2014 internet retail audit report

8/18/2019 2014 Internet Retail Audit Report

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HARLEY DAVIDSON | CASE STUDY

See how Internet Retailer Top 1-100 and

501-600 ranked companies manage theirdigital marketing technologies.

Internet RETAIL

AUDIT





2DIGITAL MARKETING TECHNOLOGY REPORT

Execuive SummaryAs he markeing echnology landscape coninues oexpand, markeers have become burdened wih managingcomplex implemenaions o many ools across manydigial properies. When daa qualiy suffers, he poenialreurn on each echnology is damaged.

Daa qualiy managemen is a growing responsibiliy ordigial markeers, and especially web analyss. A newclass o daa qualiy managemen ools, such as hose

offered by ObservePoin, aim o sysemaically collecagging daa and proacively deec ag deploymen errorsha negaively impac downsream digial markeingaciviies.

ObservePoin applied is audiing echnology o hewebsies o companies lised by Inerne Reailer in he2014 Inerne Reailer Top 500 and he 2013 InerneReailer Second 500. Sies ranked 1-100 were comparedo hose ranked 501-600 o see wha he op sies dodifferenly. We ound ha in many areas, digial markeersall suffer rom he same problems.

DATA QUALITY IN DIGITAL MARKETING

Daa qualiy as an aerhough in digial markeing iswillul ignorance. While many digial markeers undersandha daa qualiy is requenly degraded hrough rouinewebsie updaes, only he mos maure o digialmarkeing praciioners apply processeses o moniordaa qualiy. Beore echnologies such as auomaedag audiing and ag managemen were available, digial

123,556 34% 56% 1,395Pages Audied Sies Using TMS Average Daa Inflaion Technologies Idenifi

markeers were le o manual and haphazard mehods omanaging ags. This is no longer he case.

Leading brands and digial agencies are now leveragingdaa qualiy managemen sysems o creae agdaabases and manage deploymens. This providesquanifiable insigh ino daa qualiy, and proecs he ROIpoenial o all digial markeing ools.

METHODOLOGY

ObservePoin sampled pages on 401 websies lised1-100 in he 2014 Inerne Reailer Top 500, and 501-600 inhe 2013 Inerne Reailer Second 500 daabases. UsingObservePoin’s paened audiing plaorm, each page andag was measured, and hese merics were compiled romevery sie o creae perormance indexes. This exclusivedaa gives unprecedened insigh ino he world’s opInerne reailers’ digial markeing echnology sacks.

This repor shines a ligh on he mos common agdeploymen issues experienced across (even) he mossuccessul Inerne reailers, as ranked by Inerne

Reailer. We also compare he op 100 reailers o hoseranked beween 500-600 o undersand he differencesand similariies beween how op reailers and 500-600 ranked companies manage heir digial markeing

DIGITAL MARKETI NG TECHNOLOGY REPORT

Internet Retailer 1-100 Vs 501-600






TOP 100 COMPANIES DEPLOY 26% MORE

MARKETING TECHNOLOGIES THAN THESECOND 500FindingsIn our audi o over 123,000 pages spanning some 400websies owned by Inerne Reailer ranked companies,we discovered which ools are he mos popular amonghe wo groups, which companies use more han oneweb analyics sysem, and which companies use ag

managemen sysems. We also measured he oal andaverage number o echnologies deployed on websies.

Our audis revealed several daa qualiy issues haseemed o surace over and over again. Among heseissues are incomplee ag deploymens, or he insanceo criical ags missing rom websie pages; daa inflaion,or he insance o criical ags being placed mulipleimes on pages; daa consisency problems ha arecaused by iming; ordering issues ha cause ags ofire a inconsisen inervals; long page load imes; andJavaScrip errors.

Finally, we analyzed he deploymen and populariy o agmanagemen sysems. We ranked he populariy o TMS’and deermined he effec ha ag managemen sysemsare having on daa qualiy.

