cross channel-attribution-modeling-in-action

by Kaylan Malm, Manager, Advanced Analytics

with Alan Gee, Manager, Business Intelligence

August 2009

iCrossing Capabilities report:

Cross-Channel attribution Modeling in aCtion

eXeCutiVe suMMarY

Many brands use a last-click attribution model for their marketing efforts online because they do not

know that they have other options. iCrossing has successfully integrated data from several sources,

created a display visualization dashboard using the iCrossing Marketing Platform that allows clients

to see what their consumers are doing before they convert, and has created a user interface that

provides KPIs in a manner that helps answer questions and allows for data to be downloaded for

further analysis.

table oF Contents

2 Background: Brands are vastly underutilizing an ocean of cross -channel attribution data

Accurate cross-channel attribution models allow marketers to create holistic online strategies

Multi-channel attribution research is actionable

3 Mining a Wealth of Information: Data aggregation and integration

Dataset includes conversion channel, site visits prior to conversion (“assists”) and display impressions

Study focused on one client

3 Making Sense of it All: Data visualization

Data timeframe includes conversions from September 2008 – December 2009

Consumersmayfindwebsitesthroughsearchordisplay,butwillreturnthroughareferringortypedinURL

ReportshowsresultsfromConversionFunnel,KeywordFunnel,ConversionMix,ChannelConversionMix,

SourceConversionMixandVisitConversionMix

10 Conclusion: Cross-channel attribution model dashboards successfully integrate data from several sources

Clients can see what consumers are doing before conversion

The display process methodology allows clients to test models that are most appropriate for their businesses

AUGUST 2009Cross-Channel attribution Modeling in aCtion

2© ICroSSInG. ALL rIGHTS rESErVED.

background: brands are Vastly underutilizing an ocean of Cross -Channel attribution dataThe importanceofcross-channeltrackinginthedigitalandinteractivespaceisanexhaustedtopic;whatmarketersarenow

appropriately focusing on is how to implement and analyze cross-channel tracking to correctly attribute conversion credit.

By creating attribution models that more clearly and accurately depict the role of each channel and visit to a site prior to a

conversion, marketers are creating the framework for creating holistic online strategies. The challenge with these models is

not motivating the need, but understanding how to collect and integrate data across channels in a meaningful way that can be

visualized and analyzed. This paper demonstrates how iCrossing’s business intelligence analysts are making this goal a reality

for our clients and the doors that get opened when this type of data set can be gathered.

“Togainefficiencyanddeeperunderstandingofcampaigneffectiveness,marketersmustimplementattributionmeasurement

via click-path tracking, data mining, and predictive modeling.” (“Search and Attribution” November 2008). Most marketers

understand the importance of cross-channel tracking, but most don’t even know where to begin to start putting the cross-

channel dataset together. Most companies still use a last-touch conversion model attributing all conversion credit to the site

visitwhentheconversiontakesplace,whileasmallgroupreliesonthefirst-touchconversionmodelofattributingallthecredit

tothefirstcustomervisittothesiteregardlessofthechannelthroughwiththeconversiontookplace.Bothofthesemethods

areflawedandmostmarketersknowit,theyjustdon’tknowhowtofixit.“Searchmarketersthatassign100percentconversion

value to the so-called last click leading to a conversion often unfairly remove much of the brand value in their display ads and

overemphasize the value of keywords that immediately precede a purchase or lead.” (“Search and Attribution” November

2008).Whilethisistrueforsearch,mostoftheresearchonthistopicisflawedaswellbynotconsideringconversionsfrom

referringURLsanddirectloads,butratherbyfocusingonlyonmediachannels.“Thelast-clickmodelissuchaproblemthat

one-fifthofadvertisersrelyongutfeelingwhenevaluatingthesuccessofbrandcampaignsonline.”(“Transitioningfromthe

Last-ClickModel”July2008).Thisiscorrectedbyusingafirsttouchconversionmodel,butthismethodalsohasitschallenges

for the same reason of not capturing the entire picture. When data fails to answer the entire question, markets fall back onto

themeasurementtoolofcomfort–theirgut.Butinanagewherewehaveaccesstosomuchdata,wejustneedtolearntouse

data in a smarter way.

According toForrester’s recently released“AFramework forMulticampaignAttributionMeasurement” (February2009), “Of

275 Web site decision-makers surveyed in 2008, a full 52 percent agree that attribution would enable them to spend marketing

dollarsmoreeffectively. Yetonly31percentareactivelyusingattribution today,even though thisconcept isnotnew for

marketers,whohavelongsinceappropriatedcredittomarketingendeavorsindubiousways.”Forrester’sresearchpointsout

thatthe31percentwhosaytheyarecurrentlyusingattributiontodaylikelyhavedifferingdefinitionsofwhatmulti-campaign

attribution is and we suspect most aren’t using it to its full ability. “Cross-channel management allows coordination of all

marketing initiatives: messaging and creative development, media buying, and analytics that allow marketers to measure the

influenceofseeminglydisparatecampaignsoneachother.”(“SearchandAttribution”November2008).

