Kies de Nederlandse taal
Course module: 300459-M-6
Social Media and Web Analytics
Course info
Course module300459-M-6
Credits (ECTS)6
CategoryMA (Master)
Course typeCourse
Language of instructionEnglish
Offered byTilburg University; Tilburg School of Economics and Management; TiSEM: Marketing; TiSEM: Marketing;
Is part of
M Marketing Management
dr. L.K. Deer
Other course modules lecturer
H.J.C. de With, MSc
Other course modules lecturer
Academic year2020
Starting block
Course mode
Registration openfrom 30/03/2021 up to and including 20/08/2021
The goal of this course is to develop an understanding of the social media landscape from the point of view of a marketing analyst. The course content will be unashamedly analytics heavy - utilising modern statistical and mathematical modelling techniques to deliver managerially relevant conclusions from social media data. You will develop the ability to deliver data-driven answers to the following questions: How does one develop an online reputation or develop their brand online? How does one maintain a strong user community on social networking sites? How can one craft a potent social advertising strategy? How does one quantitatively assess whether their online reputation is positive and/or improving, or whether their social media marketing is effective?
At the end of this course, you will be able to:
  • Explain the challenges and opportunities social media and social networks present marketers
  • Write new code (assisted by the use of cheat sheets), and edit existing code in R.
  • Conduct statistical analyses on large datasets from companies such as Twitter,, Trip Adviser and Facebook.
  • Translate the results of quantitative and statistical analysis to develop managerially relevant findings.
  • Critically evaluate which modelling or statistical technique is best suited to your application and understand the limitations.
  • Discuss used methods, results of analyses, and relevant academic literature in writing and/or orally.

ATTENTION! The deadlines for application deviate from the standard OSIRIS deadlines.
The enrollment procedure for this course is different for different groups:
  • Students in MSc Marketing Analytics  need to enroll via the procedure published on the MSc Marketing Analytics General Information CANVAS page  before the deadline
  • Students in MSc Marketing Management need to enroll via the procedure published on the MSc Marketing Management General Information CANVAS page before the deadline.
  • Students in Research Master need to send a message to before the deadline
  • This course is not open to student from other programs. We make no exceptions.

Registration is possible as of February 1, 5pm (for February entrants) until February 15, 5pm (for February entrants). All students who registered correctly will be enrolled in the OSIRIS course by the program management, after which they will be automatically added to the Canvas page of the course.
We will not take into account requests for enrollment after this deadline. If you want to follow this as an extra course, you can send an e-mail to before the registration deadline. You will be put on a waiting list. After placing all students (second half of September/February), we will check if there are seats left and let you know if you can do the course or not.

If there are any questions or problems, please contact immediately.
The content is divided into four blocks, consisting of:
  •  Empirical Analysis of Patterns in Social Networks
    • Describes and explains several common patterns in real world social networks
    • Demonstrates how to visualize real world networks and compute statistics that summarizes their features
  •  Analysing Brand Reputation in Online Communities
    • Explains best practise methods for maintaining a strong brand online and managing the user community
    • Demonstrates how to use text analytics to synthesize comments from consumers posted online
    • Uses statistical techniques to determine the causal effect of online communities on offline consumer decisions
  •  Quantifying the Importance of Influencers and Word of Mouth
    • Discusses how an individual’s place in the network impacts how memes, early adoption, word of mouth and other ‘information cascades’ propagate
    • Demonstrates how to measure the effect of influencers on consumer demand
    • Applies statistical techniques to measure causal effects of online word of mouth on consumer purchase
  •  Measuring the Effectiveness of Social Media Advertising
    • Evaluates what causes marketing to go viral
    • Describes marketing techniques on large social media platforms
    • Demonstrates how to evaluate the success of a social media marketing campaign
Typically, students should have the following characteristics when following this class:
  • Strong interest in understanding how social media and social networks affect consumer behaviour, brand reputation and the actions of marketers.
  • Strong interest in learning and applying statistical analysis and data science techniques to social media data, although with limited existing background.
  • Strong interest in using quantitative results to develop management or marketing insights.
  • Interest in learning how to write computer code and acquire “best practice” methods.
  • Interest in learning new quantitative modelling techniques.
  • Ability to work collaboratively.
Type of instructions
Lectures, computer lab sessions, self-study (e.g., using web clips or short computer scripts)
Type of exams
Quizzes (5%) and  group assignments (during the course of the semester, in total 40%), and  an individual course project (55%). Both the  individual course project  and the final course grade need to equal 5.5 or higher to pass the course.
Compulsory Reading
The syllabus for the course will be made available at the start of the course.
There is no need to purchase books.
Contact person
L.K. Deer
Timetable information
Social Media and Web Analytics
Required materials
Recommended materials
Quizzes (5%)

Homework assignment (40%)

Course project (55%)

Final grade

Kies de Nederlandse taal