Download Course: WEB ANALYTICS Instructor: V. Nagadevara No. of Credits

Course: WEB ANALYTICS
Instructor: V. Nagadevara
No. of Credits: 1.5 (10 sessions of 90 minutes duration)
Offered in Term 6 B/6 A
I.
Background
Web Analytics involves complex interaction between human beings, computers and software
code that generates large amounts of data which needs to be analyzed in order to get the best
out of the website. According to Sarner and Janowski of the Gartner Group “Web Analytics uses
a variety of data and sources to evaluate website performance and visitor experience,
potentially including usage levels and patterns at both an individual and an aggregate level.
Data and sources may include click-stream data from the web server log, Web transaction data,
submitted data from input fields on the website and data in the internet customer depository.
The goals are to improve site performance, both from technical and content perspective,
enhance visitor experience (and thus loyalty), contribute to overall understanding of customers
and channels, and identify opportunities and risks”.
The fundamental objective of web analytics is to improve the overall quality of the visitor
experience leading to enhanced usage resulting in monetary gains to the organization. The data
from various sources is mined to provide insights for increased customer loyalty and sales. This
course aims to bring together various data sources and associated mining techniques in order to
measure the reach, customer acquisition, conversion and retention. It also looks at the key
performance indicators for various models used in online business.
II.
Objectives
This course aims to provide you with
i.
An understanding of various metrics that are important in analyzing the performance of
a website
ii.
An exposure to various techniques that are useful in web analytics and
iii.
An understanding of how to improve the overall effectiveness of the website using web
analytics.
III.
Session Coverage
1. Session 1: Introduction to Web Analytics
Web Mining: Web Content Mining, Web Structure Mining and Web Usage Mining
Readings:
Jason Burby, Angie Brown & WAA Standards Committee, “Web Analytics Definitions – Version 4.0”, Web
Analytics Association, 2007
Arun Sen, Peter A. Dacin, AND Christos Pattichis, “Current Trends in Web Data Analysis”,
Communications of the ACM November 2006/Vol. 49, No. 11, pp 85-91
Michael L. Kent∗, Bryan J. Carr, Rebekah A. Husted, Rebeca A. Pop, (2011), “Learning web analytics: A
tool for strategic communication”, Public Relations Review 37 (2011) 536– 543
2. Session 2: Web Traffic Data Sources:
Web Server Log Files, Page tags, Real time data collection, Cookies and their application
Reading:
A. Phippen, L. Sheppard and S. Furnell, (2004), “A practical evaluation of Web analytics”, Internet
Research, Volume 14, Number 4, pp. 284-293
3. Session 3: Measures for Website Performance
Various Metrics used in Web Analytics; Pyramid Model of Web Analytics Data
Readings:
Tony Chung, Rob Law, (2003), “Developing a performance indicator for hotel websites”, Hospitality
Management 22, pp 119–125
Ray Welling and Lesley White, (2006) “Web site performance measurement: promise and reality”,
Managing Service Quality, Vol. 16 No. 6, pp. 654-670
4. Sessions 4: Tools and Techniques
Content Organization tools, Process measurement tools, Visitor Segmentation tools
Readings:
Wei Fang, (2007) “Using Google Analytics for Improving Library Website Content and Design: A Case
Study”, Library Philosophy and Practice, LPP Special Issue on Libraries and Google, pp1-17
MAGDALINI EIRINAKI and MICHALIS VAZIRGIANNIS, (2003), “Web Mining for Web Personalization”,
ACM Transactions on Internet Technology, Vol. 3, No. 1, February 2003, Pages 1–27.
5. Session 5: Tools and Techniques (continued)
Campaign Analysis tools and Commerce Measurement tools
Reading:
Kazuo Nakatani and Ta-Tao Chuang, (2011), “A web analytics tool selection method: an analytical
hierarchy process approach”, Internet Research Vol. 21 No. 2, pp. 171-186.
