This document provides an introduction and overview of artificial intelligence for marketing. It discusses how machine learning works using supervised, unsupervised, and reinforcement learning methods. It also outlines tips for bringing AI into a marketing organization, including clearly defining goals, having clean and consistent data, and starting with repetitive tasks like ranking, sorting, and anomaly detection. The document emphasizes that business stakeholders should use their industry and market knowledge to set goals, identify problems, and make decisions.

1. Intro to Artificial Intelligence
for Marketing
jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
Jim Sterne

2. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
Jim Sterne Intro to Artificial Intelligence
for Marketing

3. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
IS NOT
for data scientists
How to be a data scientist
Cool algorithms
Statistical tips and tricks
How to build a bot
Latest start-ups
Quantum computing
Intro to AI & Machine Learning
How some methods differ
How to talk to data scientists
Where AI/ML is used in marketing
How to bring AI/ML into your org
How to keep your job
IS
for marketers

4. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
Why Artificial Intelligence Now?
50 years of study
Cheap storage of Big Data
Massively parallel processing (GPUs)
Open Source

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What is it?
How does it work?
What is it good at?
How is it useful?
Intro to Artificial Intelligence
for Marketing

6. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
As Seen on TV
AI: Anything computers can’t
SciFi: Anything AI can’t
“General AI” – thinks and acts human
Sentience
“Narrow AI” – task specific
Functional

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Artificial Intelligence
Natural Language Processing
Speech recognition
Speech to text
Text to meaning
Sentiment analysis
"Wreck a nice beach"

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Artificial Intelligence
Computer Vision

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Facial Recognition

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Self-Driving Cars!

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Artificial Intelligence
Robots

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Artificial Intelligence
Natural Language Understanding
Conversation Bots
Computer Vision
Self-Driving Cars & Robots
Machine Learning

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Software Grows Up
Specific Logic Mathematical Model
Do this, then this, then this
If this happens, do that
If confused, report error
Statistical Model Machine Learning
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(a.getAttribute("type")||a.tagName).toUpperCase()in ca},ba={CHECKBOX:!0,OPTION:!0,RADIO:!0},ca={COLOR:!0,
DATE:!0,DATETIME:!0,"DATETIME-LOCAL":!0,EMAIL:!0,MONTH:!0,NUMBER:!0,PASSWORD:!0,RANGE:!0,SEARCH:!0,TEL:!0,
TEXT:!0,TEXTAREA:!0,TIME:!0,URL:!0,WEEK:!0},da={A:!0,AREA:!0,BUTTON:!0,DIALOG:!0,IMG:!0,INPUT:!0,LINK:!0,MENU:
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16. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
Software Grows Up
Specific Logic Mathematical Model
Do this, then this, then this
If this happens, do that
If confused, report error
Statistical Model Machine Learning

17. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
Software Grows Up
Specific Logic Mathematical Model
Do this, then this, then this Describe numerical relationships
If this happens, do that Calculate alternatives
If confused, report error Human compares results & iterates
Statistical Model Machine Learning

18. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
Software Grows Up
Specific Logic Mathematical Model
Do this, then this, then this Describe numerical relationships
If this happens, do that Calculate alternatives
If confused, report error Human compares results & iterates
Statistical Model Machine Learning
Calculate probabilities
Project likelihoods
Human compares & iterates

19. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
Software Grows Up
Specific Logic Mathematical Model
Do this, then this, then this Describe numerical relationships
If this happens, do that Calculate alternatives
If confused, report error Human compares results & iterates
Statistical Model Machine Learning
Calculate probabilities Uses examples to figure it out
Project likelihoods and changes it's mind
Human compares & iterates

20. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne

21. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
What is it?
How does it work?
What is it good at?
How is it useful?
Intro to Artificial Intelligence
for Marketing

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Machine Learning
Supervised
Unsupervised
Reinforcement

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Machine Learning
Supervised
You know the right answer
needs many examples
of labeled data
Dog: Yes Cat: No Tag a friend?

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Machine Learning
Supervised
cat cat
cat cat
You know the right answer
needs many examples
of labeled data

25. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
Machine Learning
Supervised
You know the right answer
needs many examples
of labeled data

26. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
Machine Learning
Supervised
You know the right answer
needs many examples
of labeled data

27. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
Machine Learning
Supervised
You know the right answer
needs many examples
of labeled data

28. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
Machine Learning
Supervised
You know the right answer
needs many examples
of labeled data

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Machine Learning
Unsupervised
Online buying goes up when the weather is bad
Ice cream causes drowning
Shoes cause headaches
Nicolas Cage is a monster
You don't know the right answer
Machine finds patterns in unlabeled data
may or may not be useful (correlation/causation)

30. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne

31. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
Machine Learning
Supervised
You know the right answer
needs many examples
of labeled data
Unsupervised
You don't know the right answer
finds patterns in unlabeled data
may or may not be useful (correlation/causation)

32. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
Machine Learning
Reinforcement
There is no absolute right answer
some answers are better
"rewards" based on results
optimizes over time
Photo by Marek Szturc on Unsplash

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3 Needs for 3 Deeds
of Machine Learning
Data Detect
Goal Decide
Control Revise
Needs Deeds

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What is it?
How does it work?
What's it good at?
How is it useful?
Intro to Artificial Intelligence
for Marketing

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Learning Machine Learning
High Dimensionality
High Cardinality
What's it good at?

