Automating customer relation through AI chatbots

1. AI & CHATBOTS
Automating customer relation

2. 20 people in 2017
7 people in R&D
13 people in the Account & Project team
100% excellency
2
Who are we?

3. Introduction
Defining the concept of AI
✓ Data-powered
✓ Algorithmic science
✓ Machine Learning

4. Introduction
The notion of chatbots is nothing new
✓ Eliza in 1964
✓ Cleverbot in 1997
✓ Bots for Messenger in April 2016

5. Plan
I. Chatbots: state of the art
II. What does a chatbot project entail?
III. In the future

6. Chatbots: State of the art

7. Plan
1. Intro
2. AI and current Chatbots
3. Hopes vs reality
4. Natural Language Understanding
5. Our approach

8. “30 or 40% of our clients’ messages are recurrent and could
be partly automated. ”
Axa France
In China, a brand will
create a bot on WeChat
before creating a website.
3 billion
users
An indeniable trend

9. An indeniable trend

10. Conversation Platforms Online Chat
Messaging apps (FB
Messenger, Slack...)
Intercom
iAdvize (Q2 2017)
Zendesk (Q2 2017)
SMS Vocal
Twilio
Phone
Alexa / Google
Assistant (Q3 2017)
Current integrations

11. Problem

12. AI and current Chatbots
An undeniable trend
April 2016: 30 000 bots created in 3 months but…
« Bots right now are in the trough of despair. To industry
observers, it feels like they are overhyped and under-delivering. »
Greg John, CEO of Burner
Current chatbot technology is nothing new. It becomes
interesting when chatbots meet AI.

13. Hopes vs Reality
Understanding the limits of AI Chatbots
✓ Bots are not yet intelligent
(language, context)
✓ Questions need to be predicted
✓ Answers need to be written in
advance
✓ Complicated features take time
to develop
Some bad examples
Х Tay: Microsoft’s error in 2016
Х M: Facebook’s perfect bot

14. Natural Language Understanding
The basics
To build a chatbot able to converse with human, you need NLU technology.
✓ Data-powered
✓ Detecting intentions
✓ Focused on keywords and trigger
words
✓ Understanding words in contexts

15. Our approach
Data-based chatbots
✓ We analyze historical datasets
✓ We detect FAQs
✓ We map those questions and detect intentions
✓ We input this data in the bot

16. Example
16

17. 17
Bot Admin &
Analytics
Your Data
Pull Data
Push API
Your Data
Push Data
Webhook
Trigger actions and
answers (Conversation
Management platform)
Intent detection
(Clustaar Deep Query)
User input
Integration with data

18. Log analysis
✓ After a few days in production, then once a month,
unanswered questions go through our algorithm Deep
Query©.
✓ New “intents” are identified and made accessible to the
bot. They need to be then associated with the
appropriate actions.
✓ The bot becomes can handle more and more cases on
its own.

19. What does a chatbot project entail?

20. Plan
1. How to envision a bot project?
2. Use cases
3. How to make it great?

21. Envision a chatbot project
Designing a new user experience
✓ Define the objectives of the bot
✓ Adapt to the target
✓ Recreate user habit
✓ Transform them into conversation
✓ Imagine client reactions

22. Possible use cases
All recurrent interactions
Internal
(ex: Nexity)
- HR
- Knowledge
management
- Search in databases
- FAQ
Client acquisition
(ex: Cortex)
- Integrated to a
website
- Automatic lead
generation
- FAQ
- Available 24/7
Service
(ex: Hachette)
- Playful features
- Geolocalisation / Store
locator
- Promotional offers or
news Push
- FAQ
Customer service
(ex: 20 Minutes)
- Integrated to a website
or Messenger
- Automatic FAQ in
Natural Language
- Available 24/7

23. Client acquisition Customer service

24. How to make it great
Put a smile on the client’s face
✓ Smalltalk: simulating human interaction and setting a tone
✓ Fallback: building a fluid conversation
✓Playful features: jokes, quiz…
Find the detail that will make the user say « thank you! »

25. Data import Bot training Put the bot online Run, manage & improve
Internal sources (FAQ,
conversations) and external
sources (Google Queries)
Intent detection
Connexion with internal data
Writing scenarios and
answers
UX & integration
Publication
Fine tuning
Machine learning
improvement & Analytics
Improving response
scenarios
25
Project phases

26. In the future

27. Plan
1. Outstanding Conversation
2. Understanding emotions
3. Personnalized insurance

28. Outstanding conversation
Flawless automated client interaction
✓ Understanding complex questions
✓ Automatically generated answers
✓ 100% automated customer care
✓No interface ?

29. No interface
All vocal conversation

30. Understanding emotions
Mesuring client satisfaction automatically
✓ Live sentiment analysis
✓ Emotionaly appropriate response
✓ Automatic report on client satisfaction

31. Personnalized insurance
Data collection to a new level
✓ Perfect perception of clients’ profiles
✓ Automatic daily personnalization

32. Personnalized insurance
It’s all about data

33. Personnalized insurance
It’s all about data

34. www.clustaar.com/en
Mail
nicolas@clustaar.com
philippe@clustaar.com
Phone
Nicolas Chollet
+33 (0) 6 51 42 79 05
Philippe Duhamel
+33 (0) 6 84 33 76 23
Offices
28 rue du faubourg
poissonnière
75010, Paris
France