Contents

ExecutiveSummary Pg4‐5

CustomersSpendBigDuringPeakTrafficTimes,andWon’tToleratePoorWebPerformance Pg7‐11

UserExpectationsWereNotMetin2009DuringPeakTrafficPeriods Pg13‐16

PoorExperiencesDuringPeakTrafficTimesDirectlyImpactBusinessResults Pg18‐23

BestPracticesforManagingPeakTrafficTimes Pg25‐26

AppendixI–Methodology Pg28

2

  PeakOnlineTrafficPeriodsarecriticalsincemoreWebvisitorsmeanmorerevenueopportunities.Yetwhatareconsumers'expectationsduringpeaktraffictimes,andhowdotheybehavewhentheyexperiencepoorwebperformance?

 Tofindout,GomezcommissionedEquationResearchtoconductastudyofconsumerInternetusageexperiencesduringpeaktraffictimes  1,538respondentinterviewswerecarriedoutbetweenDec16–22,2009  Studywasconductedacross3verticals:Retail,TravelandFinancial

 ExamplesofPeakTrafficPeriods:  HolidayShoppingSeason,Valentine’sDay,Mother’sday,4thofJuly,Summer,TaxSeason,FinancialMarketMeltdowns,BacktoSchoolShopping.Thanksgiving,Xmastoendoftheyear...

Introduction

3

KeyFinding1 Customersspendbigduringpeaktraffictimes,andwon’ttoleratepoorwebperformance  51%spendasignificantpercentageoftheirretailbudgetduringpeaktimes  67%expectwebsitestoworkwellregardlessofhowmanyvisitorsthesitegetsduringpeaktraffictimes

KeyFinding2 Userexpectationswerenotmetduring2009peaktrafficperiods

  72%experiencedslowerwebsitesmorefrequentlyduringpeaktraffictimesthanatothertimes

KeyFinding3 Poorexperiencesduringpeaktraffictimesdirectlyimpactbusinessresults

  78%wenttoacompetitor’ssiteduetopoorperformanceatpeaktimes  Afterapoorexperience…

 88%arelesslikelytoreturntoawebsite 47%leftwithanegativeperceptionofthecompany 42%discussediteitherwithfriendsoronline

ExecutiveSummary

4

ExecutiveSummaryAcrossIndustryVerticals

Vertical KeyFindings

Retail

•  51%spendasignificantpercentageoftheirbudgetduringpeaktimes

•  41%wouldabandonaretailer’sWebsiteatpeaktimesandshopsomewhereelseafteronlyoneortwobadexperiences

•  33%hadabadexperienceonaretailWebsitethis2009HolidayShoppingSeason

Travel

•  35%makeasignificantpercentageoftheirtravelbookingsduringpeaktimes

•  53%wouldabandonatravelWebsiteatpeaktraffictimesandbooksomewhereelseafteronlyoneortwobadexperiences

•  24%hadanegativeexperienceonatravelWebsiteduring2009peaktravelseason

FinancialServices

•  51%offinancialserviceusers&65%ofonlinestocktradershadpoorWebexperiencesduringpeakusagetimesin2009

•  42%offinancialserviceusers&57%ofonlinestocktraderswouldswitchtoacompetitorifdissatisfiedwiththeirfinancialprovider’sWebsite

5

Key Finding 1 Customers Spend Big During Peak Traffic

Times and Won’t Tolerate Poor Web Performance

6

51%SpendaSignificantPercentageofTheirRetailBudgetDuringPeakTrafficTimes

9%

40%

37%

14%Most

Asignificantpercentage

Alittle

None

51%

RetailFindings

Figure2:Percentageofretailonlinespendingthatoccursduringpeaktraffictimes

7

35%MakeaSignificantPercentageofTheirTravelBookingsDuringPeakTrafficTimes

TravelFindings

17%

48%

28%

7%Most

Asignificantpercentage

Alittle

None

35%

Figure3:Percentageofonlinetravelbookingsdoneduringpeaktraffictimes

8

67%ofOnlineConsumersExpectWebsitestoWorkWellRegardlessofHowManyVisitorstheSiteGetsDuringPeakTrafficTimes

67%

26%

4%

Iexpectwebsitestoworknomatterhowmanyvisitorstheyhave

Iunderstandthatmorevisitorswillslowwebsitesdown

Nospecificexpectations

Figure4:Onlineconsumers'expectationsduringpeaktraffictimes

Customers are just as demanding during peak traffic times

9

Cross‐VerticalFindings

41%WouldAbandonaRetailer’sWebsiteatPeakTrafficTimes&ShopSomewhereElseAfterOnlyOneorTwoBadExperiences

