Discover Practical AI use cases in Customer Service! In this webinar, you will learn how to lower the time to first response and time to resolution to keep your SLAs intact, as well as about chatbots, ticket tagging, and urgency detection. We will also mention some technologies, such as text recognition and sentiment analysis.

1. Practical AI use cases in
Customer Service
Denys Holovatyi
Data Science and AI consultant @
EnterpriseAI Consulting
WEBINAR 1 OF 3
Anna Augé
Senior Project Manager AI
Freelance

2. Speakers
Denys Holovatyi
Data Scientist & Founder
EnterpriseAI Consulting
Anna Augé
Senior Project Manager AI
Freelance

3. 04
0201
03
Practical AI use cases
Next steps
Why AI in customer
service?
Interview with our
expert

4. • Why AI
• What is customer service?
• How AI and CS work together
Why AI in customer
service?

5. Why AI, why today?
https://www.pwc.de/de/digitale-transformation/kuenstliche-
intelligenz/kuenstliche-intelligenz-in-unternehmen.html
$15.7 trillion
AI will add this much to the global economy until 2030

6. Why AI, why today?
https://www.gartner.com/en/newsroom/press-releases/2019-
01-21-gartner-survey-shows-37-percent-of-organizations-have
270% growth
AI use in companies over the past 4 years

7. Example: Text generation – OpenAI GTP-2
https://openai.com/blog/bet
ter-language-models/

8. Example: Text generation – OpenAI GTP-2
https://openai.com/blog/bet
ter-language-models/

9. Customer Service
Conversation
CS is a conversation
/ interaction with the
customer
Document
CS is about tickets,
requests, cases
Business process
CS is there to solve
specific problems of
the customers
Cost center
(CS is a P&L line)
How do you understand it?

10. Businessprocess Create
ticket
Set to: in
progress
Resolve
ticket

11. Conversation

12. Profit & Loss statement
Some Expense (10,000,000.00)
Customer service (12,725,346.24)
Some other expense (10,000,000.00)

13. Document
Support Ticket 743775
Issue description Customer cannot access the page to..
Customer Peter ver Ymportant
Submitted by Meg Doe
Priority Major incident
Status In progress
Category Website issue
Service representative John Roe
Supervisor Ed Poe
Tasks
1. Be nice
2. Do things
3. Do things again
4. Make the customer happy
Assignment group Web Crew 101

14. How do you understand Customer
Service?
Write in the chat or Q&A!

15. • CS process & context
• AI use casesPractical AI use cases

16. Customer Service context
Customer characteristics
• Demographics
• Psychographics
• Behavior
Customer relationship
• Incident history
• Lifetime value
• Churn probability
Key KPIs
• CSAT
• Resolution time
• ROI

17. Customer Service process
Create ticket Assign ticket
Set to: in
progress
Escalate ticket Resolve ticket
● Incoming
channel
● Chatbot
response
● Incident
history
● Intelligent
routing
● Auto tagging
● Urgency
detection
● No multi-hops
● Min escalation
● Knowledge
management
● Digital
assistants
● Utilization vs.
resolution
times
● Auto
escalation
● Major incident
detection
● CSAT effect
prediction
● Resolution
time prediction
STEP
AI POTENTIAL

18. Use case: predicting resolution times
● Get the data from your CS system (e.g., Zendesk, Salesforce for
Services, ServiceNow)
● Model your CS process with the most time-consuming steps. Identify the
bottlenecks
● Train an ML model to predict which steps can delay ticket resolution
What's that
A process mining & machine
learning model that estimates
when a given ticket will be
resolved
How it works
Why it matters
Keeping SLAs in check,
managing customer
expectations, improving CSAT

19. Use case: urgency detection / ticket tagging
● Get the data from your CS system (e.g., Zendesk, Salesforce for
Services, ServiceNow)
● Classify the data into urgent / not urgent cases (or other types of tags)
● Train an NLP model to understand & predict new cases based on the
sentiment in the ticket description
● Train an ML model to predict new cases based on time of day, category,
device, and customer characteristics
What's that
Detecting urgency of a
customer request & tag
tickets based on their content
/ description
How it works
Why it matters
Decrease reassignments,
prioritize requests, increase
CSAT

20. Use case: voice & call analysis
● Integrate call recordings into the analytical database / warehouse
● Implement a speech-to-text solution
● Implement a sentiment analysis solutions
● Train & adapt to improve performance of voice analysis
● Rollout to CSRs to help them track checklist progress
What's that
AI-based voice recognition tool
that transcribes & analyzes calls
between customers & CSRs
How it works
Why it matters
It helps to understand how the calls
with customers are going, if the
script is being followed, and which
interactions perform well
https://www.cognizant.com/case-studies/pdfs/customer-care-
done-right-with-real-time-ai-codex3598.pdf and
https://www.chorus.ai/case-studies/engagio/

21. Use case: upsell prediction
● Get the data from the chat system
● Integrate this data with CRM data
● Train an NLP model to understand customer’s journey stage & buying
propensity
● Train an ML model to identify optimal contract and device package
What's that
AI-based prediction tool that
identifies potential for new data
plans, new smartphone etc
How it works
Why it matters
Generates revenues,
transforms CS into a profit
center
https://www.mindtitan.com/wp-
content/uploads/2018/11/CaseStudy_Elisa_Final-1.pdf

22. Use case: chatbot for better response times
● Identify chatbot skills (what it can do)
● Design chatbot dialogues together with support reps
● Identify channels & adapt dialogues
● Deploy chatbot in productive systems
What's that
Chatbots are intelligent chat
assistants that can handle
some of the customer’s
questions
How it works
Why it matters
Chatbots can lower waiting
time, respond outside of
service hours, and lower call
duration

23. As we’re switching to the interview with
Anna, write your questions about the use
cases!

24. • Interview with Anna Augé
Interview with our
chatbot expert

25. Interview with Anna Augé
Denys Holovatyi
Data Scientist & Founder
EnterpriseAI Consulting
Anna Augé
Senior Project Manager AI
Freelance

26. Ask us questions!

27. Next Steps

28. Register here

29. Do the homework
Define Top-3 problems in your customer
service department
Apply AI use cases at them
Send me your ideas
denys@denysholovatyi.com
I will come back with a quick
feasibility assessment

30. Denys Holovatyi
Data Scientist & Founder
EnterpriseAI Consulting
denys@denysholovatyi.com
+49 176 7530 3871
Pau Cerdá
Co-founder
Outvise
pau.cerda@outvise.com
+34 607 272728
Contact
Anna Augé
Senior Project Manager AI
Freelance
m306auge@gmail.com
+49 157 5018 4159