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Prediction and Optimization Models for Online Display
Advertising_________________________"
Mahesh Kumar!CEO, Tiger Analytics!
March 5th, 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"
5"
User Ad-Exchange Ad-network 2
Advertiser
Publisher (NYT.com)
Ad-network 1
Ad-network 3
Advertiser
Advertiser
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"
• 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"
- !
50 !
100 !
150 !
200 !
250 !
300 !
350 !
400 !
450 !
1! 1001! 2001! 3001! 4001! 5001!
predicted!baseline!
K K K K K K 0% 20% 40% 60% 80% All data
Top 20% of data got 232 out of 415 (56%) of clicks"
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"
Solution Integration"
14"
• Data cleaning • Dimension Reduc1on
Data Processing Modeling
Campaign Op1miza1on Deploy
• Click probability model • Model Valida1on
• Click Probability • Campaign Revenue
Op1miza1on • Business Constraints
• Integrate with opera1onal processes
• Large number of campaigns • Execu1on efficiency
Twitter – CTR Prediction"
15"
NLP
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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