Machine Learning for Marketers by Mike King at The Inbounder New York

MICHAEL KING
MACHINE LEARNING
FOR MARKETERS
@iPullRank

IPULLRANK.COM @ IPULLRANK
Agenda
Machine Learning
Doomsday
ML vs DL vs AI?
Marketing Use Cases
Models & Use Cases
Tools For Marketers
Wrapping Up
Real World Examples

Machine Learning Doomsday
What everyone is afraid of

Smart People are Scared of
Artificial Intelligence

Shockingly, He Now Wants To Sell Us Something

Really Smart People…

It’s All Olivia Pope’s dad’s fault
Either the Robots Enslave Us

Or Evolve to a Point We Can’t
Understand

Singularity is Considered a
Very Real Theory
Ray Kurzweil believes that we will achieve singularity by 2045.

…And Larry & Sergey from Google are All Set Though

But if We’re Nerding Out, Don’t Forget Isaac Asimov

Apparently Machine Learning Can Write Copy For you
There is a sub-field of artificial intelligence called Natural Language Generation that has made the concept of content spinning a lot more viable and has been used for sports recaps and financial reports.

But It Can Also F*ck It Up

Improved Google Translate from Scratch in 2 Months
A team rebuilt the broken Google Translate using Machine Learning and within 2 months it was already as good as the version that had taken years to build.

AI Is Gonna Steal Your Job?
One of the more common fears of middle America around the idea of artificial intelligence is that robots will replace humans in their jobs.

Obama Had Some
Measured Thoughts
On His Way Out

The real fear of machine
learning and artificial
intelligence should be its
ability to reflect and amplify
our biases and the lack of
diversity of the people
creating it.

For now though, it can get you a date
(h/t @goutaste)

And you can go there in a self-driving Uber Lyft

WARNING!
This is going to be some
heady stuff.

This my daughter Zora. She’ll show up
whenever y’all get bored

Machine Learning vs. Deep
Learning vs. AI
The Core Concepts

They Are Not the Same Thing

AI is Comprised of Many Disciplines
Deep Learning is a subset of Machine Learning is a subset of Artificial Intelligence.
AI many branches of which machine learning is a core branch that we can execute.

Artificial Intelligence as it is represented
in sci-fi is “general” artificial
intelligence. What we have achieved so
far is “narrow” artificial intelligence.

Types of Artificial Intelligence Explained Using
“The Lawnmower Man”
Narrow Artificial Intelligence
Machines that can do a specific
task or series of tasks
exceedingly well and very
efficiently.
General Artificial Intelligence
A machine that is as smart as a
human in that it can take in new
situations and make decisions.
Artificial Superintelligence
A machine that is potentially
orders of magnitude smarter
than a human in all categories
simultaneously

Experts Disagree on When General Intelligence Will
Happen
The primary issue keeping this from happening is computing power.

Experts Disagree
on When General
Intelligence Will
Happen
The primary issue
keeping this from
happening is
computing power.

Accelerating Moore’s Law
Google has been working on quantum computing to accelerate Moore’s Law

100mm
times faster than a classical computer by using a D-Wave
quantum computer
NewScientist.com

Ok. So, What Is Machine Learning?
“Machine learning is a type of artificial
intelligence that provides computers with
the ability to learn without being
explicitly programmed.”

It’s Really Just Using Math to Guess and Check

Supervised Learning
The machine looks for patterns that match the labeled data that you provide and classifies new data based on that.

Unsupervised
Learning
The machine identifies patterns in the data and creates clusters based on what it finds.

Reinforcement Learning
With reinforcement learning, the model is continually trained based on new data thereby improving the classifier’s ability to perform.

And Deep Learning?
“Deep Learning is a subfield of machine
learning concerned with algorithms inspired
by the structure and function of the brain
called artificial neural networks.”

That’s not what we’re talking
about today.

Machine Learning vs. Statistics
Machine Learning learns from data without
relying on rules-based programming,
statistical modeling identifies relationships in
the form of mathematical equations.

