Presented March 2008 To SAIS 2008

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Web Analytics: A Brief Tutorial by Dr. Robert J. Boncella Professor of Information Systems & Technology School of Business Washburn University. Presented March 2008 To SAIS 2008. Introduction. Web analytics is the study of the behavior of website visitors. - PowerPoint PPT Presentation

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  • *Web Analytics: A Brief TutorialbyDr. Robert J. BoncellaProfessor of Information Systems & TechnologySchool of BusinessWashburn UniversityPresented March 2008ToSAIS 2008

  • *IntroductionWeb analytics is the study of the behavior of website visitors. In a commercial context, web analytics refers to the use of data collected from a web site to determine which aspects of the website achieve the business objectivesTutorial OutlineWeb Analytics: ContextWeb Analytics: Technology & TerminologyWeb Analytics: Tools and Case Studies

  • *Context for Web AnalyticsDSS Decision Support SystemA conceptual framework for a process of supporting managerial decision- making, usually by modeling problems and employing quantitative models for solution analysisBI - Business Intelligence subset of DSSAn umbrella term that combines architectures, tools, databases, applications, and methodologiesBA - Business Analytics subset of BIThe application of models directly to business dataAssists in making strategic decisionsWA - Web Analytics subset of BAThe application of business analytics activities to Web-based processes, including e-commerce

  • *Web Analytics - DetailsRelevant TechnologyInternet & TCP/IPClient / Server ComputingHTTP (HyperText Transfer Protocol)Server Log Files & CookiesWeb BugsData Collection The ClickstreamServer Log FilesPage TaggingData AnalysisData PreparationPattern DiscoveryPattern Analysis

  • *Client/Server Computing

  • Internet & TCP/IPThe InternetThe infrastructure that provides for the delivery of data between computer based processesTCP/IPThe protocols that provides for reliable delivery of data on The Internet*

  • *HTTP ProtocolClient sends a request to a serverServer sends a response to clientConnectionlessClient: Opens connection to serverSends requestServerResponds to requestCloses connectionStatelessClient/Server have no memory of prior connectionsServer cannot distinguish one client request from another client

  • *CookiesUsed to solve the Statelessness of the HTTP ProtocolUsed to store and retrieve user-specific information on the webWhen an HTTP server responds to a request it may send additional information that is stored by the client - state informationWhen client makes a request to this server the client will return the cookie that contains its state informationState information may be a client ID that can be used as an index to a client data record on the server

  • *Cookie: My_BrwsrPg A - Server APg B - Server BPg C - Server C1. Render page2. Click on URLPage B cnts- URLs & Img Src- WebBug Img@ WBS. TRKSTRM.COMPage A cnts- URLs & Img Src- WebBug Img @ WBS. TRKSTRM.COMPage C cnts- URLs & Img Src- WebBug Img@ WBS. TRKSTRM.COMWeb Bug Process

  • *Common Clickstream Data SourcesServer Log FilesPassive data collectionNormal part of web browser/ web server transactionPage TaggingActive data collectionOften requires a third party to implement a vendor

  • *Server Log FilesThe name & IP address of the client computerThe time of the requestThe URL that was requestedThe time it took to send the resourceIf HTTP authentication used; the username of the user of the client will be recordedAny errors that occurredThe referer link The kind of web browser that was usedEach time a client requests a resource the server of that resource may record the following in its log files:

  • *Server Log FilesExample127.0.0.1 - frank [10/Oct/2000:13:55:36 -0700] "GET /apache_pb.gif HTTP/1.0" 200 2326

    127.0.0.1 Remote hostfrank - user name[10/Oct/2000:13:55:36 -0700] - date & time"GET /apache_pb.gif HTTP/1.0" - request200 - status2326 - bytes

  • *Server Log Files Technical issues for server log dataData PreparationPageview IdentificationUser IdentificationSession Identification

  • *Page Tags as Data SourceProvided by Third Party - VendorVendor Supplies Page TagsVendor Collects the DataVendor Analyzes the DataBusiness Accesses the DataOnline orReports sent to Business

