Bid Price Dynamics: Data Set

Collected over a 30 day period (~10pm)

Two missing days

45 keywords (Acura, Audi …)

About 400 advertisers, 50K bids

Available in text and SQL format bids: bid_date, keyword, advertiser, bid_price cars: brand, model, year, min_price, max_price searches: keyword, suggested, volume

Bid Price Dynamics: Volatility

Volatility ~ Keyword Desirability Desirability = f(g(bid prices),#adv) Desirability = f(mfg average car price) Desirability = f(search volume)

Volatile/Non-Volatile Advertisers Across keywords and time

Volatility ~ Presence Volatile Advertisers One bad apple

Bid Price Dynamics: Adversary

Regression predict bid price of adv i (or pos p=1,2,3..) on

date t for keyword k covariates: bid prices of top M (4,8,16) adv of

each keyword auction for previous T days

Classification predict whether adv i will increase, or decrease

bid on date t for keyword k covariates: bid prices of top M adv of each

keyword auction for previous T days

> mincon=dbConnect(drv,"/home/ebice/CIS620WIN/Auto-Bid-Data/Data Minutes Apart/Normalized-Data/autobids.db")> bids2=dbGetQuery(mincon,"select * from bids2")> akd_sds=aggregate(bids2$bid_price,list(bids2$advertiser,bids2$keyword,bids2$bid_date),sd)> akd_sds[which.max(akd_sds$x),]

Group.1 Group.2 Group.3 x4249 www.whypaysticker.com Nissan 2006-11-24 0.7159143

> mvminbids=dbGetQuery(mincon,"select * from bids2 where keyword = 'Nissan' and advertiser='www.whypaysticker.com' and bid_date = '2006-11-24'")

> mvminbids

bid_date bid_time keyword advertiser bid_price1 2006-11-24 11:35 Nissan www.whypaysticker.com 1.642 2006-11-24 11:45 Nissan www.whypaysticker.com 0.403 2006-11-24 11:45 Nissan www.whypaysticker.com 1.64

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Intra Day Bid Price Volatility