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2026/2027  KAN-CEMAV1011U  Brand Analytics

English Title
Brand Analytics

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 150
Study board
Study Board for Markets & Innovation
Programme Cand.merc. - Økonomisk Markedsføring (EMF)
Course coordinator
  • Felix Eggers - Department of Marketing (Marketing)
Main academic disciplines
  • Marketing
  • Statistics and quantitative methods
Teaching methods
  • Blended learning
Last updated on 29-01-2026

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 for branding purposes and learn how and when they can be applied. At the end of the course the student is expected to be able to:
  • Understand, reflect upon, and apply various marketing metrics and brand equity measures
  • Identify relevant qualitative and quantitative information for a given branding problem and select a suitable brand analytics technique
  • Apply suitable brand analytics techniques to collect and analyze data
  • Interpret the outcome of analyses and explain the relationship between brand analytics and strategic brand decisions
  • Understand and reflect upon the connection between brand analytics and financial brand valuation
  • Follow academic conventions in written presentations and be able to clearly communicate insights from brand analytics to a business audience
Course prerequisites
The course makes use of freely available analytical software, especially R. Although students are not required to have prior coding experience, they should be interested in data-driven work and open to learning the fundamentals of programming logic. GenAI tools can be used to assist in generating code. A basic understanding of statistics is expected, with all further methodological and analytical competencies developed throughout the course.
Examination
Brand Analytics:
Exam ECTS 7,5
Examination form Home assignment - written product
Individual or group exam Individual exam
Size of written product Max. 15 pages
The use of AI is permitted.
Assignment type Written assignment
Release of assignment The Assignment is released in Digital Exam (DE) at exam start
Duration 72 hours to prepare
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Winter and 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

The course Brand Analytics offers insights into the multifaceted nature of brand equity, exploring the drivers behind it, and mastering the analytical methods needed to track and manage brands in the digital era. This approach is highly relevant for both large corporations and smaller startups, laying a solid foundation to understand the ways in which brands drive organizational value.


At the heart of the curriculum are analytical and experimental methods for measuring brand value. Students delve into brand image measurement, web scraping, sentiment analysis, and gaining insights into consumer perceptions and online brand presence. Experimental approaches like A/B testing and conjoint analysis are also central, providing tools to understand consumer decision making, for example brand purchases, that can be quantified into monetary brand value. These techniques form a comprehensive toolkit for students to measure and manage brand equity efficiently and effectively and lay the foundation for marketing accountability.


Furthermore, the course also prepares students for emerging trends in digital marketing, including the integration of artificial intelligence. Students learn how GenAI tools and large language models can support the generation and analysis of market research data, for example through synthetic data, enhanced sentiment analysis, and improved identification of brand associations, while also reflecting on their limitations, biases, and ethical challenges. This equips students to adeptly face future opportunities and challenges in the realm of brand analytics and management.

 
Research-based teaching
CBS’ programmes and teaching are research-based. The following types of research-based knowledge and research-like activities are included in this course:
Research-based knowledge
  • Classic and basic theory
  • New theory
  • Teacher’s own research
  • Methodology
  • Models
Research-like activities
  • Data collection
  • Analysis
  • Discussion, critical reflection, modelling
Description of the teaching methods
The teaching will be blended and consists of a mix of prerecorded lectures, in-class lectures, and computer exercises.
Feedback during the teaching period
Students will receive feedback via in-class discussions and during exercises. Additional individual feedback can be obtained after the lectures, during office hours, or individual meetings that can be requested via email.
Student workload
Teaching 30 hours
Preparation 126 hours
Exam 50 hours
Further Information

Part of Minor in Excellence in Brand Strategy & Analytics
Expected literature
  • Keller, K. L. (1993). Conceptualizing, measuring, and managing customer-based brand equity. Journal of Marketing, 57(1), 1-22.
  • Dzyabura, D., & Peres, R. (2021). Visual elicitation of brand perception. Journal of Marketing, 85(4), 44-66.
  • 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), 1-25.
  • Boegershausen, J., Datta, H., Borah, A., & Stephen, A. T. (2022). Fields of Gold: Scraping web data for marketing insights. Journal of Marketing, 86(5), 1-20.
  • 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, 40(1), 75-87.
  • Hartmann, J., Heitmann, M., Schamp, C., & Netzer, O. (2021). The power of brand selfies. Journal of Marketing Research, 58(6), 1159-1177.
  • Eggers, F., Sattler, H., Teichert, T., & Völckner, F. (2018). Choice-based conjoint analysis. In Handbook of Market Research. Springer.
  • Fischer, M., Völckner, F., & Sattler, H. (2010). How important are brands? A cross-category, cross-country study. Journal of Marketing Research, 47(5), 823-839.
  • Kapferer, J.-N. (2012). Chapter 18: Financial valuation and accounting for brands. In The New Strategic Brand Management: Advanced Insights and Strategic Thinking. Kogan Page, Limited.
  • Völckner, F., & Sattler, H. (2006). Drivers of brand extension success. Journal of Marketing, 70(2), 18-34.
  • Arora, N., Chakraborty, I., & Nishimura, Y. (2025). AI–human hybrids for marketing research: Leveraging large language models (LLMs) as collaborators. Journal of Marketing, 89(2), 43-70.
  • Blanchard, S. J., Duani, N., Garvey, A. M., Netzer, O., & Oh, T. T. (2025). EXPRESS: New Tools, New Rules: A Practical Guide to Effective and Responsible GenAI Use for Surveys and Experiments Research. Journal of Marketing.
  • Brand, J., Israeli, A., & Ngwe, D. (2023). Using LLMs for market research. Harvard business school marketing working paper, (23-062).
Last updated on 29-01-2026