packages, and the benefits and challenges of each. The metrics for web analytics are introduced as well as an approach to understanding and

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2 20. Web Analytics What s inside: Yu will learn abut the different types f web analytics packages, and the benefits and challenges f each. The metrics fr web analytics are intrduced as well as an apprach t understanding and analysing web data. Single page analysis packages and apprach are als explained.

3 Web Analytics intrductin Web Analytics key terms and cncepts 20.1 intrductin 20.2 histry Picture the scene: yu ve pened up a new fashin retail utlet in the trendiest shpping centre in twn. Yu ve spent a small frtune n advertising and branding. Yu ve gne t great lengths t ensure that yu re stcking all f the prestigus brands. Cme pening day yur stre is inundated with visitrs and ptential custmers. And yet, yu are hardly making any sales. Culd it be because yu have ne cashier fr every hundred custmers? Or maybe it s the fact that the smell f yur freshly painted walls chases custmers away befre they cmplete a purchase. While it can be difficult t islate and track the factrs affecting yur revenue in this fictinal stre, mve it nline and yu have a wealth f resurces available t assist yu with tracking, analysing and ptimising yur perfrmance. T a marketer, the Internet ffers mre than just new avenues f creativity. By its very nature, it allws yu t track each click t yur site and thrugh yur site. It takes the guesswrk ut f pinpinting the successful elements f a campaign, and can shw yu very quickly what s nt wrking. It all cmes dwn t knwing where t lk, knwing what t lk fr, and knwing what t d with the infrmatin yu find. term A/B Split Test Click path Cnversin Cnversin funnel Cunt Event Gal Heat map Hit definitin 20.3 key terms and cncepts Testing tw variables fr statistically significant influence. The clicks taken by a visitr t a website in ne visit. A visitr cmpleting a target actin. A defined path that visitrs shuld take t reach the final bjective. Raw figures captured fr analysis, these are the mst basic web analytics metric. A step a visitr takes in the cnversin prcess. The defined actin that visitrs shuld perfrm n a website r the purpse f the website. A data visualisatin tl that shws levels f activity n a web page in different clurs, reds and yellws shwing the mst activity and blues and vilets the least. Every request t the server is recrded as a hit, mistakenly used in web analytics as an indicatin f a successful website. Testing, analysing and ptimising are nt new t marketing. Being able t gauge the success f any campaign is crucial t grwth. Early web analytics packages came t the fre in the mid 1990s; a cuple f years after the first Msaic brwser was launched. Early analysis reflected the nature f the early web, fcusing nly n hits with sme very basic click stream analysis. With ne page websites being the nrm, it was enugh t knw hw many clicks came t the website. Traffic meant yu were ding well. Yu can still see hit cunters n sme websites tday. The websites yu find these n usually lk as sphisticated as this tl. Hwever, as websites became mre cmplex, and as mre peple had access t the Internet, better analysis became mre imprtant. Measuring hits was, and is, nt enugh. In fact, measuring hits is a fairly meaningless task. Web analytics split int tw types f tls: page tags and lg files. Bth cntinue t becme mre sphisticated, capturing infrmatin abut visitrs t a website, and recrding detailed infrmatin related t their time n a website. There are several lg file analysis tls which cst nthing t use. Sphisticated page tag web analytics became available fr free when Ggle bught Urchin in March 2005 and launched Ggle Analytics as a free service. Are yu wndering what the difference is between page tag and lg file analysis? Dn t wrry, it s cming! JavaScript Key Perfrmance Indicatr (KPI) A ppular scripting language als used in web analytics fr page tagging. A metric that indicates whether a website is achieving its gals Lg files Multivariate test Page tags Rati Referrer Segmentatin Visitr Text files created n the server each time a click takes place, capturing all activity n the website. Testing many variables t determine statistically significant influences n utcmes. JavaScript files embedded n a web page and executed by the brwser. An interpretatin f data captured, a rati can be between cunts, ratis r a rati and a cunt metric. The URL that riginally generated the request fr the current page Filtering visitrs int distinct grups based n characteristics s as t analyse visits. An individual visiting a website that is nt a search engine spider r a script.