All Values vs. Linear
Representation
Machine Learning examines all
potential values based on probability
whereas statistics looks for a linear
function to describe the trend.

Machine Learning is the “Growth
Hacking” of the Statistics World
However, in some ways machine learning and statistics are so similar that many statisticians just feel as though machine learning is just a rebranding of what they do much like “growth hacking” is just a rebranding of marketing.

The Machine Learning
Process
GET & PREPARE
YOUR DATA
You identify and clean
your dataset in
preparation for solving
the machine learning
problem
CHOOSE YOUR
MODEL TRAIN YOUR
CLASSIFIER
You chose the
algorithm or model that
you believe will yield
the best results then
run it in order to train
your classifier.
SCORE AND
EVALUATE
You score the
accuracy and precision
of the classifier and
test it against other
algorithms to see what
performs best.
PREDICT OR
IDENTIFY
OUTCOMES
Once you are happy
with the results, you
use the classifier
moving forward to
make conclusions
about new data.

This is an example of how you could
predict the demand of cars for a car
rental company. It follows the same
framework.
Car Rental Example

Training Chatbots
Training chatbots is
similar to training
ML classifiers in that
you take a
knowledge base and
run it through NLP
then tune it with
regard to
conversations.

Marketing Use Cases
How can you use it?

Predictive Analytics

Marketing Campaign
Performance
Prediction

Customer Churn
Prediction

Personalization

Customer
Segmentation

Natural Language
Processing

Clustering &
Classifying Keywords
http://ipullrank.com/clustering-vs-classification-speed-keyword-research/

Follow Vicky Qian
@vickyqian24

Sentiment Analysis

Natural Language
Generation

Computer Vision
There are services that leverage machine learning and computer vision to identify objects in pictures.

Chatbots

Key Takeaway
Machine Learning, like all of digital
marketing, is a complicated form of
guess and check.

Shit, Google Doesn’t Even Know How Rankbrain Works
Yet, the world’s greatest search engine has deployed it to production.

Remember this?

Look Closer

Guess and Check

Don’t be afraid of it, just
try something.
Anything.

Real World Examples
Some things we work on

We Re-Ranked the Inc.
500

Follow Up Blog Content

Company-level Report

Retargeting Ads

Super-specific
Retargeting Ads

We Built a Simple Marketing
Automation System

Each Contact Has a
Unique URL

Integrates with Reply

LinkedIn Sales Navigator

Prospectify for finding
Emails Quickly

Salesperson Writes
Mail Merge Templates

42% Open Rate!

The Methodology is the
Machine Learning Part
We took all available domain-level link
features for the Searchmetrics losers
and winners and figured out (5-fold
cross validation, random forest and
lasso) which ones correlated best with
the results and then used that model
to re-rank the Inc. 500. (I probably
shoulda asked Marcus for more data,
but whatever).

Methodology behind
the Vector Report
We broke it into two types of machine learning questions. Classification and Logistic Regression to predict the probability of continued visibility in Organic Search.
Goal: identify SEO
winners and losers
and predict a site’s
performance in SEO
Classification
Random Forest
Gradient Boosting
Machine
Support Vector
Machine
Logistic Regression Regularization

Have You Met
@tomcritchlowbot?

The Bot wit the Solid
Delivery.

This is Twitter bot
built from Markov
Chains

Adwords Scripts
http://searchengineland.com/machine-learning-adwords-scripts-google-prediction-api-217936

Programmatic Display
Lookalike moderlign

Models
Types of Models when you should Use Them

There are Tons of
Different Models
Your best bet is to test and learn.

Seriously Tonnnnnns

The Uses of Each
Type Are Difficult to
Memorize

Models & Use Cases
Random Forest
Lead Qualification
Logistic Regression
Customer Churn
Prediction
Decision Trees
Customer Churn
Prediction

Models & Use Cases
(Cont’d)
Support Vector
Machines
Text Categorization
Apriori
Market Basket Analysis
(Amazon)
Naïve Bayes
Sentiment Analysis
Recommendation Systems
Spam Classification

K-Fold Cross
Validation
Try out a model and validate it using k-fold cross validation.

