This course provides an introduction to the systematic creation of consumer insights based on large structured and unstructured data that consumers generate in their journey across different channels and touchpoints with companies (e.g., ratings, reviews, web clickstream data, transactions). Students learn about different sources and types of data, about collecting, verifying, and using data for enhanced marketing decision-making. In particular, the course presents a portfolio of tools and techniques that decision makers can use to prepare and transform different data types into adequate information to support marketing decisions. Data visualization tasks offering clear business insights will be specifically emphasized. Students’ work will be application-oriented, as they will analyze business cases and (real) datasets by using software such as JASP, Tableau, DataRobot, Python, and SmartPLS.

This module deals with the principles, methods, and tools of empirical analysis in business administration. It provides an overview of the key concepts of empirical methods in management research, and introduces selected approaches in greater detail, drawing on real-world examples.
Students will learn to identify suitable approaches to answer business-related questions, as well as to critically assess extant empirical analyses. Students will also learn the principles of various statistical methods. During the tutorials, students will apply these approaches and statistical methods to concrete tasks.

The Institute for Marketing will focus on conducting systematic literature reviews, on collecting primary data through surveys, and on discussing important choices in survey design. Particular focus will be put on the measurement of unobservable concepts (e.g., brand image, customer satisfaction, and service experience), which are of great concern in behavioral research. Students will also gain an overview of fundamental methods for validating and processing corresponding measures in empirical research. Furthermore, students will gain insights into company projects and the Institute’s recent research in the field.


This course provides an introduction to the systematic creation of consumer insights based on large structured and unstructured data that consumers generate in their journey across different channels and touchpoints with companies (e.g., ratings, reviews, web clickstream data, transactions). Students learn about different sources and types of data, about collecting, verifying, and using data for enhanced marketing decision-making. In particular, the course presents a portfolio of tools and techniques that decision makers can use to prepare and transform different data types into adequate information to support marketing decisions. Data visualization tasks offering clear business insights will be specifically emphasized. Students’ work will be application-oriented, as they will analyze business cases and (real) datasets by using software such as JASP, Tableau, DataRobot, Python, and SmartPLS.

Course Overview

Discussions on false-positive findings and replicability have transformed the psychological sciences (e.g., Miller and Ulrich 2022; Open Science Collaboration 2015) and are also highly relevant for consumer research, as the field is embedded in a similar academic system and often refers to psychological concepts and theories. Hence, calling seminal psychological findings into question also affects consumer research. Furthermore, initial results on reproducibility in consumer research give rise to concerns in this regard since only about 10% of 34 replication attempts have been replicable (e.g., Data Colada 2022; Motoki and Iseki, 2022).

In the seminar, students will engage in this highly relevant research field and work in groups on a replication study in a predefined topic area (tba). Specifically, students will learn how to conduct a replication study that includes reviewing and assessing prior literature, setting up and conducting the experiment as well as analyzing and discussing its results.

To equip students with the theoretical knowledge to conduct the study, four input sessions will provide basics in empirical research and reproducibility.

The course language is English.

Admission requirements and application

Students wanting to attend the seminar must be enrolled for either the second or a higher semester of the BWL Master (PStO 2018).

Seminar registration will open on March 1, 2023. Please apply here.

Application deadline: April 2, 2023 (11:59 p.m.)

Students who are offered a place at the seminar must confirm the seminar place (please adhere to the deadline indicated in the acceptance mail). Otherwise, the seminar place will transfer to a student on the waiting list.

Please note that registration is binding upon assignment and acceptance of a seminar place.

Important dates and deadlines

  • Application deadline: April 2, 2023 (11:59 p.m.)
  • Kickoff session: April 18, 2023 (04:00 to 06:00 p.m., room 329, Ludwigstr. 28 RB/III)
  • Input sessions: April 25, May 2, May 9, and May 16, 2023 (04:00 to 06:00 p.m., room 329, Ludwigstr. 28 RB/III)
  • Final presentations: July 18, 2023 (09:00 a.m. to 05:00 p.m., room 329, Ludwigstr. 28 RB/III)

Grading

The grading consists of a seminar paper (50 % of the grade) and a presentation (50 % of the grade).


This course provides an introduction to the systematic creation of consumer insights based on large structured and unstructured data that consumers generate in their journey across different channels and touchpoints with companies (e.g., ratings, reviews, web clickstream data, transactions). Students learn about different sources and types of data, about collecting, verifying, and using data for enhanced marketing decision-making. In particular, the course presents a portfolio of tools and techniques that decision makers can use to prepare and transform different data types into adequate information to support marketing decisions. Data visualization tasks offering clear business insights will be specifically emphasized. Students’ work will be application-oriented, as they will analyze business cases and (real) datasets by using software such as SQLITE, Tableau, Rapid Miner, DataRobot, Python, Polinode, and SmartPLS.

Im Rahmen dieser Veranstaltung werden die betriebswirtschaftlichen Grundlagen der Verflechtung von Unternehmen und Märkten behandelt. Die Studierenden erlangen grundlegende Kenntnisse der Funktion von Marketing in Unternehmen, der Analyse von Märkten, lernen die Instrumente des Marketings kennen und entwickeln Fähigkeiten zur Erstellung einer konsequent auf die Marktbedürfnisse ausgerichteten Unternehmensstrategie. Hierbei werden Grundlagen und Handlungsebenen der Unternehmensführung sowie konkrete Strategien einer ressourcen- und marktorientierten Unternehmung vermittelt.