Do you understand the differences between pattern recognition, artificial intelligence and machine learning? And most important, what they separately bring to the table? In this week’s webinar we will tackle the terminology and discuss its recent explosion of popularity, and also look at how the Ogilvy analytics team has applied machine learning methods to effectively answer client challenges and drive value.

1. FOR
Powered by
MACHINE LEARNING

2. Hello!
Andrew Van Aken
Consultant,
OgilvyOne Worldwide
Laurie Close
Global Brand Partnerships,
OgilvyRED
Michael McCarthy
Senior Consultant,
OgilvyOne Worldwide

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5. This Talk
• We will demystify machine learning (ML) and artificial intelligence (AI)
• Why now for ML and AI?
• Ogilvy case studies

6. What is Machine Learning
Machine learning gives “computers the ability to learn without being
explicitly programmed.”
-Arthur Samuel, 1959

7. Any Type of Data

8. Machine Learning Concept
• Machine learning takes an input
• to an output: David Ogilvy

9. How does it do it?
x1 x2 x3 y
23 146 1 91
x1 x2 x3 y
23 146 68 163

10. Another David Ogilvy

11. Panda or Gibbon?

12. Soccer/Football Example
Visitor Goals
Score
Visitor Goals
Allowed
Home Goals
Scored
Home Goals
Allowed
Outcome
2 3 1 4 0
3 3 1 2 1
5 6 2 1 1

13. Tree Based Approach

14. Tree Based Approach

15. All Models are Wrong
• After the tree has been built, a calculation is done to show how
accurate your model is
• The algorithm will try its best to minimize the error

16. Adding Complexity

17. New Example
Visitor Goals
Score
Visitor Goals
Allowed
Home Goals
Scored
Home Goals
Allowed
Outcome
1 2 4 2 ?
4 3 1 4 ?
3 4 1 1 ?

18. What is Artificial Intelligence
“Artificial intelligence is whatever hasn't been done yet”
-Larry Tesler, 1970

19. Is This AI?
• A program that can beat anyone in chess?
• A software service that can tell you the answer to almost any
question?
• A digital assistant?
• C3PO?

20. Is This AI?

21. Is This AI?

22. Is This AI?

23. Is This AI?

24. Is This AI?
• While not a universal definition, at Ogilvy we consider a main differentiation
of AI versus Machine Learning to be the ability to “self-learn” or “self-
update”
• This is in terms of analytics techniques, while a different criteria might be
applied to interactive marketing tools like ChatBots, etc.

25. What is an Example of AI?
• Example 1: Autonomous Media Buying

26. What is an Example of AI?
• Example 2: AI Generated Content

27. What is AI

28. WHY NOW?

29. Why Now
• Big Data
• Compute

30. Google Trends - Machine Learning

31. Corporations

32. Why Now?
“90% of the data in the world has been created in the past two years”
-IBM, 2017

33. Big Data

34. Data = Accuracy
Accuracy
Amount of Data
Data vs Accuracy

35. Enormous Data

36. But CPU’s are Slowing

37. Enter GPUs

38. Enter GPUs

39. But at a Cost
• A single GPU can cost up to $10,000 and uses tremendous amounts
of power
• Facebook recently used 256 GPUs to train 40,000 images a second
• Can rent on the cloud for cheaper

40. Where Next?
• Do we just keep adding data and power?
• Do we need new methods?

41. What do we Think!
• It’s complicated…

42. CASE STUDIES

43. Text Mining -> Chatbot
Text mining analysis to provide insights into best use of Chatbot
functionality

44. The Challenge - Utility Client
Social media customer
service is a significant cost
expenditure and usage
continues to rise
Competitors and businesses
are implementing Chatbots,
which are crucial to scaling
customer service and making
brand engagement more
interactive
Existing data around
customer service
conversations was
insufficient to examine
cost-effectiveness and
feasibility of a Chatbot
Business Case Landscape Existing Data

45. The Ask
Process Social Media Data
Analyze Recommend
Utilize Machine Learning to
Extract Key Topics from Text Data
Provide Recommendations
on Deploying a Chatbot

