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 will learn
- How organizations are building engaging interactions that deliver value to customers
- Best practices to automate AI/ML models
- Demo: How to route customer queries to the right department or professional
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
AI for Customer Service: How to Improve Contact Center Efficiency with Machine Learning
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, Insurance, Manufacturing, Retail and
Ecommerce to define and implement AI strategies
Nisha Shoukath
Co-founder,
People10 & Skyl.ai
The Speaker
3. 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.
Shruti Tanwar
Lead - Data Science
The Speaker
4. Bikash Sharma
CTO and Co-founder at
Skyl.ai
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
5. Getting familiar with ‘Zoom’
All dial-in participants will be muted to enable the presenters
to speak without interruption
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
automated routing of
customer service
inquiries using NLP
How contact centers
are leveraging AI &
Machine learning to
improve efficiency
How to quickly
overcome the
challenges in building
ML models
1 2 3
...In the next 45 minutes
7. Machine Learning automation platform for unstructured data
A quick intro about Skyl.ai
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 contact centers are
leveraging AI & Machine
learning to improve Efficiency01
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
13. Automated Call/Email Routing
Laura
Amy
Sam
Jessica
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. Live Demo of automated
routing of customer service
inquiries in Contact Center02
19. Live Demo on automated
routing of customer service
inquiries using NLP
20. POLL #2
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
⊚ Data Security
⊚ Lack of knowledge of ML tools
⊚ Lack of end to end platform
⊚ Lack of expertise
⊚ Choosing the right algorithms
21. Advantages of a unified
platform Speed, Visibility,
Quality, Collaboration,
Flexibility
03
22. Data Collection - Flexible options
(CSV bulk upload, APIs, Mobile capture, Form based…)
23. Data Collection - Flexible options
(CSV bulk upload, APIs, Mobile capture, Form based…)
24. ⊚ On-prem solutions - Data stays in your own
servers, and in your own databases, giving you complete
control over your data.
⊚ Controlled access flow - Defined and controlled
access flow allows selective restriction so that you have
full command to regulate who can view or use resources
in your ML projects.
⊚ Encrypted data sources - All data sources are
encrypted in Skyl thus giving users an additional layer of
security, making sure your data stays safe and protected.
DataSecurity- on premise solutions
(encrypted data sources, access controlled flow..)
25. Data Labeling - Simple 4 steps process
(collaboration jobs, guided workflow…)
26. Data Labeling - Real-time early visibility
(class balance, missing data…)
27. Data Labeling - Early Visibility
(data frequency, data intuition, outliers, trends, labeling accuracy…)
28. Data Labeling with Effective Collaboration
(Job allocation, trends, statistics, interactive messaging…)
⊚ Analyse trends and
progress of your data labeling
job in real time with statistics
and interactive visualizations
⊚ Manage collaborator
progress, activity, interactive
messaging
29. Data Visualization to build strong data intuition
( visuals for data composition, data adequacy)
30. Data Visualization to build strong data intuition
(visuals for data composition, data adequacy...)
31. 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.
32. 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.
33. ⊚ 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.
Model Evaluation - Release Confidently
(Accuracy, Precision, Recall, F1 Score)
34. No upfront cost in Infrastructure set up
(no DevOps needed, auto-deploy, SaaS & On-prem models…)
No DevOps
required
01
Latest tech
stack
02
On premise
and saas
models
03
Scalable
On
demand
04
35. Skyl.ai - as ML automation platform
Efficient
Data Management
Solve your data issues; collect and manage data
efficiently
Accuracy
& Quality
Maintain accuracy and quality; train and test
faster; monitor quality
Effective
Collaboration
Collaborate and manage projects efficiently
Early
Visibility
Get early visibility; visualize and affirm correctness
on every step of the way
Scalable
High - Performance
Access on-demand and scalable, high-
performance infrastructure
Reduce
Cost
Reduce cost of implementation; do it with less
specialized resources
36. ⊚ Personalised demo
⊚ 15 days free trial with data credits
⊚ Complimentary consultation on pilot project
⊚ AI Implementation Playbook
www.skyl.ai contact@skyl.ai
Offers for you...
38. We hope to hear from you soon
Thank you for joining!
Editor's Notes
Hello everyone and welcome. Thank you for joining today’s webinar on How to Improve Contact Center Efficiency with Machine Learning? My name is Edwin and I’ll be your host today. First off, I’d like to introduce 3 expert speakers for today’s webinar..
First we have Nisha Shoukath - Nisha is a technology entrepreneur with background in investment banking.
