MG4F2      Half Unit
Marketing Analytics II: Analytics for Managing Innovations, Products and Brands

This information is for the 2019/20 session.

Teacher responsible

Prof Om Narasimhan NAB.5.06


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.


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


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

Guidelines for interpreting course guide information

Personal development skills