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2023/2024  KAN-CCMVV1445U  Digital Analytics and Digital Experimentation

English Title
Digital Analytics and Digital Experimentation

Course information

Language English
Course ECTS 7.5 ECTS
Type Elective
Level Full Degree Master
Duration One Quarter
Start time of the course Second Quarter
Timetable Course schedule will be posted at calendar.cbs.dk
Max. participants 100
Study board
Study Board for cand.merc. and GMA (CM)
Course coordinator
  • Felix Eggers - Department of Marketing (Marketing)
Main academic disciplines
  • Customer behaviour
  • Marketing
  • Statistics and quantitative methods
Teaching methods
  • Blended learning
Last updated on 15-02-2023

Relevant links

Learning objectives
The main objective of this course is to provide an overview of marketing research techniques dedicated to collecting and analyzing data from online environments and learn how and when they can be applied. At the end of the course the student is expected to be able to:
  • explain digital marketing research techniques and differentiate between them
  • apply suitable marketing research techniques to collect and analyze data
  • interpret the outcome of analyses and explain the managerial implications
  • select a suitable marketing research technique for a given marketing decision problem
  • evaluate the ethical implications of digital marketing research
Course prerequisites
The course will be based on freely available software (including R) that will be discussed in the course (no previous experience is required). Basic statistical knowledge is required. Further required knowledge will be taught and practiced in the course.
Digital Analytics and Digital Experimentation:
Exam ECTS 7,5
Examination form Home assignment - written product
Individual or group exam Individual exam
Size of written product Max. 15 pages
Assignment type Written assignment
Release of assignment The Assignment is released in Digital Exam (DE) at exam start
Duration 2 weeks to prepare
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Winter
Make-up exam/re-exam
Same examination form as the ordinary exam
If the student fails the ordinary exam the course coordinator chooses whether the student will have to hand in a revised product for the re-take or a new project.
Course content, structure and pedagogical approach

Due to the ongoing digitalization of business models and consumer interactions there is an abundance of online data available that can provide valuable marketing insights if collected and analyzed properly. Moreover, the online environment facilitates digital testing and experimentation to inform marketing decision making. This course will focus on digital marketing research techniques for collecting and analyzing such data. The research techniques can be broadly separated into two categories: 1) digital analytics and 2) digital experimentation.


Digital Analytics:

• Web analytics, analysis of user generated content

• Text analysis, analysis of unstructured data, sentiment analysis

• Social network analysis

• Online customer journeys, attribution modeling


Digital Experimentation:

• Matching approaches

• A/B tests, explore & exploit

• Discrete choice experiments

• Choice modeling


The course focuses on the application of these techniques and how they can inform decision making in marketing, for example, regarding advertising effectiveness, targeting, product development, or pricing. The course will also address ethical implications of digital marketing research regarding data privacy and algorithmic biases and the concept of Corporate Digital Responsibility.


This course addresses several of the Nordic Nine principles. As the course is centered around analytical and experimental approaches that need to be translated into managerial implications it covers "Knowledge: you are analvtical with data and curious about ambiguity." While the digital environment offers many benefits, digitalization also creates concerns and potential threats for consumers, such as violation of data privacy and algorithmic biases. In that regard, the course addresses the "Values: you understand ethical dilemmas and have the leadership values to overcome them; 

vou are critical when thinking and constructive when collaborating." The result, being digitally responsible, leads to the "Action: you produce prosperity and protect the prosperity of next generations."

Description of the teaching methods
The teaching will be blended and consists of a mixture of prerecorded lectures, dialog-based in-class lectures, presentations, and computer tutorials.
Feedback during the teaching period
Students will receive feedback via in-class discussions and during exercises. Additional individual feedback can be obtained during office hours.
Student workload
Preperation 123 hours
Teaching 33 hours
Exam 50 hours
Expected literature

The literature consists of state-of-the-art journal articles and book chapters as listed below. This list may be updated at the start of the course. 

Digital Analytics:

  • Boegershausen, J., Datta, H., Borah, A., & Stephen, A. T. (2022). Fields of Gold: Scraping Web Data for Marketing Insights. Journal of Marketing86(5), 1-20.
  • Berger, J., Humphreys, A., Ludwig, S., Moe, W. W., Netzer, O., & Schweidel, D. A. (2019). Uniting the tribes: Using text for marketing insight. Journal of Marketing, 84(1).
  • Hartmann, J., Huppertz, J., Schamp, C., & Heitmann, M. (2019). Comparing automated text classification methods. International Journal of Research in Marketing, 36(1), 20-38.
  • Hennig-Thurau, T., Wiertz, C., & Feldhaus, F. (2015). Does Twitter matter? The impact of microblogging word of mouth on consumers’ adoption of new movies. Journal of the Academy of Marketing Science, 43(3), 375-394.
  • Hartmann, J., Heitmann, M., Siebert, C., & Schamp, C. (2022). More than a feeling: Accuracy and application of sentiment analysis. International Journal of Research in Marketing.
  • Hinz, O., Skiera, B., Barrot, C., & Becker, J. U. (2011). Seeding Strategies for Viral Marketing: An Empirical Comparison. Journal of Marketing, 75(6), 55-71. 

Digital Experimentation:

  • Lambrecht, A. and Tucker, C. E. (2015). Field Experiments in Marketing, Available at SSRN: https:/​/​ssrn.com/​abstract=2630209
  • Gordon, B. R., Zettelmeyer, F., Bhargava, N., Chapsky, D. (2019). A comparison of approaches to advertising measurement: Evidence from big field experiments at Facebook. Marketing Science, 38(2), 193-225.
  • Feit, E. M., & Berman, R. (2019). Test & roll: Profit-maximizing a/b tests. Marketing Science, 38(6), 1038-1058.
  • Timoshenko, A., Hauser, J. R. (2016). Mining and Organizing User-Generated Content to Identify Attributes and Attribute Levels.  In Proceedings of the Sawtooth Software conference.
  • Eggers, F., Sattler, H., Teichert, T., Völckner, F. (2018). Choice-Based Conjoint Analysis. Handbook of Market Research, Springer.

Corporate Digital Responsibility: 

  • Lobschat, L., Mueller, B., Eggers, F., Brandimarte, L., Diefenbach, S., Kroschke, M., & Wirtz, J. (2021). Corporate digital responsibility. Journal of Business Research, 122, 875-888.
Last updated on 15-02-2023