She’s co-founded two successful technology startups and has worked with a wide variety of global organizations from different industries.
She helps enterprises with defining AI strategy, and AI adoption roadmaps. Welcome, Nisha!
Next we have Shruti Tanwar - Shruti is an expert in data science who is a veteran in building SaaS products using Machine Learning and AI.
Her expertise includes Deep Learning and Data Analysis, as well as full stack development and building tech products in various different fields such as ecommerce, travel, and healthcare. Welcome, Shruti!
Finally, we have Bikash Sharma joining us today.
Bikash is CTO and Software Architect with over 15 years of experience in leading innovative software projects and solutions.
He’s co-founded Skyl with his expert knowledge in AI and Machine Learning. Welcome, Bikash!
Before we begin, I’d like to briefly talk about some relevant Zoom features.
All participants in the webinar will be muted to avoid any interruptions during the session.
Any questions you might have can be submitted to the Zoom Questions chat window in the control panel, located on the bottom of the screen.
We’ll make sure to address your questions during the Q&A session.
Also, the recording of the webinar will be emailed to you afterwards, just in case you’ve missed any talking points or wish to view it again.
So that’s all for the introduction - now we’ll get started with the webinar and I’ll hand over the session to Nisha
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
No more phone trees or juggling with 5-6 cases at a time. AI can automate simple, common interactions, doing handoffs to live agents when needed.
Crisp and increase the font size
Speed up the recruitment process by automating
time-consuming & repetitive tasks
Speed up the recruitment process by automating
time-consuming & repetitive tasks
live agents get recommendations in real time about knowledge sources that can help resolve customer issues more quickly and helpfully.
Speed up the recruitment process by automating
time-consuming & repetitive tasks
Machine learning uncovers and categorizes popular customer questions along with all their variations, helping analysts more quickly formalize responses that will please those customers.
How
5 minutes intro - 10 industry awareness - 15 min demo - 20 minutes QnA
Define problem - Features model - How this model is built using skyl.ai
Add slide of Pneumonia detection
We don’t have any AI projects yet
Practitioner - Data Science/ Engineering background
Sponsor
Product Manager
Project Manager
Student
Others
Benefit
On-prem solutions
For industries, where business depends upon sensitive data, Skyl provides the provision of on-prem solutions. Your data stays in your own servers, and in your own databases, giving you complete control over your data.
Access controlled flow
Defined and controlled access flows with different organizational roles like business owner, project lead, collaborators etc. allow for selective restriction so that you have full command to regulate who can view or use resources in your ML projects.
Encrypted data sources
All data sources are encrypted in Skyl thus giving users an additional layer of security, making sure your data stays safe and protected.
Now, we
Thank you Nisha and Shruti, for the wonderful presentation and demo.
As mentioned earlier, the recording of the webinar will be emailed to you afterwards. [pause]
Before we get to the Q&A, I want to mention some of the offers Skyl has for those of you that are curious about incorporating Machine Learning to your business.
Skyl offers a personalized demo as well as a 15 days free trial.
You’ll be able to interact with real data on the screen, just like we showed in the demo. You’ll experience the process of going from collecting & labeling the data… all the way to deploying a model!
Skyl also offers a complimentary consultation on a pilot project of your choice and an AI implementation playbook to go along.
This is a great opportunity to see how Skyl can provide Machine Learning solutions to your challenges.
If you’re interested in finding out more, please visit the skyl.ai website or you can send an email directly to contact@skyl.ai.
Alright, now it’s Q&A time!
As a reminder, if you have any questions, go to the question box in your control panel - located on the bottom of your Zoom screen.
We’ll try to answer as many questions as possible in the time that we have left.
So let’s answer some questions.
Sample questions:
For me/ Bikash:
-(Olivia) How do I know if my model performance is going down, and how do I fix it?
-(Freddy) Can Skyl help me in figuring out if my data needs re-labelling?
- How much is the devops effort in building a model deployment pipeline in Skyl?
For Nisha:
-(Emma) Can I upload unlimited data? Or is there some kind of subscription system I need, to use Skyl?
-(Alem) Apart from text, can I use Skyl for image based data, like screenshots, to build model for my customer center??
Ok, that’s all the time we have for questions today, but feel free to contact us with your specific questions and we’ll make sure to get them answered.
All right, so we have reached the end of the webinar.
We hope you enjoyed it.
We have a lot more webinars coming up on different machine learning topics and how they can be implemented into different businesses and industries,
So don’t miss out and make sure you sign up for upcoming webinars as well
Thank you for joining and I hope you have a wonderful day.