  • *Web Data AbstractionsAbstractions concerning Web usage, Content, and StructureEstablishes precise semantics for the concepts Web siteUsers or VisitorsUser SessionsServer Sessions or VisitsPageviewsClickstreams

  • *Data AbstractionsWeb Site - collection of interlinked Web pages, including a host page, residing at the same network location. User or Visitors - principal using a client to interactively retrieve and render resources or resource manifestationsan individual that is accessing files from a Web server, using a browser. User Session - a delimited set of user clicks across one or more Web servers

  • *Data AbstractionsServer Session or Visit - a collection of user clicks to a single Web server during a user sessionPageview - the visual rendering of a Web page in a specific environment at a specific point in timea pageview consists of several items frames, text, graphics, and scripts that construct a single Web pageClickstream - a sequential series of pageview requests made from a single user

  • *Web Data Abstractions (High Level)Abstractions concerning VisitorsEstablishes precise semantics for the conceptsUnique VisitorConversion RateAbandonment RateAttritionLoyaltyFrequencyRecency

  • *Data AbstractionsUnique VisitorA unique visitor is counted when a human being uses a web browser to visit a web site.A visitor may be unique for different periods of time.The individual is defined by a cookie in the visitors web browser

  • *Data AbstractionsConversion RateA conversion rate is the number of completers divided by the number of starters for any online activity that is more than one logical step in lengthStarting and finishing any activityPurchaseDownload a research articleEtc.

  • *Data AbstractionsAbandonment RateThe abandonment rate for any step in a multi-step process is one minus the number of units that make it to step n+1 divided by those at step nThe formula is (1 ((n+1)/n)Consider a 10 step process to acquire a resourceHow any quit after step 1 or 2 or 3 or 4 or Consider a 5 step process to acquire a resourceHow any quit after step 1 or 2 or 3 or 4 or

  • *Data AbstractionsAttritionAttrition is a measurement of people you have been able to successfully convert but are unable to retain to convert againConsider e-bay web site vs. web site for technical information

  • *Data AbstractionsLoyaltyLoyalty is a measure of the number of visits any visitor is likely to make over their lifetime as a visitorReported as number of visits per visitor100 visitors made 3 visits each, 87 visitors made 4, etc.Avoid double counting (i.e. do not count the 87 in with the 100)

  • *Data AbstractionsFrequencyFrequency is a measure of the activity a visitor generates on a web site in terms of time between visitsMeasured in terms of days between visits

  • *Data AbstractionsRecencyRecency is the number of days since the last visit (or purchase)Reported as the number of visitors who returned after n days.

  • *Pyramid Model of Web Analytics DataHitsPage ViewsVisitsUnique VisitorsUniquely Identified VisitorsVolume of Available DataIncreasing Value of Data

  • *Web Usage MiningWeb usage mining is to apply statistical and data mining techniques to the processed server log data, in order to discover useful patternsData mining methods and algorithms that have been adapted for the Web domainAssociation rulesSequential pattern discoveryClusteringClassification

  • *Web Usage Data MiningAfter discovering patterns from usage data, a further analysis has to be conducted. Common ways of analyzing such patterns Using a query mechanism on a database where the results are storedLoading the results into a data cube and then performing OLAP operationsVisualization techniques are used for an easier interpretation of the resultsUsing these results in association with content and structure information concerning the Web site there can be extracted useful knowledge for modifying the site according to the correlation between user and content groups.

  • *Web Analytics: Tools and Case StudiesToolsVisiStat - www.visistat.comWeb Analytics Case StudiesCommunications Provider-TuVox.comOnline Retailer-TicketsByInternet.com Winery & Entertainment Venue-The Mountain Winery Non-Profit Organization-SFBallet.org Public Relations & Media Agency-BLASTmediaTechnology Provider for Real Estate Professionals-Pullan.com Real Estate Agency-Intero Real Estate Start-Up Online Business-GuruPrint.com

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