This information is for the 2019/20 session.
Teacher responsible
Prof Om Narasimhan NAB.5.06
Availability
This course is available on the CEMS Exchange, Global MSc in Management, Global MSc in Management (CEMS MiM), Global MSc in Management (MBA Exchange), MBA Exchange, MSc in Management (1 Year Programme), MSc in Marketing and MSc in Strategic Communications. This course is available with permission as an outside option to students on other programmes where regulations permit.
Course content
Marketing managers make ongoing decisions about product features, prices, advertising (online and offline), distribution, sales compensation plans, and so on. In making these decisions, managers choose from among alternative courses of action in a complex and uncertain world. Increasingly, in this age of Big Data, companies that emerge as market leaders tend to be the ones that employ sophisticated Marketing Analytics. This course in Marketing Analytics will entail a deep-dive into the state-of-the-art Marketing Analytics models that allow managers to make scientific decisions regarding launching new products or innovations and managing more mature products and brands.
This course will focus upon the use of cutting-edge data analytic techniques to understand and inform managerial decision making with a primary focus on the formulation of dynamic marketing policies. The course is structured to enable the student to gain familiarity with techniques for scraping the web for data, sentiment analysis, multivariate regression, discrete choice modelling, probability models for customer management, causal inference through A/B testing, classification and regression trees, and introductory machine learning.
Teaching
30 hours of lectures in the LT.
Formative coursework
Students will be engaged in analysing a number of data sets using the techniques learned in class. This will set the stage for their group project (gathering and analysing data) as well as the take-home assignment (which will involve analysing data sets given to them).
Indicative reading
Assessment
Take-home assessment (45%), group project (45%) and class participation (10%) in the LT.
The Individual take-home assignment is due within 1 week of when it is assigned.
Key facts
Department: Management
Total students 2018/19: 68
Average class size 2018/19: 69
Controlled access 2018/19: No
Value: Half Unit
Personal development skills