If you are not paying forit,you are theproduct*:How much do advertisers pay toreachyou?

PanagiotisPapadopoulos

FORTH-ICS,Greece&UniversityofCrete,Greece

panpap@ics.forth.gr

NicolasKourtellis TelefonicaResearch,SpainPabloRodriguezRodriguez TelefonicaAlpha,SpainNikosLaoutaris DataTransparencyLab,Spain

*phraseoriginator:AndrewLew

is,a.k.a.blue_beetle

Data-driveneconomy

• TheuserdataofanITcompany→contributetoitsoverallmarketvaluation

• Companiespursuemoreandmoreuserspersonaldata• Bypurchasingthem• Byprovidingfreeservices(Googlesearch,Facebooketc.)

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Howisallthisdataconvertedtomoney???

ecosystemof digitaladvertising($194.6billionin2016)

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Digitaladvertising

progressivelymovingtowardsaprogrammaticmodel

adsarematchedtointerestsofindividuals

elaboratedusertracking

privacyimplications

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Howmuchdoadvertisersactuallypaytoreachyou?

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TheMotivation…

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TheBackground…

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ProgrammaticauctionsofRTB

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AdExchange(ADX)

Real-timeAuction

Websiteontheuser’sbrowser

DemandSidePlatforms(DSPs)

BidRequest(+userinfo)

0.95CPM(+impression)Availablead-slot

0.95CPM(+impression)

RTBpricenotificationchannel

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Availablead-slot

AdExchange(ADX)

Real-timeAuction

Websiteontheuser’sbrowser

DemandSidePlatforms(DSPs)nURL example:

cpp.imp.mpx.mopub.com/imp?ad_domain=amazon.es&ads_creative_id=ID&bidder_id=ID&..&bidder_name=..&charge_price=0.95&country=ESP&currency=EUR&latency=0.116&mopub_id=ID&pub name=..

AD

Impressiondelivery(i)youwon,(ii)here’sthechargeprice,(iii)theimpressionisrenderedsuccessfully

Winningnotification

TheChallenge…

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Encryptedpricesontherise

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• ChargepricesinnURLs tendtobeencryptedØEncryptionisaregularpracticeindesktopRTBauctions(~68%)ØLowerbutrapidlyincreasinginmobileRTBauctions(~30%)

Previouswork[Olejnik,2013]assumesencryptedpricesfollowthesamedistributionascleartext.Butisthatso?

Encrypted

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Ourapproach

1. LeverageReal-TimeBidding(RTB)protocol:1. 74%ofprogrammaticallypurchasedadvertising2. $8.7billionin2016onlyinUS

2. Methodologytocalculateatrealtimetheoverallvalueadvertiserspayperindividual userbasedonherleakedinformation.

3. Year-longdataset(2015)of1600realusers+2realprobingadcampaigns

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Methodology

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YourAdValue browserextension

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MonitorsRTBnURLs &collectsfeatures:(i)auction-specificmetadataand(ii)personaldatatheuserleakswhilebrowsingtheweb

PriceModelingEngine(PME)

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RequiredinputforthePME

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TheEvaluation…

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Evaluatingourapproach

• Offlineyear-long(2015) datasetD withmobiletrafficfrom1600realusers

• WeblogAdsAnalyzer:ØfilterRTBtrafficandØextractfeatures(auction’smetadataanduserdata)fromnURLs

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Metric D

Timeperiod 12months

Impressions 78,560

IABcategoryofpublishers

18

RTBpublishers ∼5.6k/month

Featureextraction

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Featuresthataffectprices(1/3)

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Distributionofchargepricesforthe2mostpopularmobileOSes.

Whiskers:5th,10th,50th,90th,95th percentiles

MoreAndroiddevices,butiOS-baseddevicesdrawhigherprices

PercentageofRTBtrafficfortopmobileOSes.

0%20%40%60%80%

100%

1 2 3 4 5 6 7 8 9 10 11 12

RTB

sha

re

Month of the year

AndroidiOS

Windows MobOther

0.01

0.1

1

10

100

Android iOS

Cha

rge

pric

e (C

PM)

Mobile Device OS

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Featuresthataffectprices(2/3)

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Distributionofthechargedpricesperad-slotsize(sortedbyareasize).

