Quantitative Marketing Research and Statistical Data Analysis

Quantitative Marketing Research

Objectives

Marketing professionals rely on information gathered through marketing research to determine the course of action of a marketing plan. Leveraging upon research to know more about customers, consumers, and citizens is crucial for effective marketing decisions.
In this Course, students will learn how to be smart producers and consumers of information, when it comes to marketing research. They will also learn to be more effective marketing decision makers. Specifically, this course will enable students to:

(1) manage sampling methods for quantitative marketing;
(2) familiarize with key quantitative marketing research methods and associate them with marketing problems they are suited to answer;
(3) become knowledgeable about the most important quantitative marketing research tools (survey and experiment);
(4) understand neuromarketing’s core concepts and tools;
(5) prepare and understand a research brief and a research report suitable to support strategic marketing decisions.

Contents
The Course is organized interactively, with video-recorded lectures and exercises, and an individual project. It also offers self-assessment moments.

Assessment

50% individual project
50% exam – online “open-book” exam

References

K. MALHOTRA NARESH, Marketing Research: An Applied Orientation, Pearson Education. (selected chapters).

BERGER, J. ET AL. (2020), “Uniting the Tribes: Using Text for Marketing Insight”, Journal of Marketing, 84(1), pp. 1-25.
HARREL, E. (2019), “Neuromarketing: What you need to know”, Harvard Business Review, 64-70.

Statistical Data Analysis

This course addresses market research design, data collection, and data interpretation based-upon quantitative data and techniques.
Methods that are specifically developed along the course include questionnaire design and survey, experiments, and panel data. Data analysis familiarizes participants with statistical data analysis - the art of examining, summarising and drawing conclusions from data. This includes the organisation of a coherent database and its use to produce statistical summaries and inference. Statistical software is essential in this respect. The course builds on students´ knowledge of introductory level statistics, such as frequency, distribution and correlation and
introduces new topics like hypothesis testing on means, percentage and regression, multiple regression, logistic regression, factor analysis and cluster analysis