6. Session 6: Measuring and expanding Reach of the Website
Visitor information; Ratio of new visitors to returning visitors and other associated metrics;
Geographic and other distributions; Campaign metrics for Reach
Readings:
Kerry Rodden, Hilary Hutchinson, and Xin Fu, (2010), “Measuring the User Experience on a Large Scale:
User-Centered Metrics for Web Applications”, CHI 2010 (ACM), April 10–15, Atlanta, Georgia, USA.
H. Pakkala, K. Presser, and T. Christensen (2012), “Using Google Analytics to measure visitor statistics:
The case of food composition Websites”, International Journal of Information Management 32, 504–512
7. Session 7: Customer Acquisition
Campaign for acquisition; Campaign Response metrics; Content Focus; measuring costs
involved
Reading:
Geoff Simmons, (2008), “Marketing to postmodern consumers: introducing the internet chameleon”,
European Journal of Marketing, Vol. 42 No. 3/4, pp. 299-310
8. Session 8: Customer Conversion and Retention
Conversion Rates; Abandonment rates; Conversion metrics; Conversion Campaigns; KPIs for
conversion; Measurement of retention; Segmentation; KPIs for Retention
Readings:
Waisberg, D. and Kaushik, A. 2009, “Web Analytics: Empowering Customer Centricity”, SEMJ.org
Volume2 Issue 1
Beatriz Plaza, (2011), “Google Analytics for measuring website performance”, Tourism Management 32,
pp 477-481
9. Session 9: Different Business Models and Key Performance Indicators
Popular Business Models; Identifying right KPIs; Data Integration; Optimization of design
and architecture
Readings:
Birgit Weischedel and Eelko K.R.E. Huizingh, “Website Optimization with Web Metrics: A Case Study”,
ICEC’06, August 14–16, 2006, Fredericton, Canada.
Wen-Chih Chioua, Chin-Chao Lin, Chyuan Perng, (2011) “A strategic website evaluation of online travel
agencies”, Tourism Management 32 1463-1473.
10. Session 10: Web Analytics in Action
Guest lecture: Real life application of Web Analytics
Additional / Optional Readings
A.D. Phippen, (2004) “An Evaluative Methodology for Virtual Communities Using Web Analytics”,
Campus-Wide Information Systems, Volume 21, Number 5, pp. 179-184
Antonio Cicone a, Stefano Serra-Capizzano, (2010) Google Page-Ranking problem: The model and the
analysis, Journal of Computational and Applied Mathematics 234, pp 3140-3169
P. Chen, H. Xie, S. Maslov, S. Redner (2007) Finding scientific gems with Google’s PageRank algorithm,
Journal of Informetrics 1, pp 8–15
Balachander Krishnamurthy and Craig E. Wills (2009) “Privacy Diffusion on the Web: A Longitudinal
Perspective”, WWW 2009, April 20–24, 2009, Madrid, Spain, pp 541-550.
Boanerges Aleman-Meza et. al. (2006), “Semantic Analytics on Social Networks: Experiences in
Addressing the Problem of Conflict of Interest Detection”, WWW 2006, May 23–26, 2006, Edinburgh,
Scotland, pp 407-416
Kristin Glass and Richard Colbaugh, (2011), “Web Analytics for Security Informatics”, 2011 European
Intelligence and Security Informatics Conference (IEEE), pp 214-219.
Rob Law, Shanshan Qi, Dimitrios Buhalis, (2010), “Progress in tourism management: A review of website
evaluation in tourism research”, Tourism Management 31 (2010) 297–313
IV.
Attendance Policy:
Given the sequential nature and the interdependence of the topics in the course, continuous
attendance is critical. Minimum required attendance is 80%. Those falling below the required
attendance will be penalized by a reduction of 0.5 GPA.
V.
Evaluation Procedure
There are two components of evaluation. These are
i.
Individual assignment (due one week before the final exam week)
ii.
Take-home Exam (due on the last day of the exam week)
: 50%
: 50%