Online Advertisement Campaign Optimization
description
Transcript of Online Advertisement Campaign Optimization
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Online AdvertisementOnline AdvertisementCampaign OptimizationCampaign Optimization
Shi ZhongData Mining and Research Group
Yahoo! Inc. Joint work with Weiguo Liu, Shyam Kapur, and Mayank Chaudhary,
published in IEEE/INFORMS SOLI Conference
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Agenda
Introduction to online advertisingOnline ad campaign optimization problem
Focus: display advertising (i.e., graphical/banner ads)
Approaches and resultsConclusion
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Yahoo Sponsored Search
Text Ads
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Google Content Match
Text Ads
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Display Ads on Yahoo
LREC, 300x250
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Online Advertising
Text ads Two main categories, a few major players
Sponsored searchE.g., Google search, Yahoo search, Live.com, Ask.com
Content matchE.g., Google adsense, Yahoo YPN
Cost models: CPC Targeting: search query, page content
Display ads Fragmented market Cost models: CPM, CPC, CPA Targeting: content, demo, geo, behavioral, or none
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Online Ad Campaign Optimization
Netflix, Q4 AdvertisingBudget=$500k,Drive traffic to netflix.com
Google Adwords$250k{dvd rental, online dvd, online movie, …}
Yahoo Display Ads$150k{yahoo top page + LREC, yahoo movie + N, BT=entertainment/movie, …}
DoubleClick$100k{CNN.COM + LREC, IMDB.com + N, …}
Ad Agencies
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We focus on …
Display advertising campaignsOptimize media buys given a campaign budget and/or campaign objectives
Maximize # conversions/clicks for a given budget Minimize cost for a given number of conversions/clicks
Experiments inside Yahoo Media buys limited to Yahoo products
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A Campaign Example
A campaign contains multiple lines/productsA line specifies a product from the publisher, a quantity, and a priceA product consists of page location, position, and profile
Page Location Position Targeting Profiles Impressions (thousands)
CPM($)
Run-Of-Personal N Age>=35, Country=US, FreqCap=1 1,476 0.8
Run-Of-Entertainment
SKY Age>=30 3,060 0.89
Run-Of-Movie LREC Age>=30, Country=US, FreqCap=3 3,000 2.72
Run-Of-Network SKY BT=Entertainment, Country=US, FreqCap=1 8,963 0.65
Run-Of-Espanol LREC BT=Entertainment/Movie, Country=US 60 13.51
Run-Of-Maps LREC Age>=30, State=CA, Mon-Fri 7am-10pm 2,000 3.65
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Quantity and Price Quantity is capped by inventory availabilityPrice is determined by a bidding process
Except for “guaranteed delivery” – for which advertisers have to pay a premium
Higher bid earns higher priority at ad delivery time, thus has a higher probability getting more impressions
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Optimization Formulation - I
Maximize profit for a given budget
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ctr = click through ratecpm = cost per thousand imps
rpc = revenue per clickBudget = total budget= max fraction of Budget per line = profit marginCapi = available # imps for line i
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Optimization Formulation - II
Minimize cost for a desired number of clicks
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Test Results Take a few historical campaigns with Yahoo for some advertiserCompare simulated results from optimization formulation-II with historical campaignsAverage cost saving (for generating same number of clicks) is 26%
History Cost Optimal Cost Saving Campaign 1 $63,503 $38,741 39% Campaign 2 $276,629 $211,472 24% Campaign 3 $376,955 $279,254 26% Total $717,088 $529,468 26%
sounds simple, but …
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Prepare inputs to optimization engine
Collect/generate product lines Use historical lines of similar advertisers Use data mining techniques to learn “new” lines that are
expected to perform well Use predictive modeling to discover/explore new lines
Estimate CTR for each product Quantity-CPM curve for each product RPC for a given advertiser/business
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Identify High CTR Segments
Data examplesPage Location Position Age category Geo Location …… Click
Finance N 45-54 CA 0
Autos LREC 30-34 CA 1
……
Finance LREC 35-44 FL 0
Approach:1. Extract frequent segments (with min # impressions) with frequent
itemset mining algorithm
2. Calculate CTR for each segment
3. Check overlap and temporal stability for high CTR segments
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Identified Segment Examples
Example high-CTR segments• Page:News + Position:LREC + Age:35-54 CTR=0.31%
• Page:Weather + Position:LREC CTR=0.32%
(Baseline average CTR ~ 0.03%)
CTR numbers seen to be stable over timeCPM estimated from most similar historical lines or
Yahoo’s internal pricing system
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Conclusion
Data mining and optimization work together nicely to enhance campaign effectivenessAn optimized campaign can be very rewardingFurther research
Ad creative optimization Landing page optimization
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Questions?