Whiskers:5th,10th,50th,90th,95th percentiles

Largerad-slotsizesdonotnecessarilygetchargedmore

0 0.2 0.4 0.6 0.8

1 1.2 1.4 1.6 1.8

2

320x50468x60

728x90120x600

300x250160x600

300x600

Cha

rge

pric

e (C

PM)

Ad-slot sizes

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Featuresthataffectprices(3/3)

• userlocationaffectsthemedianchargeprices

• duringthedaymedianchargepricesareofsimilarrange->earlymorninghours- noon:morechargepriceswithincreasedvalues

• someIABsaremorecostlythanothers->(“Business&Marketing”moreexpensivethan“Science”)

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Seemoreaboutfeaturesinthepaper…

PriceModelingEngine:adcampaigns

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Realprobingad-campaigns

• 2realprobingad-campaignsin2016(A1,A2):variousexperimentalsetups

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Metric D A1(enc) A2(clr)

Timeperiod 12months 13days 8days

Impressions 78,560 632,667 318,964

IABcategoryofpublishers

18 16 7

RTBpublishers ∼5.6k/month ∼0.2k ∼0.3k

Filtername Rangeofvalues(type)

Cities Madrid,Barcelona,Valencia,Seville

Timeofday 12am-9am,9am-6pm,6pm-12am

Dayofweek Weekday,Weekend

Typeofdevice Smartphone,Tablet

TypeofOS iOS,Android

Ad-format(smartphone) 320x50,300x250,320x480or480x320

Ad-format(tablet) 728x90,300x250,768x1024or1024x768

Ad-exchange MoPub,OpenX,Rubicon,DoubleClick,PulsePoint

Contentcategoryofpublisher allIABspossible

ComparisonofCPMcostsforthedifferentIABcategoriesinourdatasetandthe2probingadcampaigns.

CostperIABincleartext andencryptedprices

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Medianencryptedpricesalwayshigherthancleartext

Timeshift:Morerecentcleartext pricesarehigherthantheoneslastyear

Howmuchdoadvertiserspaytoreachyou?

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EncryptedVs.Cleartext prices

“It’ssafetoassumethatencryptedpricesfollowthesamedistributionwithcleartext prices.”

pricedistributionofencryptedprices(A1):→distinctlydifferent→about1.7xhighermedianvaluethancleartext prices(A2)

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Comparisonofpricedistributionsbetweencleartext andencrypted,fordifferenttimeperiodsanddatasets(Dvs.A1andA2).

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Howmuchdoadvertiserspaytoreachyou?

• Cumulativecostfromencryptedprices:cannotsurpasscleartext (stilldominant).

• someusersmorecostlythanothers

• medianusercosts25CPM(73%oftheuserscost<100CPM)

• 2%ofuserscost10-100× moretothead-ecosystemthantheaverageuser!

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CumulativeCPMpaidperuserinouryear-longdataset(2015)

0%10%20%30%40%50%60%70%80%90%

100%

10-2 10-1 100 101 102 103 104

CD

F

Cumulative user cost (CPM)

cleartextcleartext (time corr.)

est. encryptedtotal

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Insummary…

• Methodologytomeasurecostofadvertisersperindividual user:• Atrealtimeandonuser’ssidebasedontheuser’sprofile

• Leveragead-auctionsandRTB’spricenotificationchannel

• Constructamodeltoestimateencryptedpricesusingasfeaturesauction’smetadataanduser’sleakedinfo.(>82%accuracy)

• Ourmethodologyistestedusingayear-longdatasetof1600realmobileusers

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Takeaways

üEncryptedpricesare1.7xhigherthancleartext

üMedianusercosts25CPMperyear

üTakingintoaccountseveraldifferentfactors:(HTTP+HTTPS,managementandintermediariescosts,mobile+desktop traffic)

→overalluserad-costintherangeof0.54-6.85€peryear→ cheaper than most users think (10sof euros*)

*Your browsingbehavior forabigmac:Economics of personal information online.WWW’13

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