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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
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Online AdvertisementCampaign 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 • Focus: display advertising (i.e., graphical/banner ads) • Approaches and results • Conclusion
Text Ads Yahoo Sponsored Search
Text Ads Google Content Match
Display Ads on Yahoo LREC, 300x250
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
Online Ad Campaign Optimization Yahoo Display Ads$150k{yahoo top page + LREC, yahoo movie + N, BT=entertainment/movie, …} Google Adwords$250k{dvd rental, online dvd, online movie, …} Ad Agencies DoubleClick$100k{CNN.COM + LREC, IMDB.com + N, …} Netflix, Q4 AdvertisingBudget=$500k,Drive traffic to netflix.com
We focus on … • Display advertising campaigns • Optimize 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
A Campaign Example • A campaign contains multiple lines/products • A line specifies a product from the publisher, a quantity, and a price • A product consists of page location, position, and profile
Quantity and Price • Quantity is capped by inventory availability • Price 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
s.t. qi = # imps for line i, in thousands cpm = cost per thousand imps ctr = click through rate rpc = revenue per click Budget = total budget = max fraction of Budget per line = profit marginCapi = available # imps for line i Optimization Formulation - I Maximize profit for a given budget
s.t. Optimization Formulation - II Minimize cost for a desired number of clicks nc = desired # clicks
Test Results • Take a few historical campaigns with Yahoo for some advertiser • Compare simulated results from optimization formulation-II with historical campaigns • Average cost saving (for generating same number of clicks) is 26% sounds simple, but …
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
Identify High CTR Segments Data examples • Approach: • Extract frequent segments (with min # impressions) with frequent itemset mining algorithm • Calculate CTR for each segment • Check overlap and temporal stability for high CTR segments
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 time • CPM estimated from most similar historical lines or Yahoo’s internal pricing system
Conclusion • Data mining and optimization work together nicely to enhance campaign effectiveness • An optimized campaign can be very rewarding • Further research • Ad creative optimization • Landing page optimization