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

This information is for the 2020/21 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

  • Lilien GL, Kotler Ph, Moorthy KS. Marketing Models. Prentice Hall: Englewood Cliffs, 1992
  • Leeflang PSH, Wittink DR, Wedel M, Naert PA. Building Models for Marketing Decisions. Kluwer Academic Publishers: Dordrecht / Boston 2000.
  • Hanssens DM, Parsons LJ, Schultz RL. Market Response Models: Econometric and Time Serie Analysis. Kluwer Academic Publishers, Boston 2001.
  • Lilien GL, Rangaswamy A. Marketing Engineering, 2nd edition. Prentice Hall: Upper Saddle River, NJ, 2003.
  • Little JDC. Models and Managers: The Concept of a Decision Calculus. Management Science 1970; 16: B466-B485.


Take-home assessment (55%) and group project (45%) in the LT.


Coursework is an Individual Take-home assignment and the project will be in groups.

Important information in response to COVID-19

Please note that during 2020/21 academic year some variation to teaching and learning activities may be required to respond to changes in public health advice and/or to account for the situation of students in attendance on campus and those studying online during the early part of the academic year. For assessment, this may involve changes to mode of delivery and/or the format or weighting of assessments. Changes will only be made if required and students will be notified about any changes to teaching or assessment plans at the earliest opportunity.

Key facts

Department: Management

Total students 2019/20: 67

Average class size 2019/20: 67

Controlled access 2019/20: Yes

Value: Half Unit

Guidelines for interpreting course guide information

Personal development skills

  • Leadership
  • Self-management
  • Team working
  • Problem solving
  • Application of information skills
  • Communication
  • Application of numeracy skills
  • Commercial awareness
  • Specialist skills