10%wouldgotoacompetitivesiteafteronlyonebadexperience

10%

31%

33%

11%

6%

10%

None,I'dleaveafterthefirstbadexperience

2

3

4

5ormore

PoorexperienceswillnotimpactthewebsitesIusetoshop

41%

Figure5:Numberofpoorwebexperiencestoleratedduringpeaktraffictimesbeforeshoppingsomewhereelse

RetailFindings

10

• Onlineconsumersarelesstolerantwithtravelsitesthanretailsites• 17%wouldgotoacompetitivesiterightaway

TravelFindings

53%WouldAbandonaTravelWebsiteatPeakTrafficTimes&BookSomewhereElseAfterOnlyOneorTwoBadExperiences

17%

36%

26%

7%

4%

10%

None,I'dleaveafterthefirstbadexperience

2

3

4

5ormore

PoorexperienceswillnotimpactthewebsitesIusefortravel

Figure6:Numberofpoorwebexperiencestoleratedatpeaktraffictimesbeforebookingtravelsomewhereelse

53%

11

Key Finding 2 User Expectations Were Not Met in 2009

During Peak Traffic Periods

12

72%ofOnlineConsumersExperiencedPoorPerformanceMoreFrequentlyDuringPeakTrafficPeriodsthanatOtherTimes

51%

58%

72%

ProblemsCompletingTransactions

ErrorsonWebPages

SlowerWebSites

Figure1:Typeofissuesencounteredmorefrequentlyduring2009peaktrafficperiods

SlowerWebSiteswastheproblemmostcommonlyencountered

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Cross‐VerticalFindings

33%HadaBadExperienceonaRetailWebsitethis2009HolidayShoppingSeason

15%foundproblemsencounteredduringthe2009HolidayShoppingSeasontobe‘unacceptable’

15%

18%

67%

Yes‐anditisunacceptable

Yes‐butitdoesn'tbotherme

No‐Ihaven'thadabadexperience

33% Had a bad experience?

Figure8:PoorwebexperiencesencounteredonaretailWebsitethis2009HolidayShoppingSeason

RetailFindings

14

24%HadaNegativeExperienceonaTravelWebsiteDuring2009PeakTravelSeason

Slowloadtimewasthemostfrequentlycitedissueat18%

18%

11%

10%

1%

Slowloadtime

Problemscompletingtransactions

Errorsonwebpages

Other(specify)

Figure9:PoorExperiencesEncounteredonTravelWebsitesDuring2009PeakTrafficTimes(SummerandThanksgiving/Decemberseasons)

TravelFindings

24%

76%

Yes

No

Had a bad experience?

15

51%ofFinancialServiceUsers&65%ofOnlineStockTradersHadPoorWebExperiencesDuringPeakUsageTimesin2009

43%

23%

20%

2%

Slowloadtime

Problemscompletingtransactions

Errorsonwebpages

Other(specify)

58%

28%

31%

1%

Slowloadtime

Problemscompletingtransactions

Errorsonwebpages

Other(specify)

Figure10:PoorExperiencesEncounteredonaFinancialWebsiteDuring2009PeakUsageTimes

FinancialFindings

51% financial service users reported these problems

65% online traders reported these problems

Slowloadtimewastheproblemmostcommonlyencountered

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Key Finding 3 Poor Experiences During Peak Traffic

Times Directly Impact Business Results

17

• 78%havegonetoacompetitor’ssiteduetopoorperformanceatpeaktimes

Afterapoorexperience..

• 88%arelesslikelytoreturntoasite• 47%leftwithalesspositiveperceptionofthecompany

• 42%havediscusseditwithfamily,friends,peersoronline

PoorWebExperiencesDuringPeakTrafficTimesDirectlyImpactBusinessResults

Brand

CustomerLoyalty

Poorwebexperiencesimpactsrevenue,brand&loyalty

18

Cross‐VerticalFindings

78%HaveGonetoaCompetitiveSiteBecauseofPoorPerformanceDuringPeakTrafficTimes

30%havegonetoacompetitivesiterightawayduetopoorperformanceduringpeaktrafficperiods

22%

48%

30%Yes‐Ihavelittlepatienceforpoorwebsiteperformance

Yes‐butonlyafterseveralbadexperiences

Noimpact

78%

Figure11:PercentageofconsumersthatswitchedtoacompetitiveWebsiteafterapoorWebexperienceduringpeaktraffictimes