The problem is that marketing analytics tools on the market are specialized based on the channel and purpose, often aggregating

information in away thatmakes it difficult tomatch recordsacross systems. According to JuniperResearch, aForrester

Company, “In reality, the technologyandbenchmarks toachieveaccurateattributionare in theearly stages.” (July2008).

Forresteroutlinesthefollowingproblemsclientsencounterwhentryingtogatherthisdata:

+ Extendedsalescyclesmasktheimpactoffirstclicks

+ Independent tracking systems result in fuzzy math that doesn’t add up

+ Search looks heroic, but advertising really provides lift

Advertising such as display media is not the only channel getting let down by last-click conversion tracking, but that conversions

creditedtoreferringURLsanddirectloadtrafficwillgiveuppartialcredittobothmediaandsearchchannels.

iCrossing’smulti-channelattributionresearchisnotconceptual,itisactionable.Oursolutionispresentedindetailinthefollowing

pagesandwasbuiltusingclientdatatomeetspecificobjectives.Wefocusedontheprocessbecausefindingsareuniqueto

every client and therefore should not be generalized. The important task at hand is to identify how to arrive at that solution.

AUGUST 2009



Mining a Wealth of information: data aggregation and integrationThe most daunting task of multi-channel campaign tracking is gathering the appropriate data set for visualization and analysis.

UsingInterest2Action(I2A),asiteanalyticstoolproprietarytoiCrossingtocollectallofthecustomervisitsdata,wekeptthe

dataintegrationtoaminimum.I2Ausesasite-sidepixeltocollectcross-channelvisitsandstoresdataforthelifetimeofthe

cookie, a characteristic that is helpful for clients with longer purchase cycles. The dataset provided by I2A includes: channel,

referringURL,timestamp,keywordsearchedandengineforallsearchvisits,adcampaignandsizeforalldisplaymediavisits,

type of conversion, and revenue. While I2A captures all site visits, for the purpose of this research we looked at the conversion

channelanduptosixsitevisitspriortotheconversionwhichwerefertoas“assists.”I2Aalsocollectsdataondirectloadsand

referringURLconversions,twochannelsthatareoftenignoredbythesolutionsproposedbymediachanneltools.

The only data missing from the I2A dataset was display impressions, an important factor when testing the hypothesis that

display media is often under credited when it comes to conversion tracking. Partnering with Atlas, we pulled cookie-level

impressiondataafterpassingauniqueidentifierbetweenI2AandAtlasduringthedisplaycampaign.Matchingthisdataback

totheI2Aconversionfile,weaddeddisplayimpressionsintothesitevisitsandconversionsdata,creatingadatasetthatthen

told the entire conversion story. The concepts presented below and the data shown are for one particular client, but the method

of data collection and analysis presented are true of all clients using I2A, and with some hard work and data integration these

could be gathered through many other Web analytics tools. iCrossing’s control over the I2A tool has helped to streamline the

process for clients using our proprietary tools.

Making sense of it all: data VisualizationAfter the cross-channel dataset is collected, we set out to aggregate and visualize the data. The trick to cross-channel

reporting istoonlyaggregatethedataafterallthechannelsareincorporated;aggregatingbeforeeachchannelisaddedleaves

the story incomplete. The amount of data for most clients is overwhelming, but iCrossing’s business intelligence team and the

iCrossingMarketingPlatformaretheperfectteamtotakeonthechallengeandspecializeindataintegrationanddisplay.Using

the iCrossing Marketing Platform, we created a standard user interface for multi-channel marketing attribution and focused the

UIonhelpingusanswerthefollowingquestions:

+ How do we attribute credit to assists?

+ What is the true marketing attribution across channels? How is it different from traditional last-click attribution?

+ Is display media “assisting” other media channels?

+ IsdirectloadandreferringURLtraffictakingconversioncreditawayfrommediaandsearchchannels?

+ Doesthesearchkeywordfunnelshowsearchersgoingfromgeneraltomorespecificbrandkeywordswhentheyarecloser

to converting?

+ Do customers that see display ads more frequently convert on branded keywords?

TheiCrossingsolutionisfocusedondataintegrationandvisualizationtoanalyzethecustomerjourneyandattributionmodels

thatwillbeuniquetoeachclient.WedoagreewithForresterthatanattributionmodelshouldaddressrecency,frequency,and

timeonsite(“AFrameworkforMulticampaignAttributionMeasurement”February2009).

