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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?

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Questions?

Andrew Van Aken Consultant,

OgilvyOne Worldwide

Laurie Close Global Brand Partnerships,

OgilvyRED

Michael McCarthy Senior Consultant,

OgilvyOne Worldwide