DEPLOYED DIGITAL MARKETING TECHNOLOGIES

On average, 1-100 ranked companies deploy 26% moremarkeing echnologies on heir websies han 501-600ranked companies do. Markeing echnologies commonlyconsis o a primary web analyics sysem o record and asecondary web analyics ool, ags or audience argeingand measuremen, search markeing, adverising,

personalizaion, recommendaions, and Tag Managemen.

In he op 100, he mos ags on one sie is 39.

29.7% o op 100 websies deploy a leas 20 ags and 15%deploy less han 6 ags, and 8 websies deploy only oneag. On average, op 100 websies deploys 13-14 ags.

In sies o companies ranked 501-600, he mos ags onone sie is 29.

10% o sies deploy a leas 20 ags, 26% deploy less han 6ags, and 3 websies deploy only one ag. On average, 501-600 ranked sies deploy 10-11 ags.

“In the top 100, the most tags onone site is 39.”

PRIMARY WEB ANALYTICS SYSTEM OF RECORD

The primary web analyics sysem o record is he ool awebsie’s analyss use mos oen or rouine reporingand daa analysis.

When more han one web analyics ool is ound onhe websie, he primary analyics ool is deerminedempirically by comparing which ool is presen on a

higher percenage o pages. When Google Analyics isound presen on he sie along wih anoher ool, GoogleAnalyics is deemed as he secondary web analyics ool.

54% o op 100 websies use Adobe Analyics as a primaryweb analyics ool, 22% use eiher Google Analyics orGoogle Universal Analyics, 16% use IBM Coremerics and8% use eiher Quancas or comScore.

79% o 501-600 websies use eiher Google Analyics or

Number of Tags

P e r c e

n t o f W e b S i t e s

5 10 15 20 25 30

10

20

30

Tags Deployed on Web SitesRanked 1-100 vs 501-600






47% OF TOP RETAILERS USE MORE THAN

ONE WEB ANALYTICS TOOLGoogle Universal Analyics as a primary web analyicsool, 9% use Adobe Analyics and 12% use eiher IBMCoremerics, Quancas, Webrends or Yahoo WebAnalyics.

USAGE OF MORE THAN ONE WEB ANALYTICS TOOL

36% o op 100 websies use Google Analyics or GoogleUniversal Analyics as a secondary web analyics ool.IBM Analyics is used 11% o he ime.

501-600 sies use a secondary ool much less oen, wih20% using Google Analyics.

In he op 100, 51% o Adobe Analyics deploymensare accompanied by a secondary analyics ool - eiherGoogle Analyics, Google Universal Analyics, IBMAnalyics or Webrends.

Surprisingly, every ime a 501-600 sie has deployedAdobe Analyics, Google Analyics is also deployed.

THE NUTS AND BOLTS OF TAG DEPLOYMENT

In order or ag-dependen ools, such as web analyics, ocollec and repor he mos accurae daa on each page,our condiions mus be me.

Firs, each page mus render one (and only one) insanceo a given ag1.

Second, each ag mus be se up o send daa o hecorrec reporing accoun, someimes reerred o invendor-specific language such as UA number, Account orReport Suite ID.

Third, variable parameers and values mus be configuredcorrecly. Pariculars o variable configuraion varies byvendor, bu generally he page name, namespace, andcusom variable daa mus be configured.

Fourh, ags mus execue wihou error and wihconsisen iming across all browsers, neworkconnecion ypes, devices, and geographic locaions.

DATA QUALITY TRAPS

Comparing repors rom muliple web analyics plaorms

has become a common pracice. 47% o 1-100 sies and26% o 501-600 sies have deployed a leas wo webanalyics ools. Perceived benefis o his acic includehaving a “back-up” daa se, confirming daa in one oolwih daa rom a second ool, providing access o morepeople, ec.

In heory, he benefis o measuring wice seem almosinuiive, bu when he daa qualiy o hese sysems isunquanified, he risks sar o ouweigh he benefis.

