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2023/2024  KAN-CSAAO1005U  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 cand.merc. and CSAA (CSAA)
Course coordinator
  • Tobias Schäfers - Department of Marketing (Marketing)
Main academic disciplines
  • Marketing
  • Methodology and philosophy of science
  • Statistics and quantitative methods
Teaching methods
  • Blended learning
Last updated on 02-06-2023

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.
  • Apply statistical and visual analytical techniques covered in the course, interpret the results, and derive managerial insights.
  • Follow the academic conventions in their written assignment.
Prerequisites for registering for the exam (activities during the teaching period)
Number of compulsory activities which must be approved (see section 13 of the Programme Regulations): 1
Compulsory home assignments
The student must get 1 out of 2 assignments/activities approved in order to attend the ordinary exam.

1. Individual analytical home assignment: During the course, an individual analytical home assignment is administered that consist of analytical exercises in which students apply the methods and tools that were covered during class. Students report their quantitative findings. Faculty will assess the assignment and provide feedback to students. The purpose of the assignment is to further develop analytical skills of students and let them reflect on the usability of methods and tools. The assignment helps students to prepare for the final exam.

2. Multiple choice test: At the end of the course a mandatory multiple-choice test is administered. The purpose of the test is to provide students with an overview of which topics they master and which not. More specifically, the multiple-choice test examines students’ capabilities with respect to (i) knowledge, (ii) comprehension, (iii) application, and (iv) problem solving for the topics covered in the course. This helps students to better prepare for the final exam.

Students will not have extra opportunities to get the required number of compulsory activities approved prior to the ordinary exam. If a student has not received approval of the required number of compulsory activities or has been ill, the student cannot participate in the ordinary exam.
If a student prior to the retake is still missing approval for the required number of compulsory activities and meets the pre-conditions set out in the program regulations, an extra assignment is possible.
The extra assignment is a 10 page home assignment that will cover the required number of compulsory activities. If approved, the student will be able to attend retake.
Examination
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 3-5
Size of written product Max. 20 pages
Assignment type Case based 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
Course content, structure and pedagogical approach

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 and data visualization; (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 statistical and visual 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, sales activities, 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 and data visualization 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.

 

Analytical techniques that students learn in the course will be based on the open-source statistical platform R. Students do not need to have prior experience with this  program/platform.
Description of the teaching methods
This course is delivered in a blended learning format that combines online material and lectures with in-class discussions. 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 self-assessments), 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 teachers.
Student workload
Teaching 33 hours
Preparation 123 hours
Exam 50 hours
Expected literature

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

 

 

  • Germann, F., Lilien, G.L., & Rangaswamy, A. (2013). Performance implications of deploying marketing analytics. International Journal of Research in Marketing, 30(2), 114-128

 

  • Ransbotham, S.A.M., Kiron, D., & Prentice, P.K. (2015). Minding the analytics gap. MIT Sloan Management Review, 56(3), 63-68.

 

  • Wedel, M., & Kannan, P.K. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97-121.

 

  • Winston, W.L. (2014). Marketing Analytics: Data-Driven Techniques with Microsoft Excel. Somerset: John Wiley & Sons. (Chapters 9, 10, 12, 13, 24, 29)

 

 

 
Last updated on 02-06-2023