online advertisement campaign optimization
DESCRIPTION
Online Advertisement Campaign Optimization. Shi Zhong Data Mining and Research Group Yahoo! Inc. Joint work with Weiguo Liu, Shyam Kapur, and Mayank Chaudhary, published in IEEE/INFORMS SOLI Conference. Agenda. Introduction to online advertising Online ad campaign optimization problem - PowerPoint PPT PresentationTRANSCRIPT
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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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s.t. nc = desired # 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?