Users who compare digial markeing daa rom muliplesysems end o have rusraing experiences whenheir merics don’ mach up. Users should be awareha inheren differences in he meaning and collecionmehod o common merics, such as page views, visis,and sessions, vary by vendor.

Collecion models or definiions or hese merics are nosandardized. Each vendor has unique daa collecionmechanisms and daa rom differen ools should no beexpeced o mach-up.

Every website has issues thatnegatively impact data quality.”

Recogniion o hese differences does no resolve allcross-vendor reporing issues, however. In our analysis,every websie has echnical issues ha negaively impacdaa qualiy.

Our daa indicaes ha in any group o 30 pages, a leasone page will be missing a ag rom one web analyicssysem. Wihou a ag, no daa is colleced. The merics

in his repor each have an impac on daa qualiy. Uli-maely, comparing wo flawed ses o daa in he pursuio gaining clariy is unrewarding.

Comparing wo flawed daa sources is counerproducive.Insead o removing bias and providing acs or decision-making, flawed daa inroduces new bias and supporsalse conclusions. I’s much beter o have one se onearly perec daa han wo ses o flawed daa.

Companies ha require srong daa alignmen across

1. Some digial markeing ools, such as adverising and social media widges may be deployed muliple imes on one page.






heir enire markeing echnology sack are advisedo deploy a daa layer hrough an enerprise-class agmanagemen sysem.

Web Analytics Tag Deployment Rates

When i comes o daa qualiy, comprehensive webanalyics ag deploymen is paramoun. Tag presence ishe percenage o pages in an enire audi which havea given ool’s ag. For example, i 450 o 500 pages in anaudi have an Adobe Analyics ag, he deploymen rae is90%. For a web analyics sysem o collec any daa on aparicuar page, he page mus have a ag.

Placing ags and mainaining ag deploymen, wheheror web analyics, esing, personalizaion, or agmanagemen, is a op challenge or digial markeers.


We measured he number o working ags or eachvendor on each page o every websie. Tag presence -he insances o working ags on pages - coninues obe a challenge. Top 100 and 501-600 sies have more incommon here han no.

For companies lised 1-100, an average o 2.8% o pageshave no ag or heir primary web analyics sysem orecord. 7% o hese websies are missing heir primaryweb analyics ag on more han 10% o pages. Onewebsie, belonging o a big-box elecronics reailer, hasdeployed Adobe Analyics on ewer han 75% o pages.

501-600 ranked websies have only slighly betercoverage – missing ags on jus under 2% o pages. 2% o501-600 websies are missing he primary web analyicsag on more han 10% o pages. One paricular websiehas Google Analyics ags on ewer han 75% o pages.

SECONDARY WEB ANALYTICS TOOL

Secondary web analyics ools are plagued by he sameproblem. In he op 100 sies, secondary analyics oolsare missing on 2.3% o pages; in he 501-600, ags aremissing on 1.5% o pages.

Data Inflation

Daa Inflaion occurs when page view daa is double-couned (or more) due o he presence o more hanone web analyics ag on a single page. As each ag

fires, racking code is sen o he vendor’s server. Whenmore han one ag or a given vendor fires on a singlepage, some or all o he daa is duplicaed wih each

subsequen ag fire.

Daa inflaion can also be caused by an incorrecdeploymen o a ag managemen sysem. Upondeploymen o TMS, i individual ags are no manuallyremoved rom he page code, ags end up firing wice -once rom he remaining ags, and once rom he TMS.

Many web analyics sysems have variable coss or servercalls. Muliple server calls caused by duplicaed agsincreases he cos o ownership while simulaneouslydegrading daa qualiy. In many ways, daa inflaion isworse han missing daa because no only is he daaflawed, bu also here is an increase in server calls and,consequenly, ool coss as well.


