how machine learning is shaping digital marketing
TRANSCRIPT
Digital Marketing + ML
Daniel Kuster, Ph.D. @indicodata @djkust
1. Machine learning
2. Digital marketing
3. How it works: using ML to automate digital marketing
4. Examples: real-world use cases
what is machine learning?
machine learning in a nutshelltext, 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 }
what is digital marketing?
digital marketing = marketing + data
many channels under the digital marketing umbrellaImage credit: eperales via flickr.com; modified by cropping
Vide
o (Y
ouTu
be)
Socia
l med
ia (Tw
itter,
Fb, In
sta)
Search ads (Google)
Email (M
ailchimp)
Cont
ent (
new
s, bl
ogs)
e-comm
erce (Amazon)
Mobile apps (iOS, Android)
SMS/text messaging (telecoms)
Chat/instant messaging (Slack)
OK, what ISN’T digital marketing?
Broadcast media (TV, radio)
Most media-on-disc
Word of mouth
Books
Printed newspaper
Signs & Billboards
There are many channels + kinds of data
?what year did $ spent for display ads
surpass search ads?
2016
1. Machine learning
2. Digital marketing
3. How it works: using ML to automate digital marketing
4. Examples: real-world use cases
Since we have data, let’s automate this…
Why automation?
Scale / throughput Latency Cost Complexity
Two kinds of data here1. 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
Ex: Facebook campaign target people, serve content
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 dataabout 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
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,
1. Machine learning
2. Digital marketing
3. How it works: using ML to automate digital marketing
4. Examples: real-world use cases
Example: Find the most positive and negative reviews
(sentiment analysis)
indico API: Sentiment(one line of python code)
Example: What emotions are being
expressed by chunk of text?
indico API: Emotion
Example: What personality type/tone was
being expressed in this text?
indico API: Personas (MBTI)
Ex: Persuasion marketing
What are influencers saying?
sentiment, topics, etc
Example: Grow your audience
Twitter Engagement Campaign
Can we find people using the content they post?
Goal: Find new followers who write content that looks like our followers
0
0.075
0.15
0.225
0.3
Twitter indico
conv
ersi
on ra
te (%
)An experiment: Twitter user segmentation vs. indico model of user content
Example: Image-in-image
People posing with wine glasses in social media images
Image credit: indico
Photo with wine glass -> intent <- campaign
Image credit: indico
How does it work? Convolutional neural networks
Image credit: Nando de Freitas, “Deep Learning Lecture 10”: https://www.youtube.com/watch?v=bEUX_56Lojc
Example: Content filtering
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
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.
…also brands using social media to engage directly with the public?
Doh! Don’t let this happen to your brand.
…also brands using social media to engage directly with the public?
Doh! Don’t let this happen to your brand.
Digital Marketing + ML
Daniel Kuster, Ph.D. @indicodata @djkust
Questions?
