How can an algorithm learn to be the best Go player in the world with no prior experience? Have chatbots finally become the answer to your business problems? In this webinar, Ogilvy’s Michael McCarthy and Andrew Van Aken will update you on news within the artificial intelligence and machine learning sector, why it is important and how Ogilvy is using some of this technology.

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CLOUDY WITH A CHANCE OF AI

2. Hello!
Andrew Van Aken
Senior Consultant,
OgilvyOne Worldwide
José Arteaga
Creative Digital Strategist,
OgilvyRED
Michael McCarthy
Senior Consultant,
OgilvyOne Worldwide

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5. This Talk
• A Look at Chatbots
• Intro to Reinforcement Learning
• Ogilvy Case Study

6. A Look at Chatbots

7. We’ll Talk About…
• What are Chatbots and why are they so popular?
• What are some of the pitfalls? Why do some Chatbots Fail?
• How can a business evaluate if a Chatbot is worth the investment?

8. We’ll Talk About…
• What are ChatBots and why are they so popular?
• What are some of the pitfalls? Why do some Chatbots fail?
• How can a business evaluate if a Chatbot is worth the investment?

9. We’ll Talk About…
• What are ChatBots and why are they so popular?
• What are some of the pitfalls? Why do some Chatbots Fail?
• How can a business evaluate if a Chatbot is worth the investment?

10. What are Chatbots?
Chatbots are automated interfaces designed to simulate an intelligent
conversation with a human user via text

11. Sephora
• Facebook Messenger Platform
• Designed for a specific purpose
• Conversations are linear

12. Replika
https://www.popsugar.com/news/Replika-Bot-AI-App-Review-Interview-Eugenia-Kuyda-44216396
• Mobile App (iPhone and Android)
• An “AI Friend”, no specific purpose
• Pushing the limits of NLP and
intelligent learning

13. GymBot
• Facebook Messenger
• Drive to fitness videos
• Conversational, but with limits

14. GymBot

15. GymBot

16. Spectrum of Chatbots
?

17. Spectrum of Chatbots
Chatbot may start
conversation, but it can only
gather information and will
quickly re-route to an agent

18. Spectrum of Chatbots
Chatbot may start
conversation, but it can only
gather information and will
quickly re-route to an agent
Chatbot is programmed to
handle certain types of task to
completion, but relies on agent
for more complicated inquiries

19. Spectrum of Chatbots
Chatbot may start
conversation, but it can only
gather information and will
quickly re-route to an agent
Chatbot is programmed to
handle certain types of task to
completion, but relies on agent
for more complicated inquiries
Chatbot can handle all but the
most complicated issues, and
can learn from new data and
customer interactions

20. But how do they work?

21. How do they work?
I. Rule Based
II. Machine Learning
III. Artificial Intelligence

22. How do they work?
I. Rule Based
II. Machine Learning
III. Artificial Intelligence
Input
Intent
Intent
Intent
Intent
Response
Response
Response
Response

23. I. Rule Based
Hi! How can I help you? You
can say (1) Get a Quote (2)
Question About Billing or (3)
Other
Chatty the Generic Auto Insurance Bot

24. I. Rule Based
Hi! How can I help you? You
can say (1) Get a Quote (2)
Question About Billing or (3)
Other
Get a quote.
Chatty the Generic Auto Insurance Bot

25. I. Rule Based
Hi! How can I help you? You
can say (1) Get a Quote (2)
Question About Billing or (3)
Other
Get a quote.
Chatty the Generic Auto Insurance Bot
Chatbot tries to map user input to a set of
pre-defined intents
Quote Billing Other

26. I. Rule Based
Hi! How can I help you? You
can say (1) Get a Quote (2)
Question About Billing or (3)
Other
Get a quote.
Chatty the Generic Auto Insurance Bot
Chatbot tries to map user input to a set of
pre-defined intents
Quote Billing Other

27. I. Rule Based
Hi! How can I help you? You
can say (1) Get a Quote (2)
Question About Billing or (3)
Other
Get a quote.
Ok great! To start, what is
your first and last name…
Chatbot tries to map user input to a set of
pre-defined intents
Quote Billing Other
Response
Chatty the Generic Auto Insurance Bot

28. I. Rule Based
Hi! How can I help you? You
can say (1) Get a Quote (2)
Question About Billing or (3)
Other
I’d like to insure an
automobile.
Chatty the Generic Auto Insurance Bot

29. I. Rule Based
Hi! How can I help you? You
can say (1) Get a Quote (2)
Question About Billing or (3)
Other
I’d like to insure an
automobile.
Chatbot has to be manually programmed
for each intent and response
Quote Billing Other
Chatty the Generic Auto Insurance Bot

30. I. Rule Based
Hi! How can I help you? You
can say (1) Get a Quote (2)
Question About Billing or (3)
Other
I’d like to insure an
automobile.
I’m sorry, I didn’t quite
understand. Would you like to
(1) Get a Quote (2) Question
About Billing or (3) Other
help.
Quote Billing Other
Error
Chatty the Generic Auto Insurance Bot
Chatbot has to be manually programmed
for each intent and response

