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MACHINE LEARNING
Hello!
Andrew Van Aken Consultant,
OgilvyOne Worldwide
Laurie Close Global Brand Partnerships,
OgilvyRED
Michael McCarthy Senior Consultant,
OgilvyOne Worldwide
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This Talk
• We will demystify machine learning (ML) and artificial intelligence (AI)
• Why now for ML and AI?
• Ogilvy case studies
What is Machine Learning
Machine learning gives “computers the ability to learn without being explicitly programmed.”
-Arthur Samuel, 1959
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 3 1 2 1
5 6 2 1 1
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 algorithm will try its best to minimize the error
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 1 1 ?
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 question?
• A digital assistant?
• C3PO?
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 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.
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
AmountofData
DatavsAccuracy
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 to train 40,000 images a second
• Can rent on the cloud for cheaper
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
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
The Ask
Process Social Media Data
Analyze Recommend
Utilize Machine Learning to Extract Key Topics from Text Data
Provide Recommendations on Deploying a Chatbot
The DataCONVERSATIONS BY TYPE CONVERSATIONS BY SENTIMENT
AVERAGE CONVERSATION LENGTH AVERAGE WORDS PER CONVERSATION
4.5 messages
~50
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
12..3
Identifying Viral TweetsText 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.
# # # # #
Extracting Key Phrases by SentimentPulling 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.
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.
Evaluating Chatbot Usage
customer service… email address… power cut…
Sentiment: 70% negativeComplexity: ↑ averageRecommendation: divert away from Chatbot
Sentiment: 60% negativeComplexity: ↑ averageRecommendation: divert away from Chatbot
Sentiment: 66% positive Complexity: ↓ averageRecommendation: potential to utilize Chatbot
Customer Service Seekers Contact Us Help Seekers
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
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 database and allows others to use
• Refreshes every month
What did we Predict?
• Revenue - a regression problem
• Cost of goods sold - logistic problem
• Coupons redeemed - Bayesian
LTV = Revenue – COGS - Coupons
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 ($)
Data Pipeline
Data-warehouse
Stored ProcedureTrains Model
Trains Model
Trains Model
Stored ProcedurePredicts
Predicts
PRedicts
Writes Error Metrics
Data-warehouse
Writes Scores
User UserUser*Process takes less than an hour
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?
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!
Questions?
Andrew Van Aken Consultant,
OgilvyOne Worldwide
Laurie Close Global Brand Partnerships,
OgilvyRED
Michael McCarthy Senior Consultant,
OgilvyOne Worldwide