2. Ad
Exchange
Shopping at one of
our campaign sites
cookies
10 Million
URL’s
200 Million
browsers
0.0001% to 1%
baserate 10 Billions of
auctions
per day
conversion
Where should
we advertise and
at what price?
Does the ad have
causal effect?
What data should
we pay for?
Attribution?
Who should
we target for
a marketer?
What requests
are fraudulent?
3. The Non-Branded Web
A consumer’s online/mobile activity
The Branded Web
gets recorded like this:
Our BrowserData:Agnostic
I do not want to ‘understand’ who you are …
Browsing History
Hashed URL’s:
date1 abkcc
date2 kkllo
date3 88iok
date4 7uiol
…
Browsing History
Hashed URL’s:
date1 abkcc
date2 kkllo
date3 88iok
date4 7uiol
…
Brand Event
Encoded
date1 3012L20
date 2 4199L30
…
date n 3075L50
Brand Event
Encoded
date1 3012L20
date 2 4199L30
…
date n 3075L50
5. TheHeartandSoul
Predictive modeling on hashed browsing history
10 Million dimensions for URL’s (binary indicators)
extremely sparse data
positives are extremely rare
Targeting
Model
P(Buy|URL,inventory,ad)
7. Theheartand soul
Has to deal with the 10 Million URL’s
Need to find more positives!
Targeting
Model P(Buy|URL,inventory,ad)
8. Experiment
Randomized targeting across 58 different large display ad campaigns.
Served ads to users with active, stable cookies
Targeted ~5000 random users per day for each marketer. Campaigns ran
for 1 to 5 months, between 100K and 4MM impressions per campaign
Observed outcomes: clicks on ads, post-impression (PI) purchases
(conversions)
Data
Targeting
• Optimize targeting using Click and PI Purchase
• Technographic info and web history as input variables
• Evaluate each separately trained model on its ability to rank order users for PI
Purchase, using AUC (Mann-Whitney Wilcoxin Statistic)
• Each model is trained/evaluated using Logistic Regression
9. .2.4.6.8
AUC
Train on Click Train on Purchase
*Restricted feature set used for these modeling results; qualitative conclusions gener
Predictiveperformance*(AUC)forpurchase
learning
[Dalessandro et al. 2012]
.2.4.6.8
AUC
Train on Click Train on Purchase
®
10. .2.4.6.8
AUC
Train on Click Train on Purchase
®
*Restricted feature set used for these modeling results; qualitative conclusions gener
Predictiveperformance*(AUC)forclick
learning
[Dalessandro et al. 2012]
Evaluatedonpredictingpurchases
(AUCinthetargetdomain)
11. .2.4.6.81
Train on Clicks Train on Site Visits Train on Purchase
AUCDistribution
*Restricted feature set used for these modeling results; qualitative conclusions gener
Predictiveperformance*(AUC)
forSiteVisitlearning
[Dalessandro et al. 2012]
Significantly better targeting training on source
task
Evaluatedonpredictingpurchases
(AUCinthetargetdomain)
14. .2.4.6.81
Train on Clicks Train on Site Visits Train on Purchase
AUCDistribution
*Restricted feature set used for these modeling results; qualitative conclusions gener
Predictiveperformance*(AUC)across58
differentdisplayadcampaigns
[Dalessandro et al. 2012]
Significantly better targeting training on source
task
High cost
High correlation
High Variance
Low cost
Low correlation
High Bias
Low Cost
High correlation
Low Bias & Variance
15. Theheartand soul
Has to deal with the 10 Million URL’s
Transfer learning:
Use all kinds of Site visits instead of new purchases
Biased sample in every possible way to reduce variance
Negatives are ‘everything else’
Pre-campaign without impression
Stacking for transfer learning
Targeting
Model
Organic: P(SiteVisit|URL’s)
P(Buy|URL,inventory,ad)
MLJ 2014
16. Logisticregressionin 10
Milliondimensions
Stochastic Gradient Descent
L1 and L2 constraints
Automatic estimation of optimal learning rates
Bayesian empirical industry priors
Streaming updates of the models
Fully Automated ~10000 model per week
KDD 2014
Targeting
Model
p(sv|urls) =
18. 0
5
10
15
20
25
0
1.0M
2.0M
3.0M
4.0M
5.0M
6.0M
NNLiftoverRON
TotalImpressions
median lift = 5x
Note: the top prospects are consistently rated as
being excellent compared to alternatives by advertising
clients’ internal measures, and when measured by their
analysis partners (e.g., Nielsen): high ROI,
low cost-per-acquisition, etc.
Lift over random for 66 campaigns
for online display ad prospecting
Liftoverbaseline
<snip>
21. Measuringcausaleffect?
A/B Testing
Practical concerns
Estimate Causal effects from observational data
Using targeted maximumlikelihood(TMLE)
to estimatecausal impact
Canbe done ex-post for different questions
Need tocontrol for confounding
Data has to be ‘rich’and cover allcombinations of
confounding and treatment
ADKDD 2011
E[YA=ad] – E[YA=no ad]
24. Hardshipsofcausality.
Beauty is Confounding
determines both the probability
of getting the number and of the
probability that James will say it
need to control for the actual
beauty or it can appear that
making compliments is a bad idea
“You are beautiful.”
25. Hardshipsof causality.
Targeting is Confounding
We only show ads to people
we know are more likely to
convert (ad or not)
conversionrates
DID NOT SEE ADSAW AD
Needtocontrolforconfounding
Datahastobe‘rich’andcoverall
combinationsofconfoundingand
treatment
42. Now it is coming also to brands
• ‘Cookie Stuffing’ increases the value of the ad for
retargeting
• Messing upWeb analytics …
• Messes up my models because a botnet is easier to
predict than a human
43. Fraudpollutesmymodels
• Don’t show ads on those sites
• Don’t show ads to a high jacked browser
• Need to remove the visits to the fraud sites
• Need to remove the fraudulent brand visits
When we see a browser on caught up in fraudulent
activity: send him to the penalty box where we
ignore all his actions
46. 1. B. Dalessandro, F. Provost, R. Hook. Audience Selection for On-Line Brand
Advertising: Privacy Friendly Social Network Targeting, KDD 2009
2. O. Stitelman, B. Dalessandro, C. Perlich, and F. Provost. Estimating The Effect Of
Online Display Advertising On Browser Conversion. ADKDD 2011
3. C.Perlich, O. Stitelman, B. Dalessandro, T. Raeder and F. Provost. Bid Optimizing
and Inventory Scoring in Targeted Online Advertising. KDD 2012 (Best Paper Award)
4. T. Raeder, O. Stitelman, B. Dalessandro, C. Perlich, and F. Provost. Design
Principles of Massive, Robust Prediction Systems. KDD 2012
5. B. Dalessandro, O. Stitelman, C. Perlich, F. Provost Causally Motivated Attribution for
Online Advertising. In Proceedings of KDD, ADKDD 2012
6. B. Dalessandro, R. Hook. C. Perlich, F. Provost. Transfer Learning for Display
Advertising MLJ 2014
7. T. Raeder, C. Perlich, B. Dalessandro, O. Stitelman, F. Provost. Scalable Supervised
Dimensionality Reduction Using Clustering at KDD 2013
8. O. Stitelman, C. Perlich, B. Dalessandro, R. Hook, T. Raeder, F. Provost. Using Co-
visitation Networks For Classifying Non-Intentional Traffic‘ at KDD 2013
46
SomeReferences
