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2018/2019  KAN-CCUSO2001U  Business Intelligence and Customer Insight

English Title
Business Intelligence and Customer Insight

Course information

Language English
Course ECTS 7.5 ECTS
Type Mandatory
Level Full Degree Master
Duration One Quarter
Start time of the course Third Quarter
Timetable Course schedule will be posted at calendar.cbs.dk
Study board
Study Board for MSc in Economics and Business Administration
Course coordinator
  • Tobias Schäfers - Department of Marketing (Marketing)
Main academic disciplines
  • Marketing
Teaching methods
  • Blended learning
Last updated on 17-12-2018

Relevant links

Learning objectives
  • Select, explain and apply relevant key terms, definitions, concepts, theories, and frameworks covered in the course to understand and describe marketing analytics, customer insights, and business intelligence.
  • Describe how companies can efficiently and effectively employ marketing analytics.
  • Critically assess the goal-oriented use of marketing analytics to create customer insights and improve business intelligence.
  • Follow the academic conventions in their written assignment.
Business Intelligence and Customer Insight:
Exam ECTS 7,5
Examination form Home assignment - written product
Individual or group exam Group exam
Please note the rules in the Programme Regulations about identification of individual contributions.
Number of people in the group 3-4
Size of written product Max. 20 pages
Assignment type Case based assignment
Duration 2 weeks to prepare
Grading scale 7-step scale
Examiner(s) Internal examiner and external examiner
Exam period Winter
Make-up exam/re-exam
Same examination form as the ordinary exam
Course content and structure

The aim of the course is to gain an understanding of how marketing analytics can be used to create customer insights and thereby improve business intelligence to allow for more effective and efficient marketing activities. Specifically, the course aims at (1) providing students with knowledge about different types of marketing analytics; (2) giving students an understanding of the core processes, frameworks, and techniques used in marketing analytics; (3) providing students with basic skills to apply different types of analytical techniques and interpret the results; and (4) providing students with knowledge about how different companies employ marketing analytics to create customer insights and business intelligence.


Companies today are facing oceans of data about, for example, customers, transactions, markets, or competitors. These data offer numerous opportunities to inform marketing decision-making by providing insights and creating business intelligence. At the same time, however, the risk of information overload has substantially increased. In order to shift from intuitive decision-making to fact-based decision processes, marketers need to adopt an analytical marketing approach.


This course will give students a deeper understanding of how marketing analytics can be used to create customer insights and thereby improve business intelligence to allow for more effective and efficient marketing activities.


Students will learn how to apply different approaches to marketing analytics; learn how to use digital tools, techniques, and frameworks essential for transforming data into relevant information; learn how marketing analytics can help companies to understand not only how customers have behaved in the past, but also to make accurate predictions about how customers will behave in the future, which in turn can help to optimize marketing activities.

Description of the teaching methods
This course is delivered in a blended learning format that combines online material and lectures with in-class discussions and workshops. Blended learning creates a powerful learning environment for students, which we intend to use to its fullest potential. The course consists of online lectures and materials, online activities (e.g., online tasks, peer graded assignments), as well as on-campus group work and in-class discussion. The class is highly interactive both online and offline with a corresponding expectation that students engage in these interactions.
Feedback during the teaching period
Quizzes are used to give students a better overview of whether they are following the expected learning curve. During the online and offline sessions students will get feedback from peers and teachers.
Student workload
Teaching 33 hours
Preparation 123 hours
Exam 50 hours
Expected literature

Text collection and research papers (Indicative literature - more literature and required readings will be announced upon enrollment):


  • Hanssens, Dominique M. and Koen H. Pauwels (2016), "Demonstrating the Value of Marketing," Journal of Marketing, 80 (6), 173-190.


  • Linoff, Gordon S. and Michael J.A. Berry (2011), Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, 3rd ed. Indianapolis: Wiley.


  • Pauwels, Koen H. (2014), It's Not the Size of the Data - It's How You Use It: Smarter Marketing with Analytics and Dashboards. New York: American Marketing Association.


  • Provost, Foster and Tom Fawcett (2013), Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking. Sebastopol, CA: O'Reilly.


  • Venkatesan, Rajkumar, Paul Farris and Ronald T. Wilcox (2015), Cutting-Edge Marketing Analytics: Real World Cases and Data Sets for Hands on Learning. Upper Saddle River, NJ: Pearson.


  • Wedel, Michel and P. K. Kannan (2016), "Marketing Analytics for Data-Rich Environments," Journal of Marketing, 80 (6), 97-121.


Last updated on 17-12-2018