Quantitative Methods in Marketing

Recommended Prerequisites

Basic knowledge of statistics.

Teaching Methods

We start by presenting the basic concepts and the types of problems to which the methodologies can be applied. Then, the key ideas are introduced in an intuitive way. The tools for applying these methodologies are then presented, and the obtained results are discussed. Some class time will be used to support the students in carrying out small projects.

Learning Outcomes

The purpose of this course is to present several quantitative methodologies that can be used to analyze and deal with problems arising in the field of marketing. By the end of the course it is expected that the student should be able to apply such methodologies to problems of limited complexity. Particularly, the student should be able to:

• critically apply some machine learning techniques to problems in the field of marketing, using computational tools;

• perform configurational analysis based on fuzzy sets (fsQCA), using computational tools;

• determine the optimal decisions in situations involving uncertainty, resorting to decision trees;

• determine the value of information in situations involving undertainty.

Work Placement(s)

No

Syllabus

Fundamental concepts of machine learning. Data analysis with machine learning, resorting to computational tools. An introduction to fsQCA. Configurational analysis using fsQCA. Decision trees for decision making under uncertainty. The value of information.

Head Lecturer(s)

Pedro Manuel Cortesão Godinho

Assessment Methods

Assessment
Periodic or by final exam as given in the course information): 100.0%

Bibliography

BRINK, Henrik, Richards, J. W., Fetherolf, M., & Cronin, B. Real-world machine learning. Manning, 2017.

GOODWIN, P., & Wright, G. Decision analysis for management judgment, 5th edition, Wiley, 2014.

RAGIN, Charles C. Redesigning social inquiry: Fuzzy sets and beyond. University of Chicago Press, 2008. [303 RAG]

RENDER, B., Stair, R., Hanna, M., & Hale, T. Quantitative Analysis for Management, 13th edition, Pearson, 2017.

WITTEN, I. H., Frank, E., Hall, M. A., & Pal, C. J. Data Mining: Practical machine learning tools and techniques, 4th edition. Morgan Kaufmann, 2016.

BLATTBERG, Robert C. Byung-Do Kim, & Scott A. Neslin. Database Marketing: Analyzing and Managing Customers. International Series in Quantitative Marketing, 2008.