4 Web Analytics hw it wrks tracking and cllecting data Web Analytics hw it wrks setting bjectives, gals, and KPIs 20.4 hw it wrks Tracking and Cllecting Data Currently, there are tw main appraches fr cllecting web analytics data: lg file analysis and page tagging. Lg file analysis sftware reads the recrds, called lg files, n the web server, which recrd all clicks which take place n the server. Web servers have always stred all the clicks which take place in a lg file, s the sftware interprets data which has always been available. A new line is written in a lg file with each new request. Fr example, clicking n a link, an Ajax call r submitting a frm will each result in a new line being written. Page tagging, n the ther hand, sends infrmatin t a third-party server, where statistics can be generated. The brwser executes JavaScript cde which cmmunicates with the tracking sftware. Pixel tracking can be used t track campaigns. Here, a tiny 1 pixel by 1 pixel is placed in the . When yu lad the images in the , yu will als lad the tiny image which tracks yur activity. What yu shuld knw: Lg file analysis Lg files are nrmally prduced by web servers, s the raw data is readily available. Page tagging, hwever, requires changes t the website. Lg files are very accurate they recrd every click. Page tagging can be less accurate. If a user s brwser des nt supprt JavaScript, fr example, n infrmatin will be captured. Lg files are in a standard frmat, s it is pssible t switch vendrs and still be able t analyse histrical data. Page tagging is prprietary t each vendr, s switching can mean lsing histrical data. Lg files recrd visits frm search engine spiders useful fr search engine ptimisatin. Lg files recrd failed requests, whereas page tagging nly shws successful requests. Page tagging JavaScript makes it easier t capture mre infrmatin (e.g. prducts purchased, r screen size f a user s brwser). Yu can use lg file analysis t capture this infrmatin, but it will invlve mdifying the URLs. Page tagging can reprt n events, such as interactins with a Flash mvie, smething lg file analysis cannt. Page tagging can be used by cmpanies which d nt run their wn web servers. Page tagging service prviders usually ffer a greater level f supprt. This is because it is a third-party service, whereas lg file analysis sftware is ften managed in-huse. Due t the different methds f cllecting data, the raw figures prduced by the tw services will differ. Smetimes, bth are used t analyse a website and raw figures nt matching up shuld nt be a prblem. It is in the interpretatin f these figures that yu will be able t understand hw effective yur digital marketing effrts are. Website analytics packages can be used t measure mst, if nt all, digital marketing campaigns. Website analysis shuld always accunt fr the varius campaigns being run. Fr example, generating high traffic vlumes by emplying varius digital marketing tactics like SEO, PPC and marketing can prve t be a pintless and cstly exercise if the visitrs are leaving yur site withut achieving ne (r mre) f yur website s gals. Cnversin ptimisatin aims t cnvert as many f a website s visitrs as pssible int active custmers Setting Objectives, Gals, and KPIs The key t the success f any website r nline campaign is that it is designed with clearly defined bjectives in mind. These will be used t measure the success f the website r campaign, and are crucial t maintaining fcus within nline activities. Smetimes, wrds like bjective r gal can be used in different situatins with slightly different meanings. Fr the sake f clarity, this chapter will use them as described belw, unless therwise indicated. The bjective f a website r nline campaign is aligned with the strategic utcmes f the business. The bjective f a campaign may be t create awareness fr a new business r it might be t increase sales f a prduct. Smetimes campaign bjectives are referred t as gals, althugh gals can have anther meaning in web analytics. The gal f a website r campaign in web analytics refers t an actin that a user takes n a website r a type f user behaviur. This actin culd be making a purchase, signing up fr a newsletter, r viewing a certain number f pages in a visit. Gals related t visitr behaviur such as time spent n site r page-views per visit are smetimes referred t as engagement gals. Gals are usually aligned with the business bjectives. nte Caching is when a brwser stres sme f the infrmatin fr a web page, s it can retrieve the page mre quickly when yu return t it. If a web page is cached by yur brwser, when yu lk at the page again, it will nt send a request t the web server. This means that that particular visit will nt shw in the lg files. Page tagging, hwever, wuld capture this visit. But, sme brwsers d nt supprt JavaScript, and page tagging wuld nt capture thse visits. This is why there is ften a discrepancy in the numbers reprted by the tw services

