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.

Syllabus

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.

Seminars

Slides & Materials[folder]

Folder access is restricted. Ask for permits

---

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

• Slides: Introduction and reminds.
• References:
  • M. Miller. The Ultimate Web Marketing Guide. Pearson, 2011. Chps. 1, 2, 4.
  • D. Raggett. Getting started with HTML, advanced HTML, a touch of style. Guides at W3C.

---

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

• Slides: Cickstream.
• References:
  • R. Kimball, R. Merz. The data webhouse toolkit. Wiley & Sons, 2000. Chp. 4.
  • B. Clifton. Advanced Web Metrics with Google Analytics. Wiley, 2012 (3rd edition). Chps. 1,2.
  • (optional, but recommended) T. Bujlow et al. A Survey on Web Tracking: Mechanisms, Implications, and Defenses. IEEE Explore, 2017.

---

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

• Slides: Log Processing. Web Metrics.
• References: A. Kaushik. Web Analytics 2.0. Wiley, 2010 Chps. 3,5.

---

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

• Slides: Google Analytics.
• References: Google Analytics - training and support

---

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

• Slides:
  • Google Analytics
  • Group Project and important dates.
• References: Google Analytics - training and support

---

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

• Slides: Finding information in the Web.
• References: E. Enge et al. The Art of SEO. O'Reilly, 2012. Chps. 1, 2.

---

- 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.

• Slides: SEO friendly websites.
• References:
  • E. Enge et al. The Art of SEO. O'Reilly, 2012. Chp. 6.
  • Google SEO Starter Guide.

---

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

• Slides: Keyword Research.
• References: E. Enge et al. The Art of SEO. O'Reilly, 2012.Chp. 5.

---

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

• Slides: Search engine advertising.
• References: M. Miller. The Ultimate Web Marketing Guide. Pearson, 2011. Chps. 14, 15, 16.

---

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

• Slides: Display advertising.
  • Group Project and important dates (updated).
  • Demo Display advertising.
• References: A. Kaushik. Web Analytics 2.0. Wiley, 2010. Chp. 12.

---

- 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.

• Slides: Controlled experiments.
• References: R. Kohavi et al. Controlled experiments on the web: survey and practical guide, Data Mining & Know. Disc. 18:140–181 (2009).

---

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

• Slides: Collaborative filtering RS.
• SW/Dataset: CF example

---

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

• Slides: Content-based RS.
• References
• D. Jannach et al. Recommender Systems: An Introduction. Cambridge University Press, 2010. Chps 2, 3.
• SW/Dataset: CB example

---

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

• Slides: Social Media Marketing.

---

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

• Slides: 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.