Thedashboardconsistsoffourdatatabsincluding:Attribution,ConversionFunnel,KeywordFunnelandConversionMix.



ATTrIBUTIon (FIGUrE 1)

TheAttribution tabof theCross-ChannelAttributionModelingdashboard (Figure1) isdesigned to show theConversionsby

Attribution, as well as the Conversions (+/-) Assists. The reports are described within the dashboard by hovering over the (?) icon.

The Conversions by Attribution is the display of Conversions using the client’s attribution model rather than the commonly-used

LastClickAttribution.Inthiscase,wegaveequalcredittoanyvisittothesitepriortotheconversion,andthenaddedthatcredit

upacrosseachconversiontogetthenewattributionmodel.Formostclients,themodelwouldbemuchmorecomplicatedand

involveamixtureofrecency,frequency,timeonsiteandchannelweighting,buttheimportantfeatureisthatclientscanredesign

theattributionmodelanddisplaytheirnewattribution,notjustlastclickattribution.

The Conversions (+/-) Assists report shows the difference between the client’s selected attribution model and the traditional last

clickmodel.Thisshowsnetchangefromthelast-clicktofullconversionattribution.Inthisexample,whenwegiveequalcredit

to all visits (not weighting based on recency, time on site or channel) and compare that to the model where only the last click

receivesfullcredit,thenweseethatnaturalsearch(2.9percent),paidsearch(1.3percent)anddirectload(1percent)receivemore

creditthantheyarecurrentlyreceivingwiththelastclickmodel,whileReferringURLlost5.2percentofitsattributioncredit.This

supportsthegenerallyacceptedideathatconsumersmayoriginallyfindasitethroughsearchordisplay,butwillcomebacktothe

sitelaterbyeithertypingtheURLorvisitingthesitethroughareferringURLwhentheyeventuallyconvert.TheConversion(+/-)

Assists report gives credits to all the other visits leading up to the action. By looking at conversions in this manner, we predict that

most clients will see that their natural search, paid search, and display media channels deserve more credit for conversion than

they are currently receiving using a last click model.

Oneachdashboardtabyoucanalsoselectthetimeframe.Inthiscase,thetimecontrolsthemonthoftheconversionandwill

pullallsubsequentvisitstothesite,eveniftheyoccurredbeforethebeginningofthemonth.Afiltercanbeaddedforclients

who want to look at only at visits within a particular timeframe of the conversion. Also, below each graph the table of raw data is

providedandcanbeexportedtoExcelforadditionalanalysisifneeded.Youwillalsonoticeonallthedashboardsthatthereisa

Conclusionssectionthatcanbeeditedbybusinessanalyststoprovidekeyfindingsandinsights.

Data Source: Interest2Action, Merchantize; Data Timeframe: Conversions that took place from September 2008-December 2009, adjustable through the

provided drop down menus.

AUGUST 2009



ConVErSIon FUnnEL (FIGUrE 2)

TheConversionFunneltabprovidesamoregranularviewofthejourneythatcustomerstakebeforetheconversion.Thedefault

pageshowsthetopfivemostcommonconversionfunnelswheretheconversionchannelis‘allchannels,’butfromtheConversion

Channel drop down, you can choose Natural Search, Paid Search, Direct Load, Display, Referring URL, and SocialMedia.

Choosing another channel will show only conversion funnels that converted on the chosen channel and are useful to service line

experts.Inallcases,thevisits’pathsstartatthetopshowingthefirstvisittothesitewithintheconversiontimeframe,andthelast

visitthatresultedintheconversionisshownatthebottomofthefunnel.Usingthe‘1st’Channelasanexample,thismeansthat

themostcommonconversionpathforthisclientwasvisitorswhofirstcamethroughDirectLoad,thenlatervisitedthesitethrough

thesamechannel,DirectLoad.Theyrepresent17.5percentoftotalconversionsduringthetimeframe(775total),andonaverage

ittookthem7daystoconvertbetweentheirfirstvisitandtheirconversion.Moreinterestingarethe2ndand3rdfunnelsthatshow

thatNaturalSearchorPaidSearchisthechannelvisitedfirst,buttheconversionsactuallycamethroughReferringURLs,together

those represent a total of 23.6 percent of total conversions.

Atthebottomofthedashboard,youcanalsochoosetoseeAllConversionFunnelsifyouwanttoseemorethanthetop5,and

alsoswitchthefunnelstoseetheFirstTouchanalysisforclientsthatuseaFirstTouchattributionmodel.





TheKeywordFunneltab(Figure3) issimilartotheConversionFunneltabexcept it focusesonlyonconversionsthatcame

from search. In the drop down menu you can also choose to look at conversions from only Brand or Non-Brand search. The

keywords for both Natural Search and Paid Search were then labeled as brand and non-brand to show keyword cross-over.

