This document discusses machine learning applications for digital advertising. It describes how machine learning can optimize advertising by predicting click-through rates and conversion probabilities based on user, ad, and website data. However, obtaining sufficient high-quality data for machine learning models is challenging for advertisers due to data sparsity, high costs, and lack of access to raw user data from platforms. The document also outlines some machine learning techniques like gradient boosting trees that have shown promise for digital advertising optimization.

1. Machine Learning for Digital Advertising
Marc Garcia
Outreach digital - June 14th, 2016
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Machine Learning for Digital Advertising - @datapythonista - @outreachdigit

2. Digital advertising: a data scientist point of view
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3. Digital advertising: a data scientist point of view
What is the Internet? A lot of bored people...
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4. Digital advertising: a data scientist point of view
...who use a gadget...
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5. Digital advertising: a data scientist point of view
...to access useless sites.
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6. Digital advertising: a data scientist point of view
Ok, some of them are looking for something.
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7. What is our goal?
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8. What is our goal?
Get their money!
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9. What is our goal?
That’s the truth,
but let’s say it in a more subtle way...
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10. What is our goal?
We want to attract them to our site...
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11. What is our goal?
...and sell our product.
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12. How does it work?
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13. How does it work?
The user journey
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14. How does it work?
The user journey
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15. How does it work?
The user journey
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16. How does it work?
The user journey
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17. How does it work?
The user journey
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18. Ad exchange point of view
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19. Ad exchange point of view
How the ad exchange works?
A publisher has an available ad space.
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20. Ad exchange point of view
How the ad exchange works?
Advertisers bid for their ads on the cost for a click.
bid: £0.35 bid: £0.20
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21. Ad exchange point of view
How the ad exchange make money?
income = bid ·
clicks
impressions
(1)
Impressions are constant regardless of the ad
Bids are known for each ad
Clicks are unknown, but can be predicted
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22. Ad exchange point of view
What is the best option?
bid: £0.35
expected clicks (for 1000 impressions): 25
expected profit (for 1000 impressions): £8.75
bid: £0.20
expected clicks (for 1000 impressions): 47
expected profit (for 1000 impressions): £9.40
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23. E-commerce point of view
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24. E-commerce point of view
T-shirt e-commerce example
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25. E-commerce point of view
Income statement1
Sales £23.89 100%
Cost of goods sold £11.72 60%
Delivery £4.36 18.25%
Payment gateway £0.84 3.5%
Total direct costs £19.53 81.75%
Available for marketing £4.36 18.25%
Average cost-per-click £0.20
Conversion rate (break-even) 22
1
Excludes many costs (product returns, freight-in, development, support, hardware, salaries, etc.)
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26. E-commerce point of view
Not so easy to make money...
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27. An optimization problem
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28. An optimization problem
What can be optimized?
Higher prices (added value, branding...)
Lower production costs
Leverage marketing costs
Virality (share on social networks...)
Recurrence (newsletter to customers...)
Conversion rate optimization
Targeting / market segmentation
Ad optimization
Website optimization (A/B testing...)
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29. An optimization problem
Conversion rate optimization: the numbers
income = average_price · margin · num_sales − average_cpc · num_clicks (2)
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30. An optimization problem
Conversion rate optimization: Features
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31. An optimization problem
Features: Users
What Facebook, Google... know about our users?
Age
Gender
Location
Interests / Pages visited
Friends / Interactions
Sociocultural status
... mostly everything
What do we know about them?
Not much individually
We can access aggregated data
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32. An optimization problem
Features: Ads
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33. An optimization problem
Features: Website
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34. An optimization problem
Features: Summary
Users
Age
Gender
Location
Interests
Friends
Sociocultural status
...
Ads
Source
Message
Type (image,
text,...)
Placement
Site
...
Website
Layout
Colours, font size...
Content density
Offers, reviews...
Conditions
(delivery, returns)
Steps to purchase
...
Other
User behaviour
Day of week
Time
Weather
Social environment
...
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35. An optimization problem
An amazing dataset
Age 28
Gender female
Location Lewisham
Occupation receptionist
Interests hiking, salsa
Education university
Device smartphone
Source Google search
Placement top
Ad type image
Message type sales offer
Web layout basic
Web main colour green
Conditions free delivery
Returns free
Product type shoes
Quality high
Price £79
Day business day
Time evening
Purchase true
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36. An optimization problem
What could we get from a dataset like this?
Women aged 25 to 35 buy shoes on rainy Sundays
Users leave pages with more than 800 characters on the layout quite fast
People from city A buy more product X, while people from city B, prefer product Y
Ads with red colours have more clicks, but we get less conversions from them
And all this means higher profits.
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37. An optimization problem
Amazed?
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38. Introduction to machine learning
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39. Introduction to machine learning
So, what is machine learning?
Different definitions
A fancy word for statistics
The science of programming computers, so we don’t tell them what to do, but we tell them how
to learn what to do
A set of techniques to implement statistical models that are able to predict, and find patterns in
the data
Similar concepts: Big data, data science, data mining, deep learning, artificial intelligence...
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40. Introduction to machine learning
What can machine learning do?
It can accurately compute the probability of a certain user, to click on a certain ad.
bid: £0.35
Click probability: 0.025
Expected profit: £0.00875
bid: £0.20
Click probability: 0.047
Expected profit: £0.00940
A small difference in profit but at scale: multiply by 1bn daily Fb users: £650,000/day
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41. Introduction to machine learning
How does machine learning work?
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42. Introduction to machine learning
How does machine learning work?
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43. Introduction to machine learning
Some maths
Prclick =
1
1 + e−(β0+β1·x1+β2·x2)
(3)
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44. Introduction to machine learning
More complex models: neural networks
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45. Machine learning for digital advertising
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46. Machine learning for digital advertising
Some problems
Data is expensive (average of £0.20 per row) :(
No public data
So, we need to obtain it all ourselves...
...and it’s expensive :(
Unbalanced / sparse data (1 purchase for 100 clicks)
So, we need 100x the data...
...and it’s expensive :(
High (or very high) dimensionality
So, we need a huge number of rows to avoid the curse of dimensionality
...and it’s expensive :(
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47. Machine learning for digital advertising
More problems
We bias the data when we know who is more likely to buy
Exploration an exploitation problem
We have to buy "bad" clicks to avoid it
Many categorical variables
They don’t play well with many models
Data seasonality
Black Friday, sales, Christmas...
Access to data
Google, Facebook do not give access to raw data
We have to use aggregated data
And they do not let you cross variables
We need to assume independence among them
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48. Machine learning for digital advertising
He is very sad now
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49. Machine learning for digital advertising
What models can be used?
Some techniques that gave good results:
Gradient boosting trees
Active learning (semisupervised learning)
Online learning (for data freshness)
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50. Machine learning for digital advertising
Questions?
@datapythonista
http://datapythonista.github.io
https://www.linkedin.com/in/datapythonista
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