46. The Data
CONVERSATIONS BY TYPE CONVERSATIONS BY SENTIMENT
AVERAGE CONVERSATION LENGTH AVERAGE WORDS PER CONVERSATION
4.5
messages
~50

47. The Solution
Topic Modeling (Non-Negative Matrix Factorization)
Programming
Language
Data Science
Platform
Machine Learning
Package
In-line Coding and
Visualizations
Data Science Toolkit
Matrix
Representation
d1 d2 d3
bi1 1 0 1
bi2 0 2 0
bi3 0 1 4
Text
Conversations
-----
---
------
Matrix Factorisation to
Derive Topic Vectors
-----
---
------
Summarize Key Topics
1
2
..
3

48. Identifying Viral Tweets
Text mining analysis revealed 28% of conservation activity could be
directed away from customer care, with 6% related to viral or
marketing activity.
Revealed an opportunity for a heuristic or machine
learning model to flag these tweets algorithmically.
# # # # #

49. Extracting Key Phrases by Sentiment
Pulling out the top phrases by positive and negative
customer service conversations gave insight into potential
flags for a Chatbot to either continue chatting or divert a
customer to a representative.

50. Summarizing Customer Service Topics
customer service,
poor customer,
service today,
excellent
customer,
shocking
customer, service
advisor, worst
customer
Customer Service Seekers
email address,
change email, old
email, send
email, address
received, details
follows, got right,
technical error
Contact Us
power cut, post
code, red
triangle, pls help,
Saturday night,
fuse box, know
long, tell long,
gets sorted,
getting address
Help Seekers
A total of 9 topics were generated from the data through unsupervised
topic modeling. Three key topics (below) show a diversity of customer
service conversations not previously categorized by agents.

51. Evaluating Chatbot Usage
customer service… email address… power cut…
Sentiment: 70% negative
Complexity: ↑ average
Recommendation: divert
away from Chatbot
Sentiment: 60% negative
Complexity: ↑ average
Recommendation: divert
away from Chatbot
Sentiment: 66% positive
Complexity: ↓ average
Recommendation: potential to
utilize Chatbot
Customer Service Seekers Contact Us Help Seekers

52. Client Recommendations
1. Brand and Viral comments could be diverted to a Chatbot with machine learning
algorithm
2. Negative and positive sentiment are distinguishable by key phrases, allowing for
direction to Chatbot or human where necessary
3. After applying non-negative matrix factorization, we can determine which
conversation types are suitable for a Chatbot based on conversation complexity and
sentiment

53. Customer Lifetime Value
Scalable machine learning applied to millions of members

54. LTV Challenge
• Build reproducible, production level lifetime value model which scales to
millions of users
• Writes to database and allows others to use
• Refreshes every month

55. What did we Predict?
• Revenue - a regression problem
• Cost of goods sold - logistic problem
• Coupons redeemed - Bayesian
LTV = Revenue – COGS - Coupons

56. Prediction Error
-$40
$160
$360
$560
$760
$960
$1,160
$1,360
1
5
9
13
17
21
25
29
33
37
41
45
49
53
57
61
65
69
73
77
81
85
89
93
97
Predicted CLV($) Actual Net Revenue($)

57. Data Pipeline
Data-
warehouse
Stored Procedure
Trains Model
Trains Model
Trains Model
Stored Procedure
Predicts
Predicts
PRedicts
Writes Error Metrics
Data-
warehouse
Writes Scores
User User
User*Process takes less than an hour

58. Going Forward
• Develop a model to find what drives LTV
• Will sending more emails affect LTV
• What’s the optimal number of coupons to serve?
• Segmenting users around LTV
• What do we do with the most valuable
• Do we do anything at all?
• How do we engage users to spend more?

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60. Questions?
Andrew Van Aken
Consultant,
OgilvyOne Worldwide
Laurie Close
Global Brand Partnerships,
OgilvyRED
Michael McCarthy
Senior Consultant,
OgilvyOne Worldwide