Advanced Marketing Modeling

Instructor: 
Time I: 
Wednesday,
10:00am to 12:00pm
Venue I: 
SPA 1, room 22
Description: 

Wednesday 10.00 am ­ 12:00 pm
and Thursday 10:00 am ­ 12:00 pm, or 14:00 pm ­ 16:00 pm

Venue: Wednesday SPA 1, room 22; Thursday SPA 1, room 22 and room 23 (afternoon)

Evaluating marketing decisions and developing goal-oriented marketing strategies, e.g. maximizing firm profits, depend on the measurement of causal relationships between firms objectives and marketing activities.In this course, we discuss in depth advanced methods to empirically determine the causal relationship between marketing activities and firms objectives.In exercise courses students learn how to apply these methods to real data. Special attention is given to modeling the effects of marketing on sales and
market share data. In this course we also focus on discrete choice models for individual purchase data and aggregate sales data. Successful participation in this class will enable students to quantify the impact of marketing on key performance measures and to evaluate the success of marketing activities.

Literature:

1. Anderson, S.P., de Palma A. and Thisse, J.-F. (1992), Discrete
Choice Theory of Product Differentiation, The MIT Press.

2. Cody, R.P. and Smith, J.K. (2006), Applied Statistics and the SAS®
Programming Language, Pearson.

3. Dubin, J. A. (1998), Studies in Consumer Demand ­ Econometric
Methods Applied to Market Data, Kluwer Academic Publishers Group.

4. Franses, P.H. and Paap, R. (2010), Quantitative Models in Marketing
Research, Cambridge University Press.

5. Hanssens, D.M., Parsons, L.J. and Schultz, R.L. (2003), Market
Response Models: Econometric and Time Series Analysis, Kluwer Academic
Publishers Group.

6. Train, K.E. (2009), Discrete Choice Methods with Simulation,
Cambridge University Press. 1st edition is available here:
http://elsa.berkeley.edu/books/train1201.pdf.

7. Wooldridge, J.M. (2008), Introductory Econometrics, South-Western
Cengage Learning.

Exam: Weekly, biweekly or monthly Assignments 70%,
Take-home case work 30%

Credits: 
6.00
Program: 
Semester: 
Spring 2014
Affiliation: 
Humboldt-Universität zu Berlin
End date of the whole course: 
Thursday, July 17, 2014 - 4:00pm