acquisio 2015 advancements in machine learning

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2015 SUMMIT MONTREAL | JULY 29+30

Advancements in Machine Learning


©2015 All rights reserved 2ACQUISIO 2015 SUMMIT MONTREAL – JULY 29+30 #ACQsummit

Advancements in Machine Learning

30 Jul 2015Bryan M. Minor, Ph.D.

Chief Scientist, Acquisio



Outline

What is Machine Learning?

BBM problem statement

Examples of how we use Machine Learning with BBM

Gamification

Acquisio and Machine Learning



What is Machine Learning?

Machine learning is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. Machine learning explores the construction and study of algorithms that can learn from and make predictions on data.

– Wikipedia (https://en.wikipedia.org/wiki/Machine_learning)



BBM problem:• For a fixed Budget for budget period (month)

– With a group of Campaigns (Budget Group)

• Make Daily Budget last whole Day

• Maximum Average CPC per day limit

• Fairly compete Campaigns based on value of Clicks (conversions)

• Maximize Clicks (conversions)



BBM Theory

C B

AAA

minCPC

0 2 4 6 8 1 00

5

1 0

1 5

2 0

2 5

3 0

3 5

C P C

Clicksday



Using Machine Learning in solving BBM problem

Setting Daily Budget

Setting Bid every 30 minutes

Anomaly detection

Day of week % of spend allocation

Managing Mobile bidding

Allocation of Budget across Publishers (AdWords, Bing, Yahoo!Japan,…)



BBM Spend - Jun 2015



BBM Constraint Obedience - Jun 2015



Proving Machine Learning works:

20,000 Campaigns in AdWords• 12,000 on BBM• 8,000 not on BBM

June 2015



BBM Search - Daily Budget spend

Case (Spent Daily Budget %) times (Not BBM %)BBM 3.6BBM (3.7+ grade) 4.0



BBM Search performance – Daily Budget Spent caseCase Imp Share IS LTB IS LTR CPC Avg. Pos.Not BBM 47.28% 32.49% 29.14% $6.46 2.306BBM 55.42% 18.43% 26.13% $4.04 2.417BBM (3.7+ grade) 54.99% 15.84% 20.23% $2.95 2.484

Case Imp Share % IS LTB % IS LTR % CPC % Avg. Pos. %BBM 17.2% -43.3% -10.3% -37.5% -4.8%BBM (3.7+ grade) 16.3% -51.2% -30.6% -54.4% -7.2%



BBM Search performance – Daily Budget not Spent caseCase Imp Share IS LTR CPC Avg. Pos.Not BBM 64.51% 35.49% $4.29 2.50BBM 77.18% 22.82% $4.05 2.22BBM (3.7+ grade) 73.41% 26.59% $2.91 2.35

Case Imp Share % IS LTR % CPC % Avg. Pos. %BBM 19.6% -35.7% -5.7% 11.1%BBM (3.7+ grade) 13.8% -25.1% -32.3% 6.4%



Daily Budget Spend (%)



Distributions of Daily Budget spent (%) - Human



Distributions of Daily Budget spent (%) – BBM no Skynet



Distributions of Daily Budget spent (%) – BBM with Skynet



Day of Week % Spend Machine Learning



Gamification

Rewards good behavior

Enhances inner commitment

1. Autonomy

2. Mastery

3. Connection



Acquisio and Machine Learning

Big Data is Different

Continuous discovery of key relationships for Predictions

Fine grain refinement of Predictions

Cornerstone of Gamification

Anomaly detection

Cross Publisher optimization


2015 SUMMIT MONTREAL | JULY 29+30

Thank you!

Bryan M. Minor, Ph.D.Chief Scientist, [email protected]@BryanMinorPhD

mailto:[email protected]

acquisio 2015 advancements in machine learning

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