Web Analytics A Brief Tutorial by Dr Robert

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 1

Introduction • Web 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 objectives • Tutorial Outline – Web Analytics: Context – Web Analytics: Technology & Terminology – Web Analytics: Tools and Case Studies 2

Context for Web Analytics • DSS – Decision Support System – A conceptual framework for a process of supporting managerial decision- making, usually by modeling problems and employing quantitative models for solution analysis • BI - Business Intelligence subset of DSS – An umbrella term that combines architectures, tools, databases, applications, and methodologies • BA - Business Analytics subset of BI – The application of models directly to business data – Assists in making strategic decisions • WA - Web Analytics subset of BA – The application of business analytics activities to Web-based processes, including e-commerce 3

Web Analytics - Details • Relevant Technology – – – Internet & TCP/IP Client / Server Computing HTTP (Hyper. Text Transfer Protocol) Server Log Files & Cookies Web Bugs • Data Collection – The Clickstream • Server Log Files • Page Tagging • Data Analysis – Data Preparation – Pattern Discovery – Pattern Analysis 4

Client/Server Computing Server Client This is a request This is a response 5

Internet & TCP/IP • The Internet – The infrastructure that provides for the delivery of data between computer based processes • TCP/IP – The protocols that provides for reliable delivery of data on The Internet 6

HTTP Protocol • Client sends a request to a server • Server sends a response to client • Connectionless – Client: • Opens connection to server • Sends request – Server • Responds to request • Closes connection • Stateless – Client/Server have no memory of prior connections – Server cannot distinguish one client request from another client 7

Cookies • Used to solve the “Statelessness” of the HTTP Protocol • Used to store and retrieve user-specific information on the web • When an HTTP server responds to a request it may send additional information that is stored by the client - “state information” • When client makes a request to this server the client will return the “cookie” that contains its state information • State information may be a client ID that can be used as an index to a client data record on the server 8

Web Bug Process Server C Res: Page_C. html Page C cnts - URLs & Img Src - Web. Bug Img@ WBS. TRKSTRM. COM Server B Req: Page_B. html Req: Page_C. html Req: WBS Cookie: My_Brwsr Pg A - Server A Pg B - Server B Pg C - Server C Web. Bug IMG -Referer Header - Any cookie for TRKSTRM. com Res: Web. Bug Img -Cookie to client Browser on 1 st Req. Client Browser My_Brwsr 1. Render page 2. Click on URL Res: Page_B. html Page B cnts - URLs & Img Src - Web. Bug Img@ WBS. TRKSTRM. COM Req: Page_A. html Server A Res: Page_A. html Page A cnts - URLs & Img Src - Web. Bug Img @ WBS. TRKSTRM. COM 9

Common Clickstream Data Sources • Server Log Files – Passive data collection – Normal part of web browser/ web server transaction • Page Tagging – Active data collection – Often requires a third party to implement – a vendor 10

Server Log Files Each time a client requests a resource the server of that resource may record the following in its log files: • • • The name & IP address of the client computer The time of the request The URL that was requested The time it took to send the resource If HTTP authentication used; the username of the user of the client will be recorded • Any errors that occurred • The referer link • The kind of web browser that was used 11

Server Log Files • Example – 127. 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 host frank - user name [10/Oct/2000: 13: 55: 36 -0700] - date & time "GET /apache_pb. gif HTTP/1. 0" - request 200 - status 2326 - bytes 12

Server Log Files • Technical issues for server log data – Data Preparation – Pageview Identification – User Identification – Session Identification 13

Page Tags as Data Source • Provided by Third Party - Vendor – Vendor Supplies Page Tags – Vendor Collects the Data – Vendor Analyzes the Data – Business Accesses the Data • Online or • Reports sent to Business 14

Web Data Abstractions • Abstractions concerning Web usage, Content, and Structure • Establishes precise semantics for the concepts – – – Web site Users or Visitors User Sessions Server Sessions or Visits Pageviews Clickstreams 15

Data Abstractions • Web 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 manifestations – an 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 16

Data Abstractions • Server Session or Visit - a collection of user clicks to a single Web server during a user session • Pageview - the visual rendering of a Web page in a specific environment at a specific point in time – a pageview consists of several items • frames, text, graphics, and scripts that construct a single Web page • Clickstream - a sequential series of pageview requests made from a single user 17

Web Data Abstractions (High Level) • Abstractions concerning Visitors • Establishes precise semantics for the concepts – – – – Unique Visitor Conversion Rate Abandonment Rate Attrition Loyalty Frequency Recency 18

Data Abstractions • Unique Visitor – A 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 visitor’s web browser 19

Data Abstractions • Conversion Rate – A 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 length – Starting and finishing any activity • Purchase • Download a research article • Etc. 20

Data Abstractions • Abandonment Rate – The 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 n” – The formula is (1 – ((n+1)/n) – Consider a 10 step process to acquire a resource • How any quit after step 1 or 2 or 3 or 4 or … – Consider a 5 step process to acquire a resource • How any quit after step 1 or 2 or 3 or 4 or … 21

Data Abstractions • Attrition – Attrition is a measurement of people you have been able to successfully convert but are unable to retain to convert again – Consider e-bay web site vs. web site for technical information 22

Data Abstractions • Loyalty – Loyalty is a measure of the number of visits any visitor is likely to make over their lifetime as a visitor – Reported as number of visits per visitor • 100 visitors made 3 visits each, 87 visitors made 4, etc. • Avoid double counting (i. e. do not count the 87 in with the 100) 23

Data Abstractions • Frequency – Frequency is a measure of the activity a visitor generates on a web site in terms of time between visits – Measured in terms of “days between visits” 24

Data Abstractions • Recency – Recency is the number of days since the last visit (or purchase) – Reported as the number of visitors who returned after “n” days. 25

Uniquely Identified Visitors Unique Visitors ing Va lue of Da ta Pyramid Model of Web Analytics Data Visits Inc re as Page Views Hits Volume of Available Data 26

Web Usage Mining • Web usage mining is to apply statistical and data mining techniques to the processed server log data, in order to discover useful patterns • Data mining methods and algorithms that have been adapted for the Web domain – – Association rules Sequential pattern discovery Clustering Classification 27

Web Usage Data Mining • After 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 stored – Loading the results into a data cube and then performing OLAP operations – Visualization techniques are used for an easier interpretation of the results • Using 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. 28

Web Analytics: Tools and Case Studies • Tools – Visi. Stat - www. visistat. com • Web Analytics Case Studies – – – – Communications Provider - Tu. Vox. com Online Retailer - Tickets. By. Internet. com Winery & Entertainment Venue - The Mountain Winery Non-Profit Organization - SFBallet. org Public Relations & Media Agency - BLASTmedia Technology Provider for Real Estate Professionals - Pullan. com Real Estate Agency - Intero Real Estate Start-Up Online Business - Guru. Print. com 29