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Course 2016-2017 a.y.

20506 - MARKETING DECISIONS


IM
Department of Marketing

Course taught in English


Go to class group/s: 6 - 7

IM (6 credits - II sem. - OB  |  ING-IND/35)
Course Director:
JOACHIM VOSGERAU

Classes: 6 (II sem.) - 7 (II sem.)
Instructors:
Class 6: JOACHIM VOSGERAU, Class 7: JOACHIM VOSGERAU


Course Objectives
This course is designed to make you a better decision maker, for marketing problems and in general. Good decision makers know how to recognize decision problems, how to represent the essential structure of the decision situation, and how to analyze the problem with the formal tools based on decision theory. Decision makers need to be able to think effectively about the inputs into a decision analysis, whether to trust the analysis, and how to use the outputs to guide actions by themselves and their firms. The course covers formal (e.g., factor and cluster analysis, conjoint analysis) and behavioral decision making tools (principles of causal inference, avoidance of biased decision making) which are applied to marketing-specific contexts (e.g., pricing, segmentation). You learn to use these decision making tools in a research group project. The project involves collecting data and analyzing it with the statistical software package SPSS.

Intended Learning Outcomes
Click here to see the ILOs of the course

Course Content Summary
  • Managerial Decision Making in Marketing.
  • Learn how to apply decision making tools: Statistical tools, research tools, and behavioral decision tools.
  • Factor analysis.
  • Cluster analysis.
  • Segmentation using factor and cluster analysis.
  • Conjoint analysis.
  • Causal Inference-how to interpret data and statistical results.
  • Measuring consumer preferences.
  • Learn how to avoid decision making biases: Confirmation bias, overconfidence, bias blind spot, and anchoring.
  • Learn how to collect data, analyze it, and interpret the results.

Teaching methods
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Assessment methods
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Detailed Description of Assessment Methods
Students evaluations rules are detailed at the beginning of the course and available in the Syllabus of the course.

Textbooks
Attending and non-attending students:
  • Articles and papers selected by the instructor and indicated on Blackboard.
  • slides uploaded on Blackboard.
  • datasets uploaded on Blackboard.
  • note on the design and use of regression (see pages 8-17 of the syllabus).

Prerequisites
Basic knowledge of statistics including t-tests, cross-tabs and chi-square tests, ANOVA, and linear regression.
Students need SPSS and EXCEL on their laptops.
Last change 13/06/2016 09:37