Advanced Marketing Modeling

Instructor: 
Time I: 
Wednesday,
12:15pm to 01:45pm
Time II: 
Thursday,
12:15pm to 01:45pm
Venue I: 
HU Berlin, Spandauer Str. 1, Room 22
Venue II: 
HU Berlin, Spandauer Str. 1, Room 22
Description: 

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:
Berry, S.T. (1994), Estimating Discrete-Choice Models of Product Differentiation, RAND Journal of Economics, Vol. 25 (2), 242-262.
Wooldridge, J.M. (2008), Introductory Econometrics, South-Western Cengage Learning, Chapters 2, 3 and 4, 68-166.
Chintagunta, P., V. Kadiyali and N. Vilcassim (2004), Structural Models of Competition: A Marketing Strategy Perspective, Assessing Marketing Strategy Performance, eds. C. Moorman and D. Lehmann, Cambridge: Marketing Science Institute, 95-113.
Nevo, A. (2000), A Practitioner’s Guide to Estimation of Random-Coefficient Logit Models of Demand, in: Journal of Economics & Management Strategy, Vol. 9(4), 513-548.
Train, K.E. (2009), Discrete Choice Methods with Simulation, Cambridge University Press, Chapter 3, 4, 6, 8, 9, 10.
https://onlinecourses.science.psu.edu/stat501/node/2
Anderson, S.P., de Palma A. and Thisse, J.-F. (1992), Discrete Choice Theory of Product Differentiation, The MIT Press.
Dubin, J. A. (1998), Studies in Consumer Demand – Econometric Methods Applied to Market Data, Kluwer Academic Publishers Group.
Franses, P.H. and Paap, R. (2010), Quantitative Models in Marketing Research, Cambridge University Press.
Hanssens, D.M., Parsons, L.J. and Schultz, R.L. (2003), Market Response Models: Econometric and Time Series Analysis, Kluwer Academic Publishers Group.
Leeflang, P.S.H, Wieringa, J.E., Bijmolt, T.H.A and Pauwels, K.H. (2015), Modeling Markets – Analyzing Marketing Phenomena and Improving marketing Decision Making, Springer.
Train, K.E. (2009), Discrete Choice Methods with Simulation, Cambridge University Press. 1st edition is available here: http://elsa.berkeley.edu/books/train1201.pdf.
Verboven, F. (1996), International Price Discrimination in the European Car Market. RAND Journal of Economics, 27(2), 240–268.
Wooldridge, J.M. (2008), Introductory Econometrics, South-Western Cengage Learning.

Exam:
30% of the grade base on a written assignment of 5 pages about estimating price elasticities and promotion effects on the basis of store-level scanner using the econometric knowledge from this class that must be submitted after the last class. 70% of the grade base on 4 special work performances of 5 pages each that must be submitted during the semester.

Credits: 
6.00
Program: 
Semester: 
Spring 2019
Affiliation: 
Humboldt-Universität zu Berlin