Dan Kuster held a workshop at General Assembly Boston on how machine learning is changing -- and improving -- the way digital marketers do their jobs. Overview: "Machine learning allows a marketer to target people based on an actual understanding of their interests, habits, and personality, rather than typical demographic data. To get more concrete here, machine learning lets you say: I want to target people that have posted a picture of a guitar in the last three months, or: I want to target people with the INTP personality type that posted something angry about Bernie Sanders recently. It also allows marketers to look strategically at the content they use to engage their audience and reflect on what works and what doesn't work in a scientific way. If you make 30 posts with very different engagement rates, you can use your own intuition, but then also scientifically vet the wording of your message to get a sense ahead of time about how engaging it may be."

1. Digital Marketing + ML
Daniel Kuster, Ph.D.
@indicodata @djkust

2. 1. Machine learning
2. Digital marketing
3. How it works: using ML to automate digital marketing
4. Examples: real-world use cases

3. what is
machine learning?

4. machine learning in a nutshell
text, images,
sequences, relationships
0.001, 0.0002, 0.077 -> logistic regression
DATA
FEATURES
REP
MODEL
PREDICTIONS
42, 0, 1826, 19736, … -> frequencies
vocabulary -> tokens
{ positive: 0.85,
negative: 0.15 }

5. what is
digital marketing?

6. digital marketing = marketing + data

7. many channels under the digital marketing umbrella
Image credit: eperales via flickr.com; modified by cropping
Video(YouTube)
Social m
edia
(Twitter, Fb, Insta)
Search ads (Google)
Email(Mailchimp)
Content(news,blogs)
e-com
m
erce
(Am
azon)Mobile apps (iOS, Android)
SMS/text messaging (telecoms)
Chat/instant messaging (Slack)

8. OK, what ISN’T
digital marketing?
Broadcast media
(TV, radio)
Most media-on-disc
Word of mouth
Books
Printed newspaper
Signs & Billboards

9. There are many channels + kinds of data

10. ?
what year did $ spent for display ads
surpass search ads?

11. 2016

12. 1. Machine learning
2. Digital marketing
3. How it works: using ML to automate digital marketing
4. Examples: real-world use cases

13. Since we have data, let’s automate this…

14. Why automation?
Scale / throughput
Latency
Cost
Complexity

15. Two kinds of data here
1. Demographics about you. Users often don’t know what data exist on server
(definitely not “permission marketing”). Need scalable ways to store and
access data about each user:
• Metadata
• Friends, Likes, browsing history, stuff in cart, …
• Social graph
2. What you’re saying. Users post their own content. We know what we post,
and we do it on purpose. We want to engage, productively, with others. To do
this at scale, need a gazillion people evaluating content, or machine learning.
• Images
• Text

16. Ex: Facebook campaign
target people, serve content

17. Pro
• Facebook takes care of
administration
• Facebook controls everything
• Facebook already has tons of
data, you don’t need to gather
• Can be effective way to reach
audience and build brand
• Can be cost-efficient compared
to other channels
• Can give you more data
about your campaigns
• You aren’t in control, Facebook is
• Your reach might be limited by your
own network
• “Who you know” is an after-effect of
interactions in the real world;
Correlation != intention
• Can be creepy to users?
• Usually not a leading indicator
• Interests change faster than your social
graph. (Shopping for a house -> buy a
house, not buying another. Might need
a plumber though…)
Con

18. machine learning in a nutshell
Content = images + text
deep neural networks
DATA
FEATURES
REP
MODEL
PREDICTIONS
informative pieces of things
feature vectors
search / similarity, sentiment, emotion,

19. 1. Machine learning
2. Digital marketing
3. How it works: using ML to automate digital marketing
4. Examples: real-world use cases

20. Example:
Find the most positive
and negative reviews
(sentiment analysis)

21. indico API: Sentiment
(one line of python code)

22. Example:
What emotions are being
expressed by chunk of text?

23. indico API: Emotion

24. Example:
What personality type/tone was
being expressed in this text?

25. indico API: Personas (MBTI)

26. Ex: Persuasion marketing
What are influencers saying?
sentiment, topics, etc

27. Example:
Grow your audience

28. Twitter Engagement Campaign

29. Can we find people using
the content they post?

30. Goal:
Find new followers
who write content
that looks like
our followers

31. 0
0.075
0.15
0.225
0.3
Twitter indico
conversionrate(%)
An experiment:
Twitter user segmentation vs. indico model of user content

32. Example:
Image-in-image

33. People posing with wine glasses in
social media images
Image credit: indico

34. Photo with wine glass -> intent <- campaign
Image credit: indico

35. How does it work?
Convolutional neural networks

36. Image credit: Nando de Freitas, “Deep Learning Lecture 10”: https://www.youtube.com/watch?v=bEUX_56Lojc

37. Example:
Content filtering

38. Example:
user-generated content
Image credit: Brigitewear International; www.shop-brigite.com, modified with crop and pixelation
You have a brand
Your brand has an identity
(Disney vs. Calvin Klein)
Your audience might
have different sensibilities
than you do, about
what is appropriate
for your brand
Use ML to filter out the
inappropriate content

39. What digital channels require
content filtering?
• Social media (Instagram, Facebook, Twitter, …)
• e-Commerce (Ebay, Amazon, …)
• Web content (News, Forums, blogs)
• Mobile (Apple, Google)
Anywhere users upload
content that everyone
can see.

40. …also brands using
social media to engage
directly with the public?
Doh! Don’t let this
happen to your brand.

41. …also brands using
social media to engage
directly with the public?
Doh! Don’t let this
happen to your brand.

42. Digital Marketing + ML
Daniel Kuster, Ph.D.
@indicodata @djkust
Questions?

43. Image credit for Mad Men / Don Draper images: AMC Studios
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