5 Web Analytics hw it wrks setting bjectives, gals, and KPIs Web Analytics hw it wrks setting bjectives, gals, and KPIs Key perfrmance indicatrs r KPIs are metrics which indicate whether bjectives are being met. There are many metrics t be analysed, and s determining which are imprtant will help t fcus n what really matters t a particular campaign. The bjective f a website r campaign may depend n the type f industry, but usually it will be an actin which results in revenue fr the cmpany. The gal f a website is als intrinsically linked t the actin that yu want visitrs t perfrm. Althugh a website has an ultimate gal, the prcess f achieving that gal can be brken dwn int several steps. These are called events r micrcnversins. Analysing each step in the prcess is called funnel analysis r path analysis and is critical t understanding where prblems in the cnversin prcess may lie. The clicks a visitr makes nce landed n a site, whether they fllw the desired steps r nt, are referred t as a click path. 13,430 ttal visitrs t the site Persuasin Event 1: Perfrm a search fr available dates fr htels in the desired area. Event 2: Check prices and amenities fr available htels. Event 3: Select a htel and g t checkut. Event 4: Enter persnal and payment details and cnfirm bking (cnversin). One expects fewer users at each step that s why it s called a funnel. Increasing the number f visitrs wh prgress frm ne step t the next will g a lng way t imprving the verall cnversin rate f the site. There are als ther pinters, r indicatrs, f whether yu are achieving yur gals. Here are sme examples f bjectives, gals and KPIs fr different websites: Hspitality ecmmerce site, such as Objective: increase bkings Objective: decrease marketing expenses Gal: make a reservatin nline KPIs: cnversin rate cst per visitr average rder value nte Events and KPIs are nt the same thing. Events can be seen as steps twards a gal and are usually an actin perfrmed by a visitr. KPIs are indicatrs that the website s gals are being met. f visitrs f visitrs News and cntent sites, such as Objective: increase readership and level f interest Objective: increase time visitrs spend n website Gal: a minimum time n site f visitrs f visitrs Cnversin KPIs: length f visit average time spent n website percentage f returning visitrs Figure 20.1 An example f a cnversin funnel. Fr example, n a htel website, the ultimate gal is that visitrs t the site make a bking n the website with a credit card. Each step in the prcess is an event which can be analysed as a cnversin pint. KPIs help yu t lk at the factrs yu can influence. Fr example, if yur bjective is t increase revenue, yu culd lk at ways f increasing yur cnversin rate (that is the number f visitrs wh purchase smething). One way f increasing yur cnversin rate culd be t ffer a discunt. S, yu wuld have mre sales, but prbably a lwer average rder value. Or, yu culd lk at ways f increasing the average rder value, s the cnversin rate wuld stay the same, but yu increase the revenue frm each cnversin. Once yu have established yur bjectives, gals and KPIs, yu need t be able t track the data that will help yu analyse hw yu are perfrming, and will indicate hw yu can ptimise yur website r campaign