Top 1-100 sies have an average o 146% daa inflaion. 3%o hese websies had beween 1000% and 1800% inflaionin heir web analyics sysem o record. 22% o websieshave over 100% daa inflaion. 51% o op 100 sies had lesshan 10% daa inflaion, and he majoriy o hose had noinflaion.

Abou hal o websies wih daa inflaion issues use a agmanagemen sysem.

501-600 ranked websies have an average o 42% daainflaion. 15% o sies have inflaion greaer han 100%, buhe sie wih he mos inflaion has 400%. 66% o 501-600ranked sies have less han 10% inflaion, a majoriy ohose having no inflaion a all.

Percent of Pages Tagged withPrimary Web Analytics System of Record

P e r c e n t o f W e b S i t e s

75 80 85 90 95 100

1

10

100

Tag Presence on Web SitesRanked 1-100 vs 501-600






“Top 1-100 sites have an average

of 146% inflation. 3% of these

websites had between 1000%

and 1800% inflation in their web

analytics system of record.”

SECONDARY WEB ANALYTICS TOOL

Top 100 ranked sies wih a secondary web analyicsool (abou 48% o sies), have an average o 190% daainflaion.

23% o 501-600 ranked sies wih a secondary webanalyics ool have 48% inflaion.

Complexity

Complexiy in digial markeing echnology deploymenis measured by he number o user-configured variableseach ag has. Ou o he box, each vendor uses deaul

variables such as page name, domain, screen size, colordeph, characer se, ec.

The real power in a premium web analyics ool is isabiliy o capure cusom variables, which are cusomdaa poins ha are imporan o business objecives.

Complexiy is expressed as he average number ovariables observed. Purely ou-o-he-box deploymensaverage 20 variables.

IN TOP 1-100 SITES

Top 100 sies use 79 variables on average, whichis 49 more variables han a ypical ou-o-he-boximplemenaion. One sie uses 2041 variables, and 21%o op 100 sies are using more han 100 variables. 17% o

sies are using very ew cusom variables. All bu a handuo companies who are using a premium web analyicssysems are using cusom variables.

IN 501-600 SITES

In conras, 501-600-ranked sies use an average o 24variables and he mos complex deploymen uses 89variables. Abou 50% o sies use more han he sandardvariables.

Consistency

Consisency measures he posiion in he neworksack in which server requess (such as calls o digialmarkeing echnology servers) are iniiaed.

Engineers have he abiliy o conrol he iming o variousags. In a TMS environmen, his can be se up wihin hesysem. In a non-TMS environmen, placing ags in heheader, body, or ooer or a page affecs he iming.

Ideally, he firing posiion o all ags belonging o a vendorwould fire eiher in he op or botom o he neworksack. This iming promoes daa collecion on each pagea a consisen poin in ime during each page view. When

i can be proven ha racking code is execued wihin hefirs .25 seconds o a page load, i can be assumed hahe daa is more consisen and complee han i he agsfire a inconsisen imes beween pages (some quicklyand some aer 3 seconds) .

Some vendors recommend placing ags in he ooer oa page. This helps visible elemens o render firs, whichopimizes user experience. However, on slow loadingpages, visiors may navigae away rom he page beore






“50% of top 100 company

sites have deployed a tag

management system.”

he ag is fired. Sill, consisenly placing ags on hebotom o all pages is beter han scatering abou hepage.

In our audis, we measured he percenage o ags whichfire in he op, botom, and middle hird o he neworksack.

PRIMARY WEB ANALYTICS SYSTEM OF RECORD IN

TOP 1-100 SITES

On average, 61% o image requess happen in he botomhird o pages. This resuls in arificially low page views onpages wih long load imes.

“23% of websites have an even

distribution of tag placement...

This produces the least consistent

data.”

32% o image requess happen in he middle hirdo pages. Tags in he middle o he page creae lessconsisen daa, since pages vary wildly in oal load ime.