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Dimensionality = Attributes per Object
Cardinality = Options per Attribute

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Dimensionality = Attributes per Object
Cardinality = Options per Attribute
Person
Objects
Attributes
billions of permutations

38. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
What is it?
How does it work?
What's it good at?
How is it useful?
Intro to Artificial Intelligence
for Marketing

39. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
Robots!

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In-Store Robots

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Augmented Reality

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Augmented Reality

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Facial Recognition

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Facial Recognition

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Artificial Intelligence
Natural Language
Speech to text
Conversation Bots
Text to meaning

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50. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
Automating Response

51. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
Artificial Intelligence
Natural Language
Speech to text
This means that
Repeated correction
Taught over time
Conversation Bots
Text to meaning
Concept & emotion imitation
Repeated correction
Taught over time
Lenovo Unified
Customer Intelligence

52. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne

53. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
Artificial Intelligence
Natural Language
Speech to text
This means that
Repeated correction
Taught over time
Conversation Bots
Text to meaning
Concept & emotion imitation
Repeated correction
Taught over time
Lenovo Unified
Customer Intelligence

54. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
Artificial Intelligence
Natural Language
Speech to text
This means that
Repeated correction
Taught over time
Conversation Bots
Text to meaning
Concept & emotion imitation
Repeated correction
Taught over time

55. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne

56. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
Bringing AI Into Your Organization
Look what followed me home!
Can we keep him?

57. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
AI Onboarding Tips

58. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
AI Onboarding Tips
Clearly identified goals
Raise Revenue
Lower Costs
Improve Customer Satisfaction
Introduce a New Competency
Raise Awareness
Improve Attitude
Influence Influence
Inspire Interaction
Generate Sales
Drive Endorsements

59. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
AI Onboarding Tips
Clearly identified goals

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AI Onboarding Tips
Clearly identified goals
Optimize Customer Retention

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Machine Learning Bump
Humans Machines

62. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
AI Onboarding Tips
Clearly identified goals
Know Your Data
Would I advise my uncle?
Would I stake my reputation?
Would I risk my own money?
Would I bet my job?

63. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
AI Onboarding Tips
Clearly identified goals
Know Your Data Clean, Consistent, Reliable

64. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
AI Onboarding Tips
Clearly identified goals
Know Your Data Clean, Consistent, Reliable
Server Outage
Missing Tag
Broken Tag
Corrupted Data
Broken ETL
Enrichment Error
Integration Error
Ad Blocker
Etc., etc....

65. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
AI Onboarding Tips
Clearly identified goals
Know Your Data Clean, Consistent, Reliable
Web Analytics, Customer Service, Customer Relationship Management, Sales Force Automation,
Campaign Analysis, Social Media, Email Marketing, Mobile, Wearables, Surveys, Facebook,
Aggregators, App Data, Behavioral Data, Accounting, Video Views, API's, Enrichment, etc., etc....

66. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
AI Onboarding Tips
Clearly identified goals
Know Your Data
Web Analytics, Customer Service, Customer Relationship Management, Sales Force Automation,
Campaign Analysis, Social Media, Email Marketing, Mobile, Wearables, Surveys, Facebook,
Aggregators, App Data, Behavioral Data, Accounting, Video Views, API's, Enrichment, etc., etc....
Clean, Consistent, Reliable

67. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
AI Onboarding Tips
Clearly identified goals
Know Your Data Clean, Consistent, Reliable

68. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
AI Onboarding Tips
Clearly identified goals
Know Your Data
Data Lake
Clean, Consistent, Reliable

69. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
AI Onboarding Tips
Clearly identified goals
Know Your Data
Data Lake
Clean, Consistent, Reliable

70. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
AI Onboarding Tips
Clearly identified goals
Know Your Data

71. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
AI Onboarding Tips
Clearly identified goals
Know Your Data
Start with repetitive, high volume, taxing tasks
Ranking
Sorting
Finding patterns
Finding look-alikes
Anomaly detection

72. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
AI Onboarding Tips
Clearly identified goals
Know Your Data

73. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
Correlations people with this attribute have that attribute
Segmentation these people form a group
Clustering there are X number of groups
Anomalies these people are unique
Are results interesting? Useful? Worthy of further study?
What Can ML Do Better?

74. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
AI Onboarding Tips
Clearly identified goals
Know Your Data
Start with repetitive, high volume, taxing tasks
Buy vs. Build

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Business
Stakeholder

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Business
Stakeholder
domain
knowledge
usesIndustry
Market
Company
Products
Customers
Competition
Price of tea in China
Which way the wind is blowing
The airspeed velocity of an unladen African swallow

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BUSINESS
GOALS
Business
Stakeholder
domain
knowledge
uses
to set

78. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
uses
to set
BUSINESS
GOALS
domain
knowledge
Business
Stakeholder
BUSINESS
PROBLEMS
to identify

79. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
uses
to set
BUSINESS
GOALS
domain
knowledge
Business
Stakeholder
BUSINESS
DECISIONS
to make
to identify
BUSINESS
PROBLEMS

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uses
to set
BUSINESS
GOALS
domain
knowledge
Business
Stakeholder
to identify
BUSINESS
PROBLEMS
BUSINESS
DECISIONS
to make
informed by
Analyst
insight

81. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
uses
to set
BUSINESS
GOALS
domain
knowledge
Business
Stakeholder
to identify
BUSINESS
PROBLEMS
BUSINESS
DECISIONS
to make
informed by
Analyst
insight
Analyst

82. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
uses
to set
BUSINESS
GOALS
domain
knowledge
Business
Stakeholder
to identify
BUSINESS
PROBLEMS
BUSINESS
DECISIONS
to make
informed by
Analyst
insight
Analyst
appreciates and considers

83. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
to craft to test
uses
to set
BUSINESS
GOALS
domain
knowledge
Business
Stakeholder
to identify
BUSINESS
PROBLEMS
BUSINESS
DECISIONS
to make
informed by
Analyst
insight
appreciates and considers
Analyst
HYPOTHESES

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85. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
Analyst
Business
Stakeholder

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determines
accounts for
most revealing data
cognitive bias
Analyst
innate bias
to
ensure
SOUND
ADVICE
Business
Stakeholder

87. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
determines
accounts for
most revealing data
cognitive bias
Analyst
innate bias
to
ensure
uses
to convey
SOUND
ADVICE
visualization
and storytelling
Business
Stakeholder

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builds
chooses
Analyst
depends on
Data
Scientist
METHODS
MODELS
Data
Engineer
depends on
collects
cleans
integrates
monitors
manages
pipelines
operationalize
s DATA
QUALITY
to improve
give
feedback
Data
Business
StakeholderBusiness
Stakeholder

89. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
builds
chooses
Analyst
depends on
Data
Scientist
METHODS
MODELS
Data
Engineer
depends on
collects
cleans
integrates
monitors
manages
pipelines
operationalize
s DATA
QUALITY
to improve
give
feedback
Data
to ensure quality of
Business
Stakeholder

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Data
Engineer
Data
Scientist
Analyst
Business
Stakeholder

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Data
Engineer
Data
Scientist
Analyst
Business
Stakeholder
Licorne

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give
feedback
Data
Engineer
Data
Scientist
Analyst
Outsource
if you can
Business
Stakeholder

93. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
AI Onboarding Tips
Clearly identified goals
Know Your Data
Start with repetitive, high volume, taxing tasks
Buy vs. Build
Buy!

94. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
AI Onboarding Tips
Clearly identified goals
Know Your Data
Start with repetitive, high volume, taxing tasks
Buy vs. Build
Determine which data sets are useful

95. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
AI Onboarding Tips
Clearly identified goals
Know Your Data
Start with repetitive, high volume, taxing tasks
Buy vs. Build
Determine which data sets are useful
Too much = noise
Too little = overfitting
Just right = insight

96. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
AI Onboarding Tips
Clearly identified goals
Know Your Data
Start with repetitive, high volume, taxing tasks
Buy vs. Build
Determine which data sets are useful
Become proficient at the Smell Test

97. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
The Smell Test

98. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
AI Onboarding Tips
Clearly identified goals
Know Your Data
Start with repetitive, high volume, taxing tasks
Buy vs. Build
Determine which data sets are useful
Become proficient at the Smell Test
Be Human

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Amount of data
Speed of correlation
Repetition minus ennui
Accuracy
Cost
Zero attitude
Machine Advantages Your Advantages
Reason
Common sense
Emotion
Empathy
Experience
Integrated cognition
Be Human

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Press Your Advantage
Recognizing the problem
Onboarding new ideas
Relating non-related data
Relating non-related experience
Collaboration & Diversity
Empathy
Imagination

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Man and Machine

102. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
AI Onboarding Tips
Clearly identified goals
Know Your Data
Start with repetitive, high volume, taxing tasks
Buy vs. Build
Determine which data sets are useful
Become proficient at the Smell Test
Be Human

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Be Prepared:
Marketing is About to Get Weird

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Be Prepared:
Marketing is About to Get Weird

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Harness the Power for Yourself
Take advantage of the tools
Build systems to talk to customers' systems
Build your brand

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What is your next step??

108. jsterne@targeting.com – Marketing Evolution Experience.com – @jimsternejsterne@targeting.com – Marketing Evolution Experience.com – @jimsterne
Jim Sterne Intro to Artificial Intelligence
for Marketing