31. II. Machine Learning
Hi! How can I help you today?
Chatty the Generic Auto Insurance Bot

32. II. Machine Learning
Hi! How can I help you today?
Chatty the Generic Auto Insurance Bot
Chatbot can now accept a
free-form response

33. II. Machine Learning
Hi! How can I help you today?
Chatty the Generic Auto Insurance Bot
I’d like to insure an
automobile.
Chatbot can now accept a
free-form response

34. II. Machine Learning
Hi! How can I help you today?
Chatty the Generic Auto Insurance Bot
I’d like to insure an
automobile.
Chatbot tries to map user input to a set of
pre-defined intents
Chatbot can now accept a
free-form response

35. II. Machine Learning
Hi! How can I help you today?
Chatty the Generic Auto Insurance Bot
Chatbot tries to map user input to a set of
pre-defined intents
“like to, automobile, insure ”
Probability
I’d like to insure an
automobile.
Chatbot can now accept a
free-form response

36. II. Machine Learning
Hi! How can I help you today?
Chatty the Generic Auto Insurance Bot
Chatbot tries to map user input to a set of
pre-defined intents
“like to, automobile, insure ”
Probability
Quote Billing Other
I’d like to insure an
automobile.
Chatbot can now accept a
free-form response

37. II. Machine Learning
Hi! How can I help you today?
Chatty the Generic Auto Insurance Bot
Chatbot tries to map user input to a set of
pre-defined intents
“like to, automobile, insure ”
Probability
Quote Billing Other
I’d like to insure an
automobile.
Response
Ok, I can help you with that.
To start, what is your first and
last name…
Chatbot can now accept a
free-form response

38. III. Artificial Intelligence
Hi! How can I help you today?
Chatty the Generic Auto Insurance Bot

39. III. Artificial Intelligence
Hi! How can I help you today?
Chatty the Generic Auto Insurance Bot
I’d like to know more about your
automobile insurance.

40. III. Artificial Intelligence
Hi! How can I help you today?
Chatty the Generic Auto Insurance Bot
I’d like to know more about your
automobile insurance.
Natural Language Processing

41. III. Artificial Intelligence
Hi! How can I help you today?
Chatty the Generic Auto Insurance Bot
I’d like to know more about your
automobile insurance.
Natural Language Processing
“like to, know more, automobile”

42. III. Artificial Intelligence
Hi! How can I help you today?
Chatty the Generic Auto Insurance Bot
I’d like to know more about your
automobile insurance.
Natural Language Processing
“like to, know more, automobile”
Probability

43. III. Artificial Intelligence
Hi! How can I help you today?
Chatty the Generic Auto Insurance Bot
I’d like to know more about your
automobile insurance.
Natural Language Processing
“like to, know more, automobile”
Probability
Certainly. We offer insurance
for both commercial vehicles
and privately owned…
…
Was this information helpful?
Response

44. III. Artificial Intelligence
Hi! How can I help you today?
Chatty the Generic Auto Insurance Bot
I’d like to know more about your
automobile insurance.
Natural Language Processing
“like to, know more, automobile”
Probability
Certainly. We offer insurance
for both commercial vehicles
and privately owned…
…
Was this information helpful?
Response
Yes! Thank you.

45. III. Artificial Intelligence
Hi! How can I help you today?
Chatty the Generic Auto Insurance Bot
I’d like to know more about
my auto insurance bill.
Natural Language Processing
“like to, know more, automobile”
Certainly. We offer insurance
for both commercial vehicles
and privately owned…
…
Was this information helpful?
Response
No, I’m looking for something
else.
Probability

46. III. Artificial Intelligence
Hi! How can I help you today?
Chatty the Generic Auto Insurance Bot
I’d like to know more about
my auto insurance bill.
Natural Language Processing
“like to, know more, automobile”
Certainly. We offer insurance
for both commercial vehicles
and privately owned…
…
Was this information helpful?
Response
No, I’m looking for something
else.
Probability

47. Rule Based Machine Learning Artificial Intelligence
Powering a Chatbot
• Doesn’t try and do too much
• Proven technology /
interface
• Easier analytics
• Not conversational
• Not likely to impress
customers
• Still requires careful
programming to handle
mapping of intents
• Analytics are more
complicated
• Not accessible to the vast
majority of companies
• Newer technology, which has
its own pitfalls
• Can be conversational and
engaging
• Technology has advanced
considerably and is now
more readily available
• Allows for innovation
• Can drastically improve
customer service costs
• Potential to be leader in
industry

48. So… why are Chatbots so popular?

49. The Rise of the Chatbot
Messaging Apps are Outpacing Social Networking Apps
https://learn.octaneai.com/the-complete-beginners-guide-to-creating-a-bot-on-facebook-messenger-1b557b971fb9

50. The Results Are Promising
100,000
bots within first
year of launch
80% of customers re-used
Chatbot within 2-weeks of
launch
266% increase in
conversion rate versus
average social ad
https://www.smartmessage.com.tr/impressive-numbers-stats-chatbot-dominance-far/

51. Why Do Some Chatbots Fail?
:(

52. Some potential causes for concern…
2016 2017March 2016
Microsoft’s Tay Chatbot on Twitter goes on spamming
rampage. Begins to spout racist, genocidal and
misogynistic messages to users.

