• Construct and evaluate simple conceptual models that capture complex consumer attitudes and/or behaviors.
• Generate actionable insights from consumer market analyses.
• Judge the reliability and validity of measurements of consumer attitudes and behaviors.
• Test theories that predict consumer attitudes and/or behaviors.
• Report the insights of consumer analytics in a way that conforms to contemporary scientific standards.

Understanding consumers is the cornerstone of doing business. It provides firms with the ability to tailor their strategies to diverse consumer preferences, behaviors, and trends across different markets and in a dynamic international context. However, unique conceptual, analytical, and strategic challenges arise when managers try to leverage consumer analytics to provide insights into consumer segments and markets. This course provides an introduction to the generation of consumer insights through consumer analytics. It combines substantive theory with applied analytics to generate practical insights.

The course is designed to develop students’ understanding of fundamental concepts and methods in the consumer research domain. It provides hands-on experience with analyzing quantitative consumer-level data, interpreting the results thereof, and ultimately deriving insights and recommendations. The course takes students on a journey through several modules that provide a toolbox for generating consumer insights. A first module focuses on conceptualization. It deals with the formalization of consumer theories that focus on predicting attitudes and/or behaviors. It trains students in developing simple theories that capture complex real-world attitudes and behaviors, where the challenge is that those are often determined by a multitute of other consumer states and traits. A second module introduces methods to identify and analyze consumer markets. It revolves around the identification of sizable and relevant consumer segments. A third module concentrates on assessing the reliability and validity of consumer measures. Those measures are commonly based on self-reports and therefore present unique challenges to consumer analysts. The final module comes full circle and touches on the tenets of testing consumer theories based on self-reports. In sum, the course focuses on developing conceptualization and analytical skills, while training students to derive practical recommendations based on consumer insights. Even though the course focuses on consumer insights, the course topics lend themselves to generating insights into managers and other stakeholders as well.

The course is highly interactive and hands-on, combining in-depth lectures with practical exercises. The exercise classes involve hands-on applications, class discussions, and student presentations of cases and prepared assignments. Continuous feedback is provided in plenum and during exercise classes. Analytics are applied on the freely available “R” analytical software platform, for which previous experience is a plus but not a requirement. The course encourages active use of Artificial Intelligence (AI) such as ChatGPT, for example as a companion data analyst. Therein, it is important to instruct the AI properly, while reflecting on the output quality and assessing its suitability in a consumer context.

After successful completion of this course, students will be prepared to start an ambitious thesis project with a consumer research focus, or in the marketing, management, and psychology domains more generally. Overall, this course trains an essential and transferable skill set that is highly sought after by (inter)national companies.

All course literature is freely available through CBS library.

Chapman, Chris and Elea McDonnell Feit (2019), R for Marketing Research and Analytics (2nd ed.). Cham: Springer International Publishing.

Zaefarian, Ghasem, Vita Kadille, Stephan C. Henneberg, and Alexander Leischnig (2017), "Endogeneity Bias in Marketing Research: Problem, Causes and Remedies," Industrial  Marketing Management, 65, 39-46.

Churchill, Gilbert A. (1979), "A Paradigm for Developing Better Measures of Marketing Constructs," Journal of Marketing Research, 16 (1), 64-73.

Peter, J. Paul (1981), "Construct Validity: A Review of Basic Issues and Marketing Practices," Journal of Marketing Research, 18 (2), 133-45.

Additional readings might be announced while the course progresses.