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FOR
Powered by
MACHINE LEARNING
Hello!
Andrew Van Aken
Consultant,
OgilvyOne Worldwide
Laurie Close
Global Brand Partnerships,
OgilvyRED
Michael McCarthy
...
What’s the weather like in your city?
Tell us where you’re dialing in from!
Want this deck?
It will be available for download shortly after
the webinar on: slideshare.net/socialogilvy
Ogilvy staff: ...
This Talk
• We will demystify machine learning (ML) and artificial intelligence (AI)
• Why now for ML and AI?
• Ogilvy case...
What is Machine Learning
Machine learning gives “computers the ability to learn without being
explicitly programmed.”
-Art...
Any Type of Data
Machine Learning Concept
• Machine learning takes an input
• to an output: David Ogilvy
How does it do it?
x1 x2 x3 y
23 146 1 91
x1 x2 x3 y
23 146 68 163
Another David Ogilvy
Panda or Gibbon?
Soccer/Football Example
Visitor Goals
Score
Visitor Goals
Allowed
Home Goals
Scored
Home Goals
Allowed
Outcome
2 3 1 4 0
3...
Tree Based Approach
Tree Based Approach
All Models are Wrong
• After the tree has been built, a calculation is done to show how
accurate your model is
• The algor...
Adding Complexity
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...
What is Artificial Intelligence
“Artificial intelligence is whatever hasn't been done yet”
-Larry Tesler, 1970
Is This AI?
• A program that can beat anyone in chess?
• A software service that can tell you the answer to almost any
que...
Is This AI?
Is This AI?
Is This AI?
Is This AI?
Is This AI?
• While not a universal definition, at Ogilvy we consider a main differentiation
of AI versus Machine Learning ...
What is an Example of AI?
• Example 1: Autonomous Media Buying
What is an Example of AI?
• Example 2: AI Generated Content
What is AI
WHY NOW?
Why Now
• Big Data
• Compute
Google Trends - Machine Learning
Corporations
Why Now?
“90% of the data in the world has been created in the past two years”
-IBM, 2017
Big Data
Data = Accuracy
Accuracy
Amount	of	Data
Data	vs	Accuracy
Enormous Data
But CPU’s are Slowing
Enter GPUs
Enter GPUs
But at a Cost
• A single GPU can cost up to $10,000 and uses tremendous amounts
of power
• Facebook recently used 256 GPUs...
Where Next?
• Do we just keep adding data and power?
• Do we need new methods?
What do we Think!
• It’s complicated…
CASE STUDIES
Text Mining -> Chatbot
Text mining analysis to provide insights into best use of Chatbot
functionality
The Challenge - Utility Client
Social media customer
service is a significant cost
expenditure and usage
continues to rise...
The Ask
Process Social Media Data
Analyze Recommend
Utilize Machine Learning to
Extract Key Topics from Text Data
Provide ...
The Data
CONVERSATIONS BY TYPE CONVERSATIONS BY SENTIMENT
AVERAGE CONVERSATION LENGTH AVERAGE WORDS PER CONVERSATION
4.5
m...
The Solution
Topic Modeling (Non-Negative Matrix Factorization)
Programming
Language
Data Science
Platform
Machine Learnin...
Identifying Viral Tweets
Text mining analysis revealed 28% of conservation activity could be
directed away from customer c...
Extracting Key Phrases by Sentiment
Pulling out the top phrases by positive and negative
customer service conversations ga...
Summarizing Customer Service Topics
customer service,
poor customer,
service today,
excellent
customer,
shocking
customer,...
Evaluating Chatbot Usage
customer service… email address… power cut…
Sentiment: 70% negative
Complexity: ↑ average
Recomme...
Client Recommendations
1. Brand and Viral comments could be diverted to a Chatbot with machine learning
algorithm
2. Negat...
Customer Lifetime Value
Scalable machine learning applied to millions of members
LTV Challenge
• Build reproducible, production level lifetime value model which scales to
millions of users
• Writes to da...
What did we Predict?
• Revenue - a regression problem
• Cost of goods sold - logistic problem
• Coupons redeemed - Bayesia...
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
8...
Data Pipeline
Data-
warehouse
Stored Procedure
Trains Model
Trains Model
Trains Model
Stored Procedure
Predicts
Predicts
P...
Going Forward
• Develop a model to find what drives LTV
• Will sending more emails affect LTV
• What’s the optimal number o...
Want to Stay Present?
• We write weekly on machine learning, artificial intelligence, cloud
computing and other technology
...
Questions?
Andrew Van Aken
Consultant,
OgilvyOne Worldwide
Laurie Close
Global Brand Partnerships,
OgilvyRED
Michael McCar...
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Whats Next for Machine Learning

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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.