53. Some potential causes for concern…
2016 2017March 2016
July 2017
Microsoft’s Tay Chatbot on Twitter goes on spamming
rampage. Begins to spout racist, genocidal and
misogynistic messages to users.
Facebook bots start talking to
each other and forming their
own language.

54. Some potential causes for concern…
2016 2017March 2016
July 2017
August 2017
Microsoft’s Tay Chatbot on Twitter goes on spamming
rampage. Begins to spout racist, genocidal and
misogynistic messages to users.
Facebook bots start talking to
each other and forming their
own language.
Two chatbots pulled from
Chinese messaging app
(QQ) after questioning
the rule of the Communist
Party

55. • Rule based logic is insufficient to meet customer inquiries
• Chatbot doesn’t work with existing business systems
• Weak use case
What Else Can Go Wrong?

56. Companies pull back
• Facebook shrunk its AI efforts after its bot API fail rate hit 70%
• Everlane, announced that it’s removing several features of its
Messenger bot and returning to email

57. Is a Chatbot Right for Your Business?

58. Determining if a Chatbot is Right for Your Business
1. Sooner you deploy, sooner you can iterate and learn
2. Chatbots are seeing record engagement rates
3. Customer service bots can alleviate other costs and have a direct
ROI

59. Deploying a Chatbot
Analyze Current
Data
1

60. 2
Deploying a Chatbot
Analyze Current
Data
Have a Clear
Purpose
1

61. 2
Deploying a Chatbot
Analyze Current
Data
Have a Clear
Purpose
Define Key
Metrics
1 3

62. 2
Deploying a Chatbot
Analyze Current
Data
Have a Clear
Purpose
Define Key
Metrics
Test and
Optimize
1 3 4

63. Intro to Reinforcement Learning

64. First, let’s review last webinar

65. To Review

66. To Review

67. To Review
New Example: David Ogilvy

68. How can we learn without data?

69. Enter Reinforcement Learning
• An “educated” guess and check, with rewards
• No starting data needed, just the rules of the road…

70. Every Time You See Breakthrough
• Book published in 1998…

71. What is Reinforcement Learning?
Agent

72. What is Reinforcement Learning?
Agent
Takes an Action

73. What is Reinforcement Learning?
Agent
Takes an Action
Environment

74. What is Reinforcement Learning?
Agent
Takes an Action
Environment
Observation/Reward
+1

75. What is Reinforcement Learning?
Agent
Takes an Action
Environment

76. What is Reinforcement Learning?
Agent
Takes an Action
Environment
Observation/Reward
Observation/Reward

77. What is Reinforcement Learning?
Agent
Takes an Action
Environment
Observation/Reward
-100

78. What is Reinforcement Learning?
Agent
Takes an Action
Environment
Observation/Reward

79. What is Reinforcement Learning?
Agent
Takes an Action
Environment

80. Video Games!

81. AlphaGo Zero

82. Getting to Greatness

83. Getting to Greatness
VS

84. Getting to Greatness
VS

85. Data/Accuracy

86. Data/Accuracy

87. Getting More Efficient

88. Will This Method Take Over?
• If we just knew how the stock market works…
• Or how proteins folded…

89. Yann LeCun
• “If intelligence was a cake, unsupervised learning would be the cake,
supervised learning would be the icing on the cake, and
reinforcement learning would be the cherry on the cake. We
know how to make the icing and the cherry, but we don’t know how to
make the cake.”

90. Look a Like Modeling

91. What is it?
• Building larger segments from smaller ones

92. Ogilvy Use Case - Everywhere
• B2B/B2C
• Across all mediums

93. Pretzel Company
• How would a pretzel company increase sales?

94. Pretzel Company
Company City Employees Alcohol Pretzels
Pete’s Bar 1 45 1 12
Cinema 8 0 23 0 4
Cues and
Shoes
0 140 1 1
Popcorn
Palace
1 7 0 1

95. Collect New Data
Company City Employees Alcohol Lbs of
Pretzels
Disco
Dave’s
0 29 0 ?
Millenium
Movies
0 3 0 ?
Gutter Bowl 1 56 1 ?
Chips and
Dips
1 40 0 ?

96. Collect New Data
Company City Employees Alcohol Lbs of
Pretzels
Disco
Dave’s
0 29 0 24
Millenium
Movies
0 3 0 3
Gutter Bowl 1 56 1 13
Chips and
Dips
1 40 0 4

97. What’s Important?
Variable City
City -0.123
Employees 0.3456
Alcohol 0.982

98. What are the Results?
• Often can see 2-3x increase in performance over regular targeting,
according to Exelate

99. Applying It
• Need to aggregate and plan your data
• Need competitive data
• Need to execute

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101. Questions?
Andrew Van Aken
Senior Consultant,
OgilvyOne Worldwide
José Arteaga
Creative Digital Strategist,
OgilvyRED
Michael McCarthy
Senior Consultant,
OgilvyOne Worldwide