7% o image requess execue in he op hird o pages.This ends o produce he highes coun rae or pageviews, even hough user experience may be slighlyimpaced by invisible elemens loading beore visible

elemens.

23% o websies had an even disribuion o agplacemen across he op, middle, and botom o pages.This produces inconsisen daa because he ags will firea differen imes on each page.

PRIMARY WEB ANALYTICS SYSTEM OF RECORD IN

501-600 SITES

On average, 66% o image requess happen in he botomhird o pages.

28% o image requess happen in he middle hird opages. 5% o image requess execue in he op hird opages. 20% o websies had an even disribuion o ag

placemen across he op, middle, and botom o pages.

Site Performance

Sie perormance impacs he qualiy o digial markeingdaa. For example, JavaScrip errors on pages canpreven he execuion o ags used or web analyics,personalizaion, adverising, ag managemen - inacualiy any ype o ag, beacon, or scrip.

LONG PAGE LOAD TIMES

Sudies have ound ha people begin o be impaien wihpages loading anywhere rom 250ms o 2 seconds aerclicking a link or enering a web address. Significanlylonger load imes affec he way people inerac wih awebsie. When a load ime is oo long, visiors will bouncerom a page more oen, conver less, and reurn lessoen.

When ags are placed oward he botom, he ags are lesslikely o execue because he visior is more likely o leavehe page oo soon.

In top 1-100 Sites

The average page load ime across all op 1-100 Inernereail websies is 3.98 seconds.

The ases 10% o sies’ pages load in an average o1.4 seconds. The slowes 10% o sies’ pages load in anaverage o 8.4 seconds. 2% websies have an average pageload ime o beween 10 and 16 seconds.

Average Page Load Time in Seconds

P e r c e n t o f W e b

S i t e s

3 5 7 9 11+

10

20

30

Load Times on Web Sites

Ranked 1-100 vs 501-600

1






In 501-600 Sites

The average page load ime across all 501-600 websies is4.85 seconds.

The ases 10% o sie’s pages load in an average o1.92 seconds. The slowes 10% o sies’ pages load in an

average o 10.2 seconds. 2% o websies have an averagepage load ime o beween 12 and 17 seconds.

JAVASCRIPT ERRORS

The majoriy o digial markeing echnologies dependon JavaScrip. Since digial markeing echnologies oendo no noiceably change he appearance o he websie.I can be difficul o deec he insance o JavaScriperrors and he effec hose errors have on ags. We, aObservePoin, collec inormaion abou he numbero JavaScrip errors ound on web pages. Here hey areexpressed as a percenage, 0% indicaes no errors; 100%

indicaes ha every page ha was audied had JavaScriperrors.

In Top 1-100 Sites

In he op 100, JavaScrip errors were ound on an averageo 24% o web pages. 11% o sies had JavaScrip errorson more han 90% o pages, 58% o sies had JavaScriperrors on less han 10% o pages.

In 501-600 Sites

In he op 501-600, JavaScrip errors were ound on anaverage o 62% o web pages. 19% o sies had JavaScriperrors on more han 90% o pages, 47% had lessJavaScrip errors on less han 10% o pages.

CONNECTIVITY

When pages ail o load compleely, user experienceis degraded. In urn, his skews digial markeingmeasuremen. In some cases, pages may render visually,bu ags ail o fire due o web server capaciy, neworkerrors, vendor server issues, web browser problems, ec.

When we audi a websie, we atemp o load such pages

muliple imes in order o correcly fire ags. Highernumbers o atemps indicae some sor o neworkconneciviy issue during he ime o our audi. On a sie-by-sie level, ideniying persisen conneciviy issues andracking down heir causes requires muliple ess.

In Top 1-100 Sites

In he op 100 sies, 93.26% o pages loaded on he firs

atemp wihou any issue. 1.11% o pages required asecond atemp o compleely load. 5.63% required hreeatemps.

In 501-600 Sites

In 501-600 sies, 96.14% o pages compleely loaded on he

firs atemp,1.25% o pages required a second atemp ocompleely load and 3.06% required hree atemps.

