TMW 22/23


Technologies for Web Marketing

Graduate Programs in Data Science & Business Informatics and in Digital Humanities
Academic Year 2022-2023, second semester

  • WHERE: Polo Didattico "L. Fibonacci", Via F. Buonarroti 4 (room 288), Pisa
  • WHEN:
    • Tuesday, 14:00-16:00 - Fib A1 (Polo Fibonacci B)
    • Thursday, 16:00-18:00 - Fib C1 (Polo Fibonacci B)
  • OFFICE HOURS: Thursday, 14:00-16:00 (by appointment only) - room 288 @ Dpt. Computer Science
  • WHAT: Programma 2022/2023 - 537AA.


Web analytics is the collection, measurement, analysis, and reporting of Internet data (web, mobile, social media, e-mail), aimed at digital service optimization and a deep customer and market understanding. The course presents web analytics methods, algorithms, strategies, and tools with several applications. Specifically, the focus is on web personalization for improving user experience; web marketing and advertising for improving visibility; search engine optimization for improving ranking; social media analysis for improving reachability and understanding opinions.


Slides & Materials[folder]

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- Lesson 1 21/02/23: Introduction. What is (web) marketing? Reminds on Internet, Web, URLs and HTML

- Lesson 2 23/02/23: Web analytics. Clickstream.

- Lesson 3 28/02/23: Clickstream. Web analytics metrics. Introduction to Google Analytics.

- Lesson 4 02/03/23: Google Analytics demo.

- Lesson 5 07/03/23: Google Analytics demo (pt. 2). Project Presentation and Important Dates.

- Lesson 6 09/03/23: Finding information in the Web. Search engine ranking metrics.

- Lesson 7 14/03/23: PageRank.

  • Slides: PageRank.
  • References: M. Levene. An Introduction to Search Engines and Web Navigation. Wiley, 2010. Chp. 5.

- Lesson 8 16/03/23: SEO friendly websites.

- Lesson 9 21/03/23: Keyword research. Google Ads (Keyword Planner) demo. Introduction to on-line advertising.

- Lesson 10 23/03/23: Search engine advertising.

- Lesson 11 28/03/23: Display advertising. Group Project and important dates.

- Lesson 12 04/04/23: Web Personalization.

  • Slides: Web Personalization.
  • References: N. Cheng et al. (2011): Author gender identification from text. Digital Investigation 8 (2011) 78-88.

- Lesson 13 06/04/23: Controlled experiments on the Web. Google Optimize Demo.

- Lesson 15 27/04/23: Recommender systems: collaborative filtering.

- Lesson 16 02/05/23: Recommender systems: content-based filtering.

- Lesson 18 16/05/23: Social Media Marketing.

- Lesson 29 25/05/23: Exercises and Exam modalities.

Learning Outcomes

Knowledge. The student who successfully completes the course will have a solid knowledge about information technologies for marketing decisions on the web, on how to advertise effectively, on how to track users and explore web metric summaries, on how to improve/personalize the customer experience on a web site, on how to invest available resources, and on how to measure success in using web marketing technologies.

Assessment criteria of knowledge. The student will be assessed on her/his demonstrated ability to discuss the course contents using the appropriate terminology and to apply the web marketing techniques and tools to proposed case studies.

Skills. The student will be able to understand and classify the large number of problems that arise in the application field of web marketing.

Assessment criteria of skills. Attending students will be asked to participate in a group project aimed at assessing skills in the design of a marketing campaign.
Non-attending students will be asked to solve exercises during a written exam and oral discussion.

Behaviors. The student will be aware of the many privacy and legal issues related to web tracking, user profiling, and the application of advertising, personalization, and social media marketing strategies.

Assessment criteria of behaviors. Ethical and legally-compliant behavior of students will be assessed during project development and/or at the oral exam.

Copyright © Laura Pollacci. Last updated: .
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