Guest lecture given in introductory data science course in Aalto University on 2017-11-30. About how data science is used in online marketing by Facebook and Smartly.io
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Data Science in Online Marketing
1. Data Science in
Online Marketing
Markus Ojala
PhD, Chief Data Scientist @ Smartly.io
Aalto University - 2017-11-30
2. Data Scientist (n.):
Person who is better at statistics than
any software engineer and better at
software engineering than any
statistician.
@josh_wills
3. My
story
2004-2008: Aalto, tech. physics
2008-2011: PhD in inf. & comp. science
2011-2015: Xtract & Comptel
2015- : Smartly.io
Randomization Algorithms for
Assessing the Significance of
Data Mining Results
8. Dimensions in online marketing
Campaign objective
(sales, branding)
Target audience,
budget, bid
Ad content
Targeting
Ad type
and
content
Bidding
Budget
9. Example: e-commerce with product feed
- XML, CSV, TSV, JSON
- No limits on the number of products
- Refresh up to once an hour
!
12. Bid value for the
desired outcome
Estimated
action rate
Relevance &
quality
X +Bid =
Maximizes advertiser value Optimizes consumer
experience
Conversion
Optimized
Bidding
It’s simple.
You can bid directly
for your end goal.
13. Bid value for the
desired outcome
Estimated
action rate
Relevance &
quality
X +Bid =
Maximizes advertiser value Optimizes consumer
experience
Conversion
Optimized
Bidding
It’s simple.
You can bid directly
for your end goal.
$50 0.5%$0.25
Predicting estimated action rate per user:
● Target: 1 user converted, 0 user did not convert
● Predictors: age, gender, location, interests,
behaviour…
● Simple model: e.g., logistic regression
● Use historical data to train the model
14. Budget pacing
maximizes
the profit
Facebook finds the
cheapest conversions
throughout the day
if budget limits
https://www.smartly.io/blog/bid-optimization-pacing-on-facebook-explained
33. Bayesian Bandits / Thompson sampling
The number of pulls for a given lever should match
its actual probability of being the optimal lever
Sample from the posterior for the mean of each lever
36. Moving from conversions to revenue
ROAS = revenue / cost
= revenue / conversions * conversions / cost
= revenue / conversions * 1 / CPA
ROAS = return on ad spend
CPA = cost per action
Existing model
● Lot of data
● Varies fast
● Big differences
New model
● Little data
● Varies slowly
● Small real differences
● Lot of random variation
37. Modeling the revenue per purchase
Revenue follows usually a long-tailed distribution, use log-normal
47. You do not really understand
something unless you can explain
it to your grandmother.
Einstein / Feynman / ...
48. https://www.smartly.io/blog/
● Experiences in Using R and Python in Production
● Insights to Predictive Budget Allocation
● Automated Statistical Significance Calculator for A/B Testing
● Tutorial: How We Productized Bayesian Revenue Estimation with Stan
Read more
49. WORK SMARTLY
We’re looking for new Humble Hungry Hunters
to build a world-class product company.
https://www.smartly.io/careers
Markus.Ojala@smartly.io