Tag management systems

50% o op 100 company sies deploy ags hrough a agmanagemen sysem. 501-600 companies lag behind wih18% o sies having a ag managemen sysem in place.

In he op 100, Signal (ormerly BrighTag) has he mosmarkeshare, wih a presence on 16.5% o sies. Tealium,Ensighen, Adobe and Google also have subsanialpresence in he op 100.

In 501-600 sies, Google Tag Manager is he moscommonly used ag manager. Signal and Adobe DynamicTag Manager are deployed on 3% o sies.

Correlation of TMS with Primary Analytics Systems ofRecord

In he op 100 sies, 60% o Adobe Analyics ags aredeployed hrough a TMS.

27% o sies wih Adobe Analyics ag have deployedhrough Signal TMS. 23% deploy hrough an Adobe TMS

(eiher Tag Manager or DTM, ormerly Saellie); 20% useTealium, 17% use Ensighen, 12% use Google Tag Managerand 1% use Qubi OpenTag.

49% o Google Analyics ags are deployed hrough aTMS.

P e r c e n t o f W e b

S i t e s

10

20

Marketshare of Tag Management SystemsRanked 1-100 vs 501-600

(Dynamic TagManager)

(Tag Manager) (BrightTag)






Every additional tag deployed adds

a level of complexity and risk todata quality

63% o sies wih Google Analyics ags have deployedhrough Signal TMS. 13% deploy hrough Google TagManager; 10% deploy hrough Tealium; 2% deploy hroughAdobe DTM (Saellie), and 1% deploy each hroughEnsighen and Adobe TagManager.

“In the top 100 sites, 60% of Adobe

Analytics tags are deployed

through a TMS.”

30% o sies wih IBM Analyics are deployed hrough aTMS. 66% o hose deploymens are hrough Tealium; 26%hrough Signal, and 1% hrough Google Tag Manager.

In 501-600 sies, 76% o ag managemen deploymens

are Google Tag Manager and Google Analyics. AdobeAnalyics and Signal are deployed once hrough Signal,and IBM Analyics is deployed once hrough Adobe DTM(Saellie).

TAG MANAGEMENT DEPLOYMENT RATE

Tag managemen sysems require he deploymen o heirown ag, jus like any oher digial markeing echnology.Tag managemen sysems are only able o manage agson web pages where he TMS ag is deployed.

In op 1-100 sies, TMS ags are missing on 4.1% o pages.

26% o sies which deploy a ag managemen sysem aremissing TMS ags on more han 5% o pages.

Data quality in primary web analytics system of record inTMS vs Non-TMS websites

Top 100 sies ha have a deployed TMS experience anaverage deploymen rae o 97.325% in heir primaryweb analyics sysem o record, and he average TMSdeploymen rae is 95.88% in he group.

This indicaes ha some websies are deploying heirprimary web analyics sysem o record ouside o heirTMS. Wihou he benefi o TMS, websies manage odeploy ags on 97.06% o pages.

Websies wih ag managemen sysems end o have

more cusomized variable racking, wih an average o 178cusom variables, vs. only 76 or non-TMS sies.

AUDIT SCORE

During an audi, each websie is evaluaed and scoredagains ObservePoin’s bes pracices. ObservePoin’sAudi Score considers page load-imes, ag deploymenraes, daa duplicaion raes, JavaScrip errors, and agsynax errors. Higher scores are beter, and perec scoresare rare.

In op 100 sies, he average audi score is 74.26; he

average audi score in 501-600 sies is 81.77.

Alhough he oal number o ags on a sie is no acomponen o he audi score, here is a saisicallysignifican correlaion beween he score and he numbero ags on a sie. Saisically, or every ag added ono asie, he websie’s overall score drops by 0.68 poins.

