Real-time bidding, in the context of digital marketing, refers to the purchase of advertising impressions one at a time, responding to tens of thousands of messages per second, paying a different price for each via an auction mechanism. This talk will cover in detail how Datacratic’s RTB Optimizer Prediction API predicts the outcome of buying a given impression, then computes the economic value of that outcome to produce optimal bidding behaviour.

1. REAL TIME MACHINE LEARNING DECISIONS AS A SERVICE
RTB Optimizer:
Behind the scenes with
a Predictive API
Nicolas Kruchten
PAPIs.io – November 18, 2014

2. About Datacratic
• Software company specializing in
high performance systems and
machine learning
• 30 employees, founded in 2009,
based in Montréal, Québec, Canada with an office in New York
• 3 Predictive APIs in market today
• Building a Machine Learning Database to help others
build Predictive APIs and Apps

3. Real-Time Bidding for online advertising
Real-Time
Exchange
Bidder
Bidder
Bidder
Bidder
Web
Browser
GET ad
bid requests

4. Real-Time Bidding for online advertising
Real-Time
Exchange
Bidder
Bidder
Bidder
Bidder
Web
Browser
ad
bids
auction

5. Real-Time Bidding for online advertising
Real-Time
Exchange
Bidder
Bidder
Bidder
Bidder
Web
Browser
ad
bids
auction
This happens millions of times per second
Bidders must respond within 100 milliseconds

6. Real-Time Bidding for online advertising
Real-Time
Exchange
Bidder
Bidder
Bidder
Bidder
Web
Browser
ad
bids
auction
RTB Optimizer enables bidders to achieve campaign goals

7. Campaign goals
• Advertising campaigns are typically outcome-oriented
– Clicks
– Video views
– Conversions: app installs, purchases, sign-ups
• e.g. Ad network has sold someone 1,000 outcomes for $1,000
• e.g. Advertiser has $1,000 to get as many outcomes as
possible
• Essentially maximize profit or minimize cost-per-outcome

8. Datacratic’s RTB Optimizer
• Client bidder relays bid-requests to API, API tells it how to bid
• Handles 100,000 queries per second, for 100s of campaign
• API says which campaign should bid and how much
• API also needs outcomes in real-time and campaign goals

9. RTB Optimizer
Bids API
Outcomes
API

10. A Predictive API that learns
• Datacratic has no proprietary data set
• API can learn from scratch from the bid-request stream
what works for each campaign:
– Contextual features: website, time of day, banner size and placement
– User features: geo-location, browser, language, # of impressions shown
– Customer-provided data: about the user, about the website
• Provides insights into what features are driving performance
• Can re-use learnings from previous campaigns

11. Second price auctions
• First Price Auctions
– You bid $1, I bid $2: I win, and I pay $2
• RTB uses Second Price Auctions
– You bid $1, I bid $2: I win, and I pay $1
• Optimal bid = E[ value ]
– Say it’s worth $2 to me
– I will never bid more than $2
– If I bid $1.50 and you bid $1.75: I’ve lost an opportunity for $0.25 surplus!
– I should always bid $2

12. Don’t buy lottery tickets!
E[ value ] = payout * P( getting the payout )

13. What’s it to you?
• If client gets paid $10,000 for 1,000 then payout = $10
E[ value | bid-request ] = $10 * P( conversion | bid-request )
• What was an economics problem is now a prediction
problem
• We need to calibrate to predict true probabilities

14. RTB Optimizer
Bids API
E[ value ]
Outcomes
API
P( outcome )

15. Collecting the data
• To compute P( X | Y ) we need examples of Y’s with an X label
• RTB Optimizer uses mix of strategies to meet campaign goals
• Probe strategy bids randomly to collect data
• Optimized strategy bids with E[ value]
• Automatic training/retraining when API see enough examples

16. RTB Optimizer
Bids API
Probe
E[ value ]
Outcomes
Training
API
P( outcome )

17. Bias control
• Never stop the probe strategy
• Always need control group for evaluation, retraining
• Risk of filter bubbles: future models trained on previous output
• Bid requests are randomly routed to probe, less often over time
• Models automatically back-tested before deployment

18. How to learn in real-time
• Classify using bagged generalized linear models
• Generate non-linear features with statistics tables
• Periodically retrain classifier
• Continuously update stats tables

19. Statistics Table by example
Table Bucket Impressions Outcomes
Outcomes/I
mpressions
95% Confidence
Lower Bound on
Outcomes/Impressi
ons
Browser
Chrome 5M 3k 0.060% 0.058%
Firefox 3M 1k 0.033% 0.031%
Website
abc.com 4M 2k 0.050% 0.048%
xyz.com 1k 10 1.000% 0.481%

20. RTB Optimizer
Bids API
Probe
Real-Time
Stats
Tables
E[ value ]
Outcomes
Training
API
GLZ Classifier
Batch

21. Implementation details (are everything)
• 100k requests per second, 10 millisecond latency, running
24/7,
1 trillion predictions to date
• Distributed system, written in C++ 11
• AWS: data in S3, training runs on Amazon EC2 spot market
• http://opensource.datacratic.com/
– RTBkit
– JML
– StarCluster

22. Does it work?
Classification success? ROC or calibration curves…

23. Does it work?
Classification success? ROC and calibration curves…
Optimization success? 80% reductions in cost-per-outcome…

24. Does it work?
Classification success? ROC or calibration curves…
Optimization success? 80% reductions in cost-per-outcome…
Customer success! 25% monthly growth

25. REAL TIME MACHINE LEARNING DECISIONS AS A SERVICE
Thanks!
nicolas@datacratic.com