K-Fold Cross Validation is a Guess and Check
Try out a model and validate it using k-fold cross validation.

How to Choose a Machine
Learning Model
https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-algorithm-choice

Ok, that was a lot.
How about a song?

Tools for Marketers
Doing ML the easy way

IPULLRANK.COM @ IPULLRANKMost machine learning is done in R or Python, but those are programming languages.

yHat Science Ops
yHat allows you to deploy machine learning models as REST APIs that can then be integrated with your site like any other API.

Beeswax Bidder-as-a-Service
Beeswax allows you to set up custom models to run your Display RTB campaigns.

Those are tools that allow
marketers to take control
with a data scientist.

mTurk - Labeling Data for Supervised Learning
Exploratory Data Analysis helps identifying general patterns in the data and serve as initial explorations of correlations.

API.AI Generating Chatbots
http://api.ai

NanoRep
http://www.nanorep.com

MonkeyLearn &
Orange
We will primarily talk about MonkeyLearn and Orange as two tools marketers can use to do machine learning right now.

These Examples Use
the Iris Petals Dataset
https://archive.ics.uci.edu/ml/datasets/Iris

Exploratory Data Analysis
Exploratory Data Analysis helps identifying general patterns in the data and serve as initial explorations of correlations.

Exploratory Data
Analysis: Scatter Plot
Two-dimensional scatter plot shows class density.

Exploratory Data
Analysis:
Distributions
Compare the distributions of different type of iris.

Classification Tree
Observe the pattern across nodes to discover important variables.

Predictive Text
Classification

Import Text Mining Add-
on
Install the free text mining add-on in order to use Orange’s text mining capabilities.

Load and Preprocess
Dataset
Preprocess text to find meaningful words only.

Word Cloud
Using the word cloud, we can determine the frequency of keywords in the list.

Hierarchical
Clustering
We can use this to determine similarity in the corpus or dataset.

Hierarchical
Clustering
Once we understand the hierarchy, we can dig into the documents in the viewer to see how the model has organized them.

Classification

Test & Score is your Guess and Check

SVM: Linear vs. Non-
linear
Linear SVM often outperforms non-linear in text classification.

Confusion Matrix:
Non-linear SVM
Send misclassified samples to corpus viewer.

Nearest Neighbors

Logistic Regression:
Ridge vs. Lasso
Logistic regression with l2 penalty achieve higher accuracy.

Confusion Matrix:
Lasso
Send misclassified samples to corpus viewer.

Naive Bayes

Compare Models – Again, Your Guess & Check
Linear SVM and logistic regression outperform the other two models.

Prediction
Predict with winning classifiers.

Prediction
SVM and logistic regression all hit 100% accuracy.

IPULLRANK.COM @ IPULLRANKMonkey Learn is a text mining cloud platform.

MonkeyLearn Now Works with Google Sheets
Monkey Learn is a text mining cloud platform.

Monkey Learn: Train Category Tree

Monkey Learn: Tree Parameters

Monkey Learn: Classify with Category Tree

Codementor

Experfy

Kaggle

Wrapping Up
Who am I and where am I from?

I’M #ZORASDAD
First and foremost.

MY NAME IS MIKE
KING
Razorfish, Publicis Modem alum
Full Stack Developer
Full Stack Marketer
Moz Associate

I Run a Better
Marketing Agency
Called iPullRank

We Do These Things
Machine
Learning
SEOContent
Strategy
Paid Media Measurement
&
Optimization
Marketing
Automatio
n

IPULLRANK
http://ipullrank.com
THANK YOU
Michael King
Managing Director
mike@ipullrank.com

Machine Learning for Marketers by Mike King at The Inbounder New York

Machine Learning for Marketers by Mike King at The Inbounder New York

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