Each addiional ag ha’s deployed adds a layer ocomplexiy and risk o daa qualiy. Simply pu, his risk isha he new ags will be inegraed incorrecly, conflicwih oher echnologies, increase load ime, sap atenionaway rom mainaining exising echnologies, or have a

number o oher adverse effecs on daa qualiy.

When ags do no fire correcly, he websie’s overall scoresuffers. The ag coun in paricular can explain 24% ohe variaion beween individual websie scores. Wheni comes o he number o ags on a page, less is more.When more is necessary, ag audis can keep hings incheck.



Conribuors

Matthew Todd MillerMathew is a digial markeingechnologis and a subjec mater

exper in digial markeing daaqualiy assurance. He is a requenpresener a indusry evens, andconribues o he exensive bodyo knowledge held by ObservePoin.

He holds an MBA rom he JohnSperling School o Business, and

is a member o he American Markeing Associaion andDigial Analyics Associaion.

Megan Regina ChipmanMegan is a passionae runner andmarkeing wunderkind. She has

a background in saisics, digialanalyics, social media, and conenmarkeing.

Megan is currenly a degreecandidae a he BYU MarrioSchool o Managemen.




ConclusionAuomaed digial markeing echnology audiing, asoffered by ObservePoin, provides daa qualiy analyicso digial markeers. Organizaions which experiencechallenges wih sie perormance, daa inflaion, polluion,

and inconsisency are unable o employ daa-drivendecision-making processes. The adopion o robus daagovernance and qualiy assurance processes, includingappropriae ools such as ag managemen and agaudiing, measurably improves he reliabiliy o digialmarkeing daa.

Websies across he enire specrum o Inerne Reailerrankings suffer rom he same undamenal problems wihheir digial markeing. Pages missing ags, duplicaedags, or privacy problems are presen in each o he op100 and 501-600 ranked websies.

Companies ha show effor o beter manage ags endo are beter, bu ag managemen is no a panacea. Tagaudiing is essenial o ideniying weakenesses in daacollecion.

Abou ObservePoinBased in Provo, Uah, ObservePoin is he leader in webag audiing – a criical pracice in digial markeingechnology managemen. Audiing proecs he ROI odigial markeing echnology and resolves significan

obsacles in markeing daa analysis and daa-drivendecision-making. ObservePoin employs paenedmehods o simulaing nework raffic a a speed andscale unparalleled in he indusry o deec errors indigial markeing echnology deploymens.

For more inormaion abou ObservePoin, visiwww.observepoin.com






APPENDIX

Amazon.com Inc.

Apple Inc.

Saples Inc.

Walmar.com

Sears Holdings Corp.

Libery Ineracive Corp.

Neflix Inc.

Macy’s Inc.

Office Depo Inc.

Dell Inc.CDW Corp.

OfficeMax Inc.

W.W. Grainger Inc.

Cosco Wholesale Corp.

Bes Buy Co. Inc.

The Home Depo Inc.

Newegg Inc.

Targe Corp.

Gap Inc.

Sony Elecronics Inc.

Williams-Sonoma Inc.

Symanec Corp.

Kohl’s Corp.

Nordsrom Inc.L Brands Inc.

HSN Inc.

Google Play

BarnesandNoble.com

Sysemax Inc.

Esy Inc.

Oversock.com Inc.

L.L. Bean Inc.

Visaprin NV

Toys ‘R’ Us Inc.

HP Home & Home Office Sore

Lowe’s Cos. Inc.

J.C. Penney Co. Inc.

Saks DirecAmway

MSC Indusrial Supply

Neiman Marcus

Fanaics Inc.

Walgreen Co.

Groupon Goods

Wayair LLC

Rakuen.com Shopping

PC Connecion Inc.

Urban Oufiters Inc.

Avon Producs Inc.

Shuterfly Inc.

Cabela’s Inc.

Abercrombie & Fich Co.

Foo Locker Inc.

GameSop Corp.

zulily Inc.

Musician’s Friend Inc.