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Whats Next for Machine Learning

  1. 1. FOR Powered by MACHINE LEARNING
  2. 2. Hello! Andrew Van Aken Consultant, OgilvyOne Worldwide Laurie Close Global Brand Partnerships, OgilvyRED Michael McCarthy Senior Consultant, OgilvyOne Worldwide
  3. 3. What’s the weather like in your city? Tell us where you’re dialing in from!
  4. 4. Want this deck? It will be available for download shortly after the webinar on: slideshare.net/socialogilvy Ogilvy staff: It’s also on The Market! themarket.ogilvy.com Are you on the go? You can join our webinars on mobile, too! Download the GoToWebinar app from the App Store or Google Play
  5. 5. This Talk • We will demystify machine learning (ML) and artificial intelligence (AI) • Why now for ML and AI? • Ogilvy case studies
  6. 6. What is Machine Learning Machine learning gives “computers the ability to learn without being explicitly programmed.” -Arthur Samuel, 1959
  7. 7. Any Type of Data
  8. 8. Machine Learning Concept • Machine learning takes an input • to an output: David Ogilvy
  9. 9. How does it do it? x1 x2 x3 y 23 146 1 91 x1 x2 x3 y 23 146 68 163
  10. 10. Another David Ogilvy
  11. 11. Panda or Gibbon?
  12. 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. 13. Tree Based Approach
  14. 14. Tree Based Approach
  15. 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. 16. Adding Complexity
  17. 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. 18. What is Artificial Intelligence “Artificial intelligence is whatever hasn't been done yet” -Larry Tesler, 1970
  19. 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. 20. Is This AI?
  21. 21. Is This AI?
  22. 22. Is This AI?
  23. 23. Is This AI?
  24. 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. 25. What is an Example of AI? • Example 1: Autonomous Media Buying
  26. 26. What is an Example of AI? • Example 2: AI Generated Content
  27. 27. What is AI
  28. 28. WHY NOW?
  29. 29. Why Now • Big Data • Compute
  30. 30. Google Trends - Machine Learning
  31. 31. Corporations
  32. 32. Why Now? “90% of the data in the world has been created in the past two years” -IBM, 2017
  33. 33. Big Data
  34. 34. Data = Accuracy Accuracy Amount of Data Data vs Accuracy
  35. 35. Enormous Data
  36. 36. But CPU’s are Slowing
  37. 37. Enter GPUs
  38. 38. Enter GPUs
  39. 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. 40. Where Next? • Do we just keep adding data and power? • Do we need new methods?
  41. 41. What do we Think! • It’s complicated…
  42. 42. CASE STUDIES
  43. 43. Text Mining -> Chatbot Text mining analysis to provide insights into best use of Chatbot functionality
  44. 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. 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. 46. The Data CONVERSATIONS BY TYPE CONVERSATIONS BY SENTIMENT AVERAGE CONVERSATION LENGTH AVERAGE WORDS PER CONVERSATION 4.5 messages ~50
  47. 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. 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. 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. 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. 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. 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. 53. Customer Lifetime Value Scalable machine learning applied to millions of members
  54. 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. 55. What did we Predict? • Revenue - a regression problem • Cost of goods sold - logistic problem • Coupons redeemed - Bayesian LTV = Revenue – COGS - Coupons
  56. 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. 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. 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?
  59. 59. Want to Stay Present? • We write weekly on machine learning, artificial intelligence, cloud computing and other technology • Cloudy with a Chance of AI - Subscribe today!
  60. 60. Questions? Andrew Van Aken Consultant, OgilvyOne Worldwide Laurie Close Global Brand Partnerships, OgilvyRED Michael McCarthy Senior Consultant, OgilvyOne Worldwide

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