Forthisclienttherewaslittlecross-overbetweenbrandedandnon-brandedsearchorbetweensearchandotherchannels.The

PercentageofConversions,TotalConversions,andAveragedaysinfunnelmetricsarealsoprovidedfortheKeywordFunnel.

ConVersion MiX (Figures 4,5,6)

ByselectingtheConversionMixtab,youcanseethemostgranulardataprovidedinthisdashboard.Inthedropdownyoucan

selecttheConversionTypeasChannel,Source,orVisittoseethreeseparatereports.



KEyworD FUnnEL (FIGUrE 3)

AUGUST 2009



CHAnnEL ConVErSIon MIX (FIGUrE 4)



TheConversionsbyChannel(Figure4)reportshowsthenumberofchannelsusedbeforeaconversion.Forexample,ifauser

comestothesiteontheirfirstvisitfromdisplay,thenfrompaidsearch,andfinallyconvertsthroughareferringURL,thatisthree

totalchannels.Ontheotherhand,ifavisitorcomesthreetimesallthroughnaturalsearch,thatisonlyonechannel.Inthis

example,thefactthatonechannelrepresents85percentoftotalconversionsshowsthatconsumersforthisbrandareunlikely

toswitchfromonechanneltoanotherduringtheirjourneytoaneventualconversion.Thissamemetricistrendedovertimein

theConversionsbychanneltimelineandtherawdataisprovidedatthebottomoftheKPIpanel.



SoUrCE ConVErSIon MIX (FIGUrE 5)

TheSourceConversionType(Figure5)reportintheConversionMixtabwillloadareportverysimilartotheChannelselection,

but instead of showing the number of channels, it shows the channel that lead to the conversion. This is the traditional last touch

attribution model and is provided for clients for comparison purposes.



AUGUST 2009



ThelastreportprovidedintheiCrossingCross-ChannelAttributionModelingdashboardistheVisitConversionType(Figure6).This

reportshowsthenumberofvisitspriortoaconversion.Forthisexample,morethan75percentofconversionshappenedonthe

firstvisit,butthereareonepercentofcustomersthatvisitthesitemorethanseventimesbeforeconverting.UsingtheConversion

Funneltab,userscanexplorethesefunnelsmoretodeterminethechannelsthesefrequentlyvisitingconsumersareusing.

VISIT ConVErSIon MIX (FIGUrE 6)





ContaCtFindoutmoreatwww.icrossing.com

Call us toll-free at 866.620.3780

Followusatwww.twitter.com/icrossing

Become a fan at www.facebook.com/icrossing

reFerenCesAndrews,Evan.“SearchandAttribution:MaximizingROIinaTightEconomy.”JupiterResearch,aForrester

ResearchCompany.November24,2008.

Lovett,John.“AFrameworkForMulticampaignAttributionMeasurement.”ForresterResearch.February19,

2009.

Riley,Emily.“Attribution:TransitioningfromtheLast-ClickModel.”JupiterResearch,aForresterResearch

Company.July28,2008.

ConclusionBy building Cross-Channel Attribution Modeling dashboards for our clients, iCrossing has successfully integrated data from

several sources, created a display visualization dashboard using the iCrossing Marketing Platform, allowing clients to see what

theirconsumersaredoingbefore theyconvert,andhascreatedauser interfacethatprovidesKPIs inamanner thathelps

answerquestionsandallows fordata tobedownloaded for further analysis. Our transparency in thisprocess shows the

industrythatwearecreatingactionablesolutionstoclientneedsandprovidingthosesolutions.Wearen’tjusttalkingaboutthe

importanceofcross-channelattribution;wearedoingitbecauseweagreewithForresterthat“Agenciesandserviceproviders

must provide increasingly approachable solutions for attribution to become the de facto measurement model.” (“Transitioning

fromtheLast-ClickModel”2008).

The data integration and display process methodology presented above allows clients to appropriately attribute credit to assists

fromotherchannelsandeventestseveralmodelstodeterminetheonethatismostappropriatefortheirbusiness.Oncethe

attributionisdetermined,themodelscanbecomparedtothetraditionallastclickmodel.Thiscanhelptoexplainhowdisplay

mediaisassistingotherchannels,andtodeterminehowmuchcreditmediaandsearchchannelsaregivinguptoreferringURL

anddirectloadtraffic.Also,bylookingatkeywordbreakouts,clientscanseehowbrandedandnon-brandedsearchterms

fitintotheconversionfunneldifferently,andifseeingdisplayadscausesuserstosearchbrandtermsmorefrequently.Allof

these questions are addressed in industry research, but clients are now asking for, and deserve to see what their customers are

doing before converting on their site. iCrossing’s approach now makes that possible.

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