Prediction and Optimization Models for Online Display Advertising

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Prediction and Optimization Models for Online Display Advertising

Prediction and Optimization

Models for Online Display

Advertising

_________________________"

Mahesh Kumar!

CEO, Tiger Analytics!

March 5 th , 2012!

_________________________!

!


Tiger Analytics"

• A boutique data analytics consulting firm started in 2010!

• Focus areas!

– Retail merchanding!

Online advertising!

– Internet marketing!

Online advertising!

Display advertising!

– Social media advertising: facebook, twitter, linkedin!


LucidMedia"

• LucidMedia is a technology start-up, focused on

making the bidding and execution for display ads

easy and effective!

• They specialize in buying ads from ad-exchanges via

real-time-bidding (RTB)!

• They manage about $50M of !

display ads per year for !

Fortune 500 companies!


Overview"

• Real-time bidding (RTB) overview!

• Overview of analytical problems in RTB!

• Data Issues!

• Solution Approach and Results!

• Current projects!

4"


Real Time Bidding (RTB) 


for Display Ads"

Ad-network 1

Advertiser

Publisher

User Ad-Exchange Ad-network 2

(NYT.com)

Advertiser

Advertiser

Ad-network 3

5"


Analytics Problems"

• Click-through rate (CTR) prediction!

– What is the predicted CTR for an impression based on its

user and webpage characteristics!

– Note that CTR for the same impression will be different for

different ad campaigns!

• Bid price optimization!

– What is the optimal campaign for this impression!

– What is the optimal bid price!

6"


Sample data"

7"


CTR Prediction"

• Goal is to identify variables that are key determinants of the click

probability!

• Standard statistical tools!

– Logistic Regression!

– Classification Trees!

• Challenges!

– More than 5000 variables!

– Millions of data points!

– Sparse and missing data!

– Clicks are very rare (typically 3-4 clicks in 10,000 impressions)!

8"


Variable Reduction"

• Too many variables lead to!

– Computational burden!

– Diluted results!

– Difficult interpretation of results!

• Solution approach!

– Drop infrequent variables!

– Drop correlated variables!

– Combine similar variables!

• This reduced the number of variables to about 100!

9"


Reducing number of data points"

• Case sampling!

– Keep all impressions with clicks!

– Keep only a random sample of 10k non-clicks!

• This reduced the data size by 100-fold, but prediction

accuracy was as good as when using all data!

10"


Logistic Regression results"

Top 20% of data got 232 out of 415 (56%) of clicks"

450 !

400 !

350 !

300 !

250 !

200 !

150 !

100 !

50 !

- !

1! K 1001! K 2001! K 3001! K 4001! K 5001! K

0% 20% 40% 60% 80% All data

predicted!

baseline!

• Total testing data!

– 500k impressions!

– 415 clicks!

• In the top 100k impression!

– We got 232 clicks!

– Baseline is only 83 clicks!

• A lift of 180%"

11"


Insights – Final set of variables"

12"


Campaign and bid price

optimization"

• Given an impression, we want to identify!

– The optimal campaign!

– The optimal bid price!

• Inputs to the optimizer!

– Predicted CTR for each campaign!

– Revenue from each campaign!

– Pacing constraints!

– Other business constraints!

13"


14"

Solution Integration"

Data Processing

Modeling

• Data cleaning

• Dimension Reduc1on

• Click probability model

• Model Valida1on

Deploy

Campaign Op1miza1on

• Integrate with opera1onal

processes

• Large number of campaigns

• Execu1on efficiency

• Click Probability

• Campaign Revenue

Op1miza1on

• Business Constraints


Twitter – CTR Prediction"

NLP

15"


Facebook – Ad optimization"

16"


Facebook – Ad optimization"

17"


Conclusions"

• Predictive analytics for online advertising is a highly growing

area with!

– Plenty of data!

– A large number of interesting problems!

• We were able to provide!

– 180% improvement in CTR!

– 31% reduction in cost!

• We are applying similar concepts on!

– Twitter advertising!

– Facebook advertising !

18"


Questions / Comments "

19"

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