Marke America

Ralph Lauren Media

Gil Groupe

J. Crew Group Inc.Peapod LLC

Bluesem Brands Inc.

Ancesry.com Inc.

Nike Inc.

1-800-Flowers.com Inc.

American Eagle

Esee Lauder

Weigh Wachers

REI

Adobe Sysems Inc.

Resoraion Hardware

Dick’s Sporing Goods

PCM Inc.

Hulu LLCDeluxe Corp.

Follet Higher Educaion

Crae and Barrel

Blue Nile Inc.

Build.com Inc.

FreshDirec LLC

RueLaLa.com

Chico’s FAS Inc.

1-800 Conacs Inc.

Disney Sore USA LLC

Coach Inc.

Ascena Reail Group

Advance Auo Pars Inc.

Keurig Green MounainViacos.com Inc.

Inerline Brands Inc.

Hayneedle Inc.

Microso Corp.

Orienal Trading Co.

Ann Inc.

NoMoreRack.com Inc.

Express Inc.

Scholasic Inc.

Bass Pro

One Kings Lane

Shoebuy.com Inc.

Appliance Zone

Ca5 Commerce

Gemvara.com

eHobbies.com

Envelopes.com

3balls.com Inc.

GourmeGiBaskes.com

Sneakpeeq Inc.

ShopJimmy.com LLC

Roos Canada Ld.CableOrganizer.com Inc.

Heels.com

Fire Mounain Gems and Beads

BrickHouseSecuriy.com

RealTruck.com

AirSpla.com

Mack’s Prairie Wings Inc.

LovelySkin.com

MyJewelryBox.com

Danskin

Dexclusive.com

Buron

eCommerce Oudoors

Original Honey Baked Ham CoTrollandToad.com

HamGo Corp.

Dungarees.ne

Scenimens.com

Bag Borrow or Seal Inc.

Posiive Promoions Inc.

Prep Sporswear

BCBG Max Azria

NorhShore Care Supply

RoyalDiscoun.com

Jack’s Small Engine & Generaor

RizPix.com

Bateries.com Inc.

Summi SporsCoolibar Inc.

Bulbs.com

Cookies by Design Inc.

Conns.com

Golfalls.com Inc.

Wiseria

Gump’s Inc.

Children’s Wear Diges Inc.

U.S. Toy Co. Inc.

Tea Collecion

CookiesKids.com

Seals.com

Tupperware Brands Corp.

Boson Green Goods Inc.

Adiamor

Real Real Inc.

MagneSree

firsSTREET

Plane Shoes

TrunkClub.com

Auohaus Arizona Inc.

Baker’s Foowear Group Inc.Clicksop Inc.

Redbox Auomaed Reail LLC

Ulla Popken

World Wresling Enerainmen Inc.

Bateries Plus LLC

Teavana Corp.

Working Persons Enerprises Inc.

Novica.com

123Greeings.com Inc.

Toolech.com LLC

Island Co.

SensaionalBeginnings.com

HomeCener.com

CyberweldBuild-A-Bear Workshop Inc.

USCuter Inc.

Birhday Direc Inc.

HobbyTron.com

OversockDeals LLC

Queensboro.com

Die Direc Inc.

BikesDirec.com

Carro Ink LLC

Burlingon Coa Facory

Whieflash

BateryJuncion.com

VicorySore.com

KichenSource.comEveryhing2go.com LLC

MaxFurniure.com

Fihroom.com

Bety Mills Co., The

Vivre

UncommonGoods LLC

Igigi Inc.

ToolBarn.com Inc.

Sandard Tools and Equipmen Co.

Abe’s o Maine

Cuisinar

Taunon Press Inc., The

Source Liss2014 Internet Retail Top 500

(Companies ranked 1-100)

2013 Internet Retail Second 500

(Companies ranked 501-600)



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12HARLEY DAVIDSON | CASE STUDY

2014 internet retail audit report

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