19

Cross‐VerticalFindings

88%AreLessLikelytoReturnAfteraPoorWebExperience

28%haveverylittletoleranceforpoorperformanceandarelesslikelytogivethewebsiteanotherchance

13%

60%

28%I'mlesslikelytoreturn‐Ihavelittlepatienceforpoorwebsiteperformance

I'mlesslikelytoreturn‐butonlyafterseveralbadexperiences

Noimpact

Figure12:PercentageofconsumerslesslikelytoreturnafterapoorWebexperience

88%

20

Cross‐VerticalFindings

AfteraPoorWebExperience,47%LeftwithaNegativePerception

47%

34%

8%

Leftwithalesspositiveperceptionofthecompany

Toldfriends,familyorcolleaguesabouttheexperience

WroteabouttheexperienceonFacebook,Twitter,ablogora

forum

Figure12:Impactonbrand&actionstakenafterapoorWebexperience

42%

42% Discussed poor experiences either with friends or online

21

Cross‐VerticalFindings

52%ofFinancialServiceUsersand68%ofOnlineStockTradersTookSomeNegativeActionasaResultofaBadWebExperience

29%

17%

13%

7%

Lesslikelytopurchaseadditionalservicesfromthem

Tellmyfriends/family/peersorwriteaboutitonthe

Internet

Useanotherfinancialprovider'ssite

Closemyaccount

40%

29%

27%

14%

Lesslikelytopurchaseadditionalservicesfrom

them

Tellmyfriends/family/peersorwriteaboutitonthe

Internet

Useanotherfinancialprovider'ssite

Closemyaccount

FinancialFindings

Figure13:Actionstakenasaresultofpoorfinancialwebsiteexperiences

52% financial service users took these actions after a poor Web experience

68% online stock traders took these actions after a poor Web experience

22

42%ofFinancialServiceUsers&57%ofOnlineStockTradersWouldSwitchtoaCompetitorifDissatisfiedWithTheirFinancialProvider’sWebsite

42%

58%

Yes

No 57%

43%

Financial Service Users Online Stock Traders

Figure14:WouldSwitchtoacompetitorasaresultofabadexperienceonafinancialprovider’sWebsite

FinancialFindings

23

Best Practices for Managing Peak Traffic Times

24

25

BestPracticesforManagingPeakTrafficTimes

LoadTestingistheonlywaytoknowhowanapplicationwillperformunderpeaktrafficconditions:

•  End‐UserExperience:WillweprovidequalityuserexperienceswhenwehavemoreWebsitevisitors,orwillcustomersencountermoreWeberrorsorproblemscompletingtransactions?

•  WebPerformance:Willthewebsiterespondfastenough?

•  Scalability:Willtheapplicationhandletheexpecteduserloadandbeyond? –beforeitgets“slow”? –beforeitstopsworking? –willitsustain?

•  Stability:Istheapplicationstableunderexpectedandunexpecteduserloads?Whatif….

–therearemoreusersthanweexpect? –alltheusersdothesamething? –wegettoomanyorders?25

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Best Practices for Managing Peak Traffic Times (Cont’d)

1.  Getready‐plantoloadtestwheneverthereisachange  Launchingmarketingandsalescampaigns  RollingoutnewWebsites,applicationsandfeatures

  Planningforseasonalandholidayspikesinwebtraffic  Upgradingorvirtualizinginfrastructures

2.  Adoptan“outside‐in”customerpointofview

  Test&monitoryourwebperformancefromtheInternet,whereyourcustomersare

  Focusonkeygeographies(newmarkets,mostvisitors,toprevenue‐generatingregions,…)

3.  EnsurethatyourbusinessgoalsaresupportedbyIT  Discussupcomingplans&eventswithyourITcounterparts

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Appendix

27

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DesignandMethodology

Overview

•  GomezInc.engagedEquationResearchtoconductanonlinestudytounderstandconsumerInternetusageexperienceduringpeaktraffictimes

•  InterviewsconductedfromDecember16‐22,2009

Methodology

•  RespondentsrecruitedfromEquation’snationallyrepresentativepanel

•  Surveyresultsmayhaveamarginoferrorofplusorminusthreepercentata95percentlevelofconfidence

Sample:1,538totalrespondents•  N=500respondentswhohaveboughtaproductorserviceonlineinthepast9months•  N=506respondentswhohavebookedtravelinthepast9months

•  N=532respondentswhohaveperformedafinancialtransactioninthepast9months(includingn=183respondentswhobought/soldstockonline)

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