1. WEB ANALYTICS Prof Sunil Wattal

2. Business questions How are people finding your website? What pages are the customers most interested in? Is your website well designed? Is your site attracting qualified visitors? Is your site supporting your business objectives? ©Webtrends

3. Web Analytics According to the Web Analytics Association, Web analytics is the practice of measuring, collecting, analyzing and reporting on Internet data for the purposes of understanding and optimizing web usage Why important Web channels are an important medium for information, sales, customer service and other interactions with customers Integrated with other channels and can drive their performance Useful bellwether of customer behavior

4. Web Analytics …..then

5. 5 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

6. 6 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 Data Gathering Client browser ISP Web server

7. Web 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 referrer link The kind of web browser that was used

8. 8 Web Log Files Technical issues for server log data Data Preparation Pageview Identification User Identification Session Identification

9. 9 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

10. Key metrics Usability Page views Sessions Time on site Downloads Click map Click path Traffic sources Referral websites Search engines Direct Promotions and campaigns

11. Key metrics Visitor profiles Keyword Geography Time of day Landing page Conversion statistics New visitors Returning visitors Shopping baskets Conversions Abandonment

12. Key metrics Website performance Server capacity vs no of hits Load balancing Peak capacity Bandwidth requirement Futurability

13. Data Abstractions Attrition Attrition is a measurement of people you have been able to successfully convert but are unable to retain to convert again 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” Recency Recency is the number of days since the last visit (or purchase) Reported as the number of visitors who returned after “n” days. 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) 13

14. Pyramid Model of Web Analytics Data 14 Hits Page Views Visits Unique Visitors Uniquely Identified Visitors Volume of Available Data Increasing Value of Data

15. Web Analytics Metrics

16. Business questions……answered How are people finding your website? What pages are the customers most interested in? Is your website well designed? Is your site attracting qualified visitors? Is your site supporting your business objectives? ©Webtrends

17. Web Analytics ……. now ©kaushik.net

18. Limitations Technology Cant tell who is logging in Starbucks effect No Cookies Conceptual Exploding data volume – difficult to capture and analyze web data Unstructured data from social media is not easily integrated with structured enterprise data Multiple online channels (web, smartphone, tablet) complicates data collection No single tool to collect and analyze all the data

19. Inclass exercise Go to Alexa.com and look for the web statistics for www.temple.edu. Compare the web statistics of Temple with those of some peer schools. Is Temple using its website effectively? What recommendations would you provide to the marketing team at Temple?www.temple.edu Use the web analytics metrics we discussed in class to build a predictive model based on the techniques that we learnt in this course. Clearly list all the variables you will use from both the web data and the enterprise data. What business question(s) will your analysis answer?

20. Questions!