6 Web Analytics hw it wrks setting bjectives, gals, and KPIs Web Analytics hw it wrks setting bjectives, gals, and KPIs KPIs and events break dwn the factrs and steps that can be influences s as hw yur website features in terms f creating lyalty. As well as grwing t achieve the gals f the website. They allw yu t see n a micr level what verall visitr numbers, a website needs t grw the number f visitrs wh is affecting perfrmance n a macr level. cme back. An exceptin might be a supprt website repeat visitrs culd indicate that the website has nt been successful in slving the visitr s What infrmatin is captured prblem. Each website needs t be analysed based n its purpse. KPIs are the metrics which help yu understand hw well yu are meeting yur bjectives. A metric is a defined unit f measurement. Definitins can Visit characterisatin: vary between varius web analytics vendrs depending n technlgy and the Entry page the first page f a visit. apprach t gathering data, but the standard definitins are prvided here. Landing page the page intended t identify the beginning f the user experience resulting frm a defined marketing effrt. Web analytics metrics are either a: Exit page the last page f a visit. cunt these are the raw figures captured that will be used fr Visit duratin the length f time in a sessin. analysis. Referrer the URL that riginally generated the request fr the rati this is an interpretatin f the data that is cunted. current page. internal referrer a URL that is part f the same website. discussin Why wuld yu want t lk at the activity f a single visitr? Why wuld yu want t segment the traffic fr analysis? In analysis, metrics can be applied t three different spheres: aggregate all traffic t the website fr a defined perid f time. segmented a subset f all traffic accrding t a specific filter, such as by campaign (PPC) r visitr type (new visitr vs. returning visitr). individual the activity f a single visitr fr a defined perid f time. Here are sme f the key metrics yu will need t get started n with website external referrer a URL that is utside f the website. search referrer the URL has been generated by a search functin. visit referrer the URL that riginated frm a particular visit. riginal referrer the URL that sent a new visitr t the website. analytics: Building blck terms: Hit a request t the server (and a fairly meaningless number n its wn). Page unit f cntent (s dwnlads and Flash files can be defined as a page). Page views the number f times a page was successfully requested. Visit r sessin an interactin by an individual with a website Clickthrugh the number f times a link was clicked by a visitr. Clickthrugh rate the number f times a link was clicked divided by the number f times it was seen (impressins). Page views per visit the number f page views in a reprting perid divided by the number f visits in that same perid perid t get an average f hw many pages are being viewed per visit. These are sme f the metrics that tell yu hw visitrs reach yur website, and hw they mve thrugh the website. The way that a visitr navigates a nte A search referrer, visit referrer and riginal referrer are all external referrers. An riginal referrer will send new visitrs t the website. The visit referrer is fr returning visitrs. cnsisting f ne r mre page views within a specified perid f time. website is called a click path. Lking at the referrers, bth external and Unique visitrs the number f individual peple visiting the website internal, allws yu t gauge a click path that visitrs take. ne r mre times within a set perid f time. Each individual is nly cunted nce. Cntent characterizatin: nte A repeat visitr may be either a new visitr r a return visitr. new visitr a unique visitr wh visits the website fr the first time ever in the perid f time being analysed. repeat visitr a unique visitr with tw r mre visits within the time perid being analysed. Page exit rati number f exits frm a page divided by ttal number f page views f that page. Single page visits visits that cnsist f ne page, even if that page was viewed a number f times. return visitr a unique visitr wh is nt a new visitr. Bunces (r single page view visits) visits cnsisting f a single page view. Bunce rate single page view visits divided by entry pages. These are the mst basic web metrics. They tell yu hw much traffic yur website is receiving. Lking at repeat and returning visitrs can tell yu abut When a visitr views a page, they have tw ptins: leave the website, r view anther page n the website. These metrics tell yu hw visitrs react t

7 Web Analytics hw it wrks setting bjectives, gals, and KPIs Web Analytics hw it wrks analysing data yur cntent. Bunce rate can be ne f the mst imprtant metrics that yu measure. There are a few exceptins, but a high bunce rate usually means high dissatisfactin with a web page. nte Fr the mst up t date definitins, visit assciatin.rg t dwnlad the latest definitins in PDF frmat. Cnversin metrics: Event a recrded actin that has a specific time assigned t it by the brwser r the server. Cnversin a visitr cmpleting a target actin. Other metrics which apply t digital marketing tactics include: Impressin each time an advert r a page is served. Open each that is deemed pen, usually if the images are laded an is cnsidered pen. In rder t test the success f yur website, yu need t remember the TAO f cnversin ptimisatin: Track Analyse Optimise Nw that yu knw what tracking is, yu can use yur bjectives and KPIs, yu can chse what metrics yu ll be tracking. Yu ll then need t analyse these results, and then take apprpriate actins. And the testing begins again! Analysing Data A number is just a number until yu can interpret it. Typically, it is nt the raw figures that yu will be lking at, but what they can tell yu abut hw yur users are interacting with yur website. Because yur web analytics package will never be able t prvide yu with 100% accurate results, analysis needs t lk at trends and changes vertime in rder fr yu t understand yur brand s perfrmance. Hw t analyse Avinash Kaushik, authr f Web Analytics: An Hur A Day, recmmends a three prng apprach t web analytics: Analysing behaviur data that infers the intent f a website s visitrs. Fr example why are peple n ur website? Analysing utcmes metrics that shws hw many visitrs perfrmed the gal actins n a website. Fr example are visitrs perfrming the actins we want them t? Testing and analysing data that tells us abut the user experience. What are the patterns f user behaviur? Hw can we influence them t ptimise/achieve ur bjectives? Figure 20.2 Avinash Kaushik s three prng apprach t web analytics Behaviur data: intent Web users behaviur can indicate a lt abut their intent. Lking at referral URLs and search terms used t find the website can tell yu a great deal abut what prblems visitrs are expecting yur site t slve. Click density analysis, segmentatin and metrics that define the visit and cntent, can all be used t gauge the intent f yur visitrs. A crucial, and ften verlked, part f this analysis is that f internal search. Internal search refers t the searches that users perfrm n the website, f the website s cntent. While a great deal f time is spent analysing and ptimising external search using search engines t reach the website in questin analysing internal search ges a lng t way t determining hw effectively a website is in delivering slutins t visitrs. Internal and external search data is likely t be very different. Internal search data can g a lng way t expsing weaknesses in site navigatin and the internal search itself, and can expse gaps in inventry n which a website can capitalise. Fr example, cnsider the keywrds a user might use when searching fr a htel website, and keywrds that might be used by a the user when n the website

