AI, Natural Language Understanding & Text Mining Agenda : 6:30 - 6:45 PM: News and Introduction 6:45 – 7:30 PM: Text mining for content marketing 7:30 - 8:15 PM: Conversational AI for brands 8:15 - 8:30 PM: Wrap-up

1. Data Science | Design | Technology
https://www.meetup.com/DSDTMTL
March 20
2019

2. Agenda
6:30 - 6:45 Intro & News
6:45 - 7:30 Text mining for content marketing
7:30 - 8:15 Conversational AI for brands
8:15 - 8:30: Wrap-up
2
Conversational AI & Text Mining
in Marketing

3. 3
A special thanks to our contributors…
Lorem ipsum congue
tempus
Lorem ipsum
tempus
Lorem ipsum congue
tempus
Lorem ipsum
tempus
Lorem ipsum
congue tempus
Lorem ipsum congue
tempus
Thanks
Merci
&
venue
sponsor
& snacks
brains

4. 4
• Back-end developer (Python)
• Front-end developer (React)
is hiring
https://jobbio.com/companies/concured/job
s/back-end-developer-9
https://jobbio.com/companies/concured/job
s/react-front-end-developer
www.concured.com

5. 5
is hiring
https://automat.breezy.hr/
• AI Researcher
• Chatbot Developers
• Product Manager
… and more...

6. 6https://careers.smartrecruiters.com/IVADOLabs
• Data Architect
• Data Scientists - ML
• Data Engineers
• Software Engineers / Full Stack
Developers
… and more…
is hiring
www.ivadolabs.com

7. 7
Ecosystem News
● March 26: Data-Driven Montreal - Advanced Retail Analytics
https://www.meetup.com/DataDrivenMTL/events/259578243/
● March 27: Beer, ETL, and Data Integration
https://www.meetup.com/DataDrivenMTL/events/259607783
● March 28: Docker Bday#6
https://www.meetup.com/Docker-Montreal/events/259762456
● April 10-11: World Summit AI - Americas (Montreal):
https://americas.worldsummit.ai/

8. 8
Conversational AI & Text Mining
Tom Wilson
“Internet-Scale Text Mining
for the Content Marketing
Industry"
Erdem Özcan
“Conversational AI for Brands”

9. Internet-Scale Text
Mining for the
Content Marketing
Industry
Data Science | Design | Technology 9

10. Text Mining in Content Marketing
Tom Wilson, CTO

12. Identifying Winning
Topics
1. What problem did we solve?
2. How?
3. Science
4. Product
5. Challenges

13. Concured finds TOPICS interesting to clients’ audiences
How should we define “interesting”?

15. Social Engagements are a proxy for reader interest
KEYWORDS: deep learning, AI, marketing, future of employment, automation, personalization

16. Match Articles with their Social Engagements

17. Do this at Scale…

18. Combine the data: Text, Video, Social

19. Every Keyword gets a Score

20. Each Brand: Keyword Freq & Engagement stats

21. Which brands / publishers to analyze?
Which Brands & Publishers to analyze?

22. Publishers who target a similar audience

23. “Borrow” winning ideas from your competitors

24. Identifying Winning
Topics
1. What problem did we solve?
2. How?
3. Science
4. Product
5. Challenges

25. From Articles to Entities

26. From Articles to Entities
• Named Entity Recognition
• APIs (e.g. Dandelion, TextRazor, …)
• Open Source code (e.g. Spotlight, WAT)
• Keyword Relevance Scoring
• Weightings to Title, 1st para, …
• Tf-idf, PoS
• Doc2Vec Similarity to labelled documents
• Pre- & post-processing

27. Multilingual Relational Ontology

28. Multilingual Relational Ontology

29. Advantages of Ontology
• Blacklisting & Whitelisting
• Keyword search expansion with “children”

30. Other applications of NLP within the platform:
• Named Entity Recognition
• Keyword Relevance Scoring
• 7-day keyword trending
predictions
• Industry Classification
• Topic Modelling x2

31. Identifying Winning
Topics
1. What problem did we solve?
2. How?
3. Science
4. Product
5. Challenges

32. Content Marketing Cycle
Source: ACQUISIO, 2015 https://www.acquisio.com/blog/agency/top-reasons-why-content-marketing-is-vital-for-

33. Which content should we promote?

34. Track your Content Performance

35. How well do we perform on this keyword?

36. Identifying Winning
Topics
1. What problem did we solve?
2. How?
3. Science
4. Product
5. Challenges

37. THEORY REALITY
Implementation, implementation, implementation…

38. Building on “Open Data”
• Internet content
• Linkedin API
• Facebook API
• Twitter API
?

41. We’re hiring!
Tom Wilson
CTO, CONCURED
tom.wilson@concured.com 20th March 2019

42. Conversational AI
for brands
Data Science | Design | Technology 42

43. Beauty in the age of messaging - 2018 Consumers Survey -

44. SUPPORTCOMMERCEMARKETING
Conversational
display advertising
Alexa &
Google Home
Voice search to product
recommendation
Virtual sales
advisors
FAQ & Human
Handoff
Proprietary AI Capabilities Expert Delivery & Success TeamConversational Cloud Platform
G.R.A.C.E.
Guidance, Recommendation, Advice, Consultation, Expertise
We provide connected conversations,
throughout the customer journey

45. Introducing
Conversational
Display Advertising
On February 13th, 2019, Automat
announced its partnership with
Google-incubated company AdLingo,
to bring Conversation AI to Google
DV360
+ +

46. Who are we?

47. Beauty in the age of messaging - 2018 Consumers Survey - 47
65%prefer to be left alone in store
71%use mobile phones to do
research while in store
* Wakefield Research - May 2018
66%are overwhelmed and confused
by the number of products
Consumers need expert
advice and guidance both
online and offline

48. Investors - Advisors
Gary Clayton
Chief Creative Officer
Ron Croen
Founder/CEO
Amy Banse
Adobe Board Member
Tim O’Reilly
CEO/Author
Richard Socher
Chief Scientist

49. We’re the Conversational Marketing leader

50. Select Customers

51. Virtual sales advisors are available 24/7 to chat with
your consumers and guide them to the best products,
in a fun and personalized way.
Messaging Artificial
Intelligence
+ =+
Product
Expertise

52. Virtual Sales Advisor for Skincare
• Understands a range of typed inputs related to user
preference and product categories.
• Only advises consumers on things they care about
• Consumers can interrupt with additional preferences
• Users can type instead of tap at any point
• Makes personalized recommendations, for the right
product category and user preference
• Ranks recommendations based on how well they fit the
user’s preferences
• Explains why a product makes sense for the user’s specific
preference to build consumers’ confidence

53. Conversational AI Fallacies we address
to build the best platform for brands
Technology Deep Dive

54. Fallacy #1:
Understanding text is about parsing intents and entities
A natural conversation is made of
multi-turn and multi-modal
interactions

55. Fallacy #2
You need ML scientists for building conversational applications
Designers and marketers understand consumer need, know how to talk/text and
should be empowered to build the conversational experiences

56. Fallacy #3:
You need big datasets to train your models
Conversational data isn’t readily accessible
Conversational AI is new
It’s not just about customer service
Academic datasets don’t scale to production
Solutions can’t rely on millions of high-quality conversations
Adapt your algorithms to small data
Leverage ontological models when suitable

57. Converse.
Connect.
Convert.

58. Merci / Thank You
@DsdtMtl
Data Science | Design | Technology
(Check for next DSDT meetup at https://www.meetup.com/DSDTMTL)
http://bit.ly/dsdtmtl-in