progress
of your data labeling job in real
time with statistics and
interactive visualizations

28. Data Visualization to build strong data intuition
( visuals for data composition, data adequacy)

29. One click training at scale
(Easy feature sets, out of the box algorithms, API integration, hyper
parameter tuning, auto scaling…)
● Train, Deploy and Version your
models by creating feature-sets
in no time with our easy feature
selection provision.
● Choose from state-of-art neural
network algorithms, tune
hyperparameters and see logs for
your training in real time.
● Integrate our powerful inference
API with your application for AI-
driven actionable intelligence.
● Auto scaling of model training
based on data and
hyperparameters

30. Model Monitoring of metrics in real-time
(inference count, execution time, accuracy…)
● Monitor your deployed
models and analyse
inference count, accuracy
and execution time.
● See how your models are
performing in real-time.
No black boxes here.

31. Model Evaluation - Release Confidently
(Accuracy, Precision, Recall, F1 Score)
● Monitor your deployed
models and analyse
inference count, accuracy
and execution time.
● See how your models are
performing in real-time. No
black boxes here.

32. No upfront cost in Infrastructure set up
(no DevOps needed, auto-deploy, SaaS & On-prem models…)
No DevOps required - Incorporates
automatic deployment and dockerization
Scalable tech with latest stack
Domain agnostic build by data type
Scalable on demand
On premise and saas models

33. Skyl.ai - as ML automation platform

34. Try out 15 days free trial with complimentary
consultation on pilot project
Register https://skyl.ai/form?p=start-trial

35. Questions?
contact@skyl.ai
https://skyl.ai/
?

36. 85 Broad Street, New York, NY, 10004
+1 718 300 2104, +1 646 202 9343
contact@skyl.ai
We hope to hear from you soon
Thank you for joining!