8 Web Analytics hw it wrks analysing data Web Analytics hw it wrks analysing data Keywrds t search fr a htel website: Cape twn htel Bed and breakfast cape twn Once n the website, the user might use the site search functin t find ut further infrmatin. Keywrds they might use include: Table muntain Pets Babysitting service Experience: why users acted the way they did, and hw that can be influenced In rder t determine the factrs that influence user experience, it is necessary t test and determine the patterns f user behaviur. Understanding why users behave in a certain way n yur website will shw yu hw that behaviur can be influenced s as t increase yur successful utcmes. This is cvered in the next chapter n Cnversin Optimisatin. Analytics tls can shw what keywrds users search fr, what pages they visit after searching and, f curse, whether they search again with a variatin fthse keywrds r different nes. Outcmes: meeting expectatins At the end f the day, yu want peple wh visit yur website t perfrm an actin which increases the website s revenue. Analysis f gals and KPIs indicate where there is rm fr imprvement. Lk at user intent t establish hw yur website meets the user s gals, and if they match with the website gals. Lk at user experience t determine hw utcmes can be influenced. Figure 20.4 CrazyEgg can prduce infrmatin that will help yu understand where users are clicking n yur website. Figure 20.3 The abve image shws hw analysing each event can shw where the website is nt meeting expectatins. After perfrming a search, 100 visitrs land n the hme page f a website. Frm there, 80 visitrs visit the first page twards the gal. This event has an 80% cnversin rate. 20 visitrs take the next step. This event has a 25% cnversin rate. 10 visitrs cnvert int paying custmers. This event has a 50% cnversin rate. The cnversin rate f all visitrs wh perfrmed the search is 10%, but breaking this up int events we can analyse and imprve the cnversin rate f each event. Single page heat maps Cmpanies such as Crazy Egg ( have sftware that can shw yu exactly where users click n a web page, regardless f whether they are clicking n links r nt. It prduces infrmatin that helps yu knw what areas f a website are clickable, but attract few r n clicks, and what areas are nt clickable but have users attempting t click there. This can shw yu what visual clues n yur web page influence where yur visitrs click, and this can be used t ptimise the click path f yur visitrs

9 Web Analytics hw it wrks analysing data Web Analytics tls f the trade There are many factrs that culd be preventing yur visitrs frm achieving specific end gals. Frm the tne f the cpy t the clur f the page, everything n yur website may affect cnversins. Pssible factrs are ften s glaringly bvius that ne tends t miss them, r s small that they are dismissed as trivial. Changing ne factr may result in ther unfreseen cnsequences and it is vital t ensure that we dn t jump t the wrng cnclusins. Segmentatin Every visitr t a website is different, but there are sme ways we can characterise grups f users, and analyse metrics fr each grup. This is called segmentatin. Sme segments include: Referral URL Users wh arrive at yur site via search engines, thse wh type in the URL directly and thse wh cme frm a link in an nline newspaper article are all likely t behave differently. As well as cnversin rates, click path and exit pages are imprtant metrics t cnsider. Cnsider the page n which these visitrs land t enter yur website can anything be dne t imprve their experience? Landing pages Users wh enter yur website thrugh different pages can behave very differently. What can yu d t affect the page n which they are landing, r what elements f the landing page can be changed t influence utcmes? t determine which package best serves yur needs. Bear in mind that it is pssible t switch vendrs with lg file analysis sftware withut lsing histrical data, but it is nt as easy t d s with page tagging sftware. Belw are sme leading prviders: Ggle Analytics (page tagging analysis) - ClickTracks (lg file and page tagging) AWStats (lg file analysis) - awstats.surcefrge.net Webalizer (lg file analysis) - SiteCatalyst - When it cmes t running split tests, if yu dn t have the technical capacity t run these in-huse, there are a number f third-party services that can hst these fr yu. Ggle s Website Optimizer ( can help yu d just that. A basic split test calculatr is available at Crazy Egg ( is a strange sunding name, but this tl can help yu t see exactly where visitrs are clicking n a web page. Cnnectin speed, perating system, brwser Cnsider the effects f technlgy n the behaviur f yur users. High bunce rate fr lw bandwidth users, fr example, culd indicate that yur site is taking t lng t lad. Visitrs wh use pen surce technlgy might expect different things frm yur website t ther visitrs. Different brwsers might shw yur website differently hw des this affect these visitrs? Gegraphical lcatin D users frm different cuntries, prvinces r twns behave differently n yur website? Hw can yu ptimise user experience fr these different grups? First time visitrs Hw is the click path f a first time visitr different t a returning visitr? What parts f the website are mre imprtant t first time visitrs? 20.5 tls f the trade The very first thing yu need when it cmes t web analytics is a web analytics tl fr gathering data. Sme are free and sme are paid fr. Yu will need Figure 20.5 An example f a split test tl

