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iCrossing Capabilities Report - Cross-Channel Attribution Modeling in Action Many brands use a last-click attribution model for their marketing efforts online because they do not know that they have other options...
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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
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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.
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Cross-Channel attribution Modeling in aCtion
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.
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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.
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Cross-Channel attribution Modeling in aCtion
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.
Data Source: Interest2Action, Merchantize; Data Timeframe: Conversions that took place from September 2008-December 2009, adjustable through the
provided drop down menus.
AUGUST 2009Cross-Channel attribution Modeling in aCtion
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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.
Data Source: Interest2Action, Merchantize; Data Timeframe: Conversions that took place from September 2008-December 2009, adjustable through the
provided drop down menus.
KEyworD FUnnEL (FIGUrE 3)
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Cross-Channel attribution Modeling in aCtion
CHAnnEL ConVErSIon MIX (FIGUrE 4)
Data Source: Interest2Action, Merchantize; Data Timeframe: Conversions that took place from September 2008-December 2009, adjustable through the
provided drop down menus.
TheConversionsbyChannel(Figure4)reportshowsthenumberofchannelsusedbeforeaconversion.Forexample,ifauser
comestothesiteontheirfirstvisitfromdisplay,thenfrompaidsearch,andfinallyconvertsthroughareferringURL,thatisthree
totalchannels.Ontheotherhand,ifavisitorcomesthreetimesallthroughnaturalsearch,thatisonlyonechannel.Inthis
example,thefactthatonechannelrepresents85percentoftotalconversionsshowsthatconsumersforthisbrandareunlikely
toswitchfromonechanneltoanotherduringtheirjourneytoaneventualconversion.Thissamemetricistrendedovertimein
theConversionsbychanneltimelineandtherawdataisprovidedatthebottomoftheKPIpanel.
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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.
Data Source: Interest2Action, Merchantize; Data Timeframe: Conversions that took place from September 2008-December 2009, adjustable through the
provided drop down menus.
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Cross-Channel attribution Modeling in aCtion
ThelastreportprovidedintheiCrossingCross-ChannelAttributionModelingdashboardistheVisitConversionType(Figure6).This
reportshowsthenumberofvisitspriortoaconversion.Forthisexample,morethan75percentofconversionshappenedonthe
firstvisit,butthereareonepercentofcustomersthatvisitthesitemorethanseventimesbeforeconverting.UsingtheConversion
Funneltab,userscanexplorethesefunnelsmoretodeterminethechannelsthesefrequentlyvisitingconsumersareusing.
VISIT ConVErSIon MIX (FIGUrE 6)
Data Source: Interest2Action, Merchantize; Data Timeframe: Conversions that took place from September 2008-December 2009, adjustable through the
provided drop down menus.
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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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