10 Web Analytics prs and cns Web Analytics references 20.6 prs and cns Tracking, analysing and ptimising is vital t the success f any marketing effrts, and even mre s t digital marketing effrts. digital marketing allws fr easy and fast tracking, and the ability t ptimise frequently the bigger picture Tracking, analysing and ptimising are fundamental t any digital marketing activity. The Internet allws fr sphisticated data gathering, s it is pssible t track almst every detail f any campaign summary Hwever, it can be easy t becme fixated n figures instead f using them t ptimise campaign grwth. Generally, macr, r glbal metrics shuld be lked at befre starting t analyse micr elements f a website. Testing variables is vital t success. Results always need t be statistically analysed, and marketers shuld let these numbers make the decisins. Never assume the utcme wait fr the numbers t infrm yu. The ability t track user behaviur n the Internet allws fr analysis f every level f a digital campaign, which shuld lead t imprved results ver time. Mst analytics packages can be used acrss all digital marketing activities, allwing fr an integrated apprach t determining the success f campaigns. While it is imprtant t analyse each campaign n its wn merits, the Internet allws fr a hlistic apprach t these activities. The savvy marketer will be able t see hw campaigns affect and enhance each ther. The cnnected nature f the Internet has made it pssible t reach ut t far greater audiences, and fr peple arund the wrld t interact, create and share. The same cnnected nature is what makes it pssible fr website wners t track and analyse hw users arund the wrld interact with their website, and t ptimise it fr thse users. The fundatin f successful analysis and ptimisatin is t determine campaign and business gals upfrnt and use these t determine KPIs fr that campaign. Analysing metrics which are nt indicatrs f success will detract frm timely ptimisatin. Web analytic packages cme in tw flavurs: lg file analysis and page tagging analysis, althugh sme packages cmbine bth methds. chapter questins 1. Why is a hit nt strictly a useful metric? 2. What wuld yu learn frm a single page heat map? 3. What is the difference between a gal and a KPI? Metrics are either: Cunts Ratis KPIs, which are either cunts r ratis Data can be analysed t infer user behaviur and intent, utcmes achieved and user experience. Testing t ptimise user experience can demnstrate ways t influence user behaviur s that mre successful utcmes can be achieved. Testing is cvered in the next chapter and can be perfrmed via: Listening labs A/B split testing Multivariate testing One page analytics 20.9 references Burby, J. Brwn, A. & WAA Standards Cmmittee, (August 2007) Web Analytics Definitins Versin WebAnalyticsDefinitinsVl1.pdf [Accessed 3 March 2008] GrkDtCm (2004) Help yurself t a KPI! GrkDtCm by Future Nw. [Accessed 16 May 2011] Kaushik, A. (26 June 2006) Are Yu Int Internal Site Search Analysis? Yu Shuld Be [Accessed 16 May 2011] Segmenting the audience allws fr analysis and ptimising fr specific grups f users

11 Web Analytics further reading Kaushik, A. (10 August 2006) Trinity: A Mindset & Strategic Apprach [Accessed 16 May 2011] Kaushik, A. (20 Octber 2007) Kick Butt With Internal Site Search Analytics [Accessed 16 May 2011] image references Wikimedia Cmmns (September 2009)Living Shadw Accessed May 2011 further reading Web Analytics 2.0 by Avinash Kaushik if yu are lking t get started in web analytics, yu can t g wrng with this bk by Avinash Kaushik - Avinash Kaushik is an analytics evangelist, and his regular insight is essential reading fr any digital marketer 506

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