Humboldt University Berlin Institute of Marketing Prof. Dr. Daniel Klapper. Advanced Marketing Modeling Syllabus SS 2015

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1 Humboldt University Berlin Institute of Marketing Prof. Dr. Daniel Klapper Advanced Marketing Modeling Syllabus SS 2015 Course Dates: ectures Wednesday, am 11:45 am, SPA 1, 22 Exercises Thursday, 10:15 am 11:45 am, SPA 1, 22 or 14:15 pm 15:45 pm, SPA 1, 23 Course Description and Objectives: 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. Course Web Page: Course material will be made available in the Moodle system of the Humboldt-University Berlin. Course Reference Materials: There is no assigned (must buy) textbook for the course. The following books will provide you with additional background. 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 anguage, 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,.J. and Schultz, R.. (2003), Market Response Models: Econometric and Time Series Analysis, Kluwer Academic Publishers Group.

2 6. Simulation, Cambridge University Press. 1 st edition is available here: 7. South-Western Cengage earning. Other Course Materials: All of the topics I will cover have been addressed in the marketing, statistics, and economics literature, both theoretically and in practice. Articles and book chapters relevant to each lecture are named below in the table which shows the sessions and content overview. It is expected that you will have done all of the readings prior to class. If you have questions, bring them to class and I will go over them. Course Grading: Your grade in the course will be computed as follows: Weekly, biweekly or monthly assignments 100% For students of the BDPEMS program: Weekly, biweekly or monthly Assignments 70% Take-home case work 30% The deadline for submitting the assignments will announced in class and via the Moodle system. All assignments must be send to daniel.klapper@hu-berlin.de. Some assignments can be done in group of 2-3 students (more details in class), but I will stop this opportunity for every "freerider". Course Software: The majority of computing in the course will be done with SAS. This will include in-class demonstrations and a tutorial how to use SAS. Students of the BDPEMS program must additionally use GAUSS, MATAB, or R for completing their take-home case work. Course Topics: We will cover the following general topics in this course: (1) Marketing models and marketing data (2) Response models for aggregate data (3) Regression analysis and panel data analysis (4) Discrete choice models for individual choice data (5) Discrete choice models for aggregated data (6) The BP-model (7) Examples on modeling marketing phenomena

3 Course: Wednesday, am 11:45 am, SPA 1, 220 Thursday, 10:15 am 11:45 am, SPA 1, 22 or 14:15 pm 15:45 pm, SPA 1, 23 CW Date Slides /E Content and Readings 16 Apr Course ogistics and Introduction to the Course 1 Marketing Models and Marketing Data ilien, G.., P. Kotler and K.S. Moorthy (1992), Marketing Models, Prentice Hall, Chapter1, 1-18, 16 Apr 16 E Course ogistics Introduction to SAS 17 Apr Response Models for ilien, G.., P. Kotler and K.S. Moorthy (1992), Marketing Models, Prentice Hall, Appendix C, Apr 23 E Introduction to SAS 18 Apr Response Models for Hanssens, D.M., Parsons,.J. and Schultz, R.. (2003), Market Response Models: Econometric and Time Series Analysis, Kluwer Academic Publishers Group, Apr 30 E Exercise and Assignment 1 on Response Models for 19 May Regression Analysis Reviewed South-Western Cengage earning, Chapter 2, May 7 E Exercise and Assignment 2 on Response Models for 20 May May 14 Ascension Day 3 Regression Analysis Reviewed South-Western Cengage earning, Chapter 3 and 4, May Regression Analysis Reviewed South-Western Cengage earning, Chapter 3 and 4,

4 21 May 21 E Exercise and Assignment 3 on Response Models for and Regression Analysis 22 May 27 Humboldt Forum Wirtschaft: Alternative meeting: Friday, April 24, Room 026: SAS Course: Data preparation and data manipulation 22 May 28 E Individual Feedback on Group Work Performance on Assignments 1 and 2 23 Jun Panel Data Analysis South-Western Cengage earning, Chapter 14, Jun 4 E Exercise and Assignment 4 on Panel Data Analysis. 24 Jun Discrete Choice Models for Individual Choice Data Simulation, Cambridge University Press, Chapter 3, Jun 11 E Exercise on Discrete Choice Models for Individual Choice Data 25 Jun Discrete Choice Models for Individual Choice Data Simulation, Cambridge University Press, Chapter 4, Simulation, Cambridge University Press, Chapter 6, Jun 18 E Individual Feedback on Group Work Performance on Assignments 3 and 4 26 Jun Discrete Choice Models for Berry, S.T. (1994), Estimating Discrete-Choice Models of Product Differentiation, RAND Journal of Economics, Vol. 25 (2), Jun 25 E Exercise and Assignment 5 on Discrete Choice Models for 27 Jul Discrete Choice Models for 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. ehmann, Cambridge: Marketing Science Institute,

5 Nevo, A. (2000), A Practitioner s Guide to Estimation of Random-Coefficient ogit Models of Demand, in: Journal of Economics & Management Strategy, Vol. 9(4), Modeling Differentiated Product Competition 27 Jul 2 E Exercise and Assignment 6 on Discrete Choice Models for 28 Jul Recapitulation on the BP-Model 28 Jul 9 E Individual Feedback on Group Work Performance on Assignments 5 and 6 29 Jul Jul CW = Calendar week = ecture E = Exercise 7 Examples on Modeling Marketing Phenomena Sudhir, K. (2001), Structural Analysis of Manufacturer Pricing in the Presence of a Strategic Retailer, Marketing Science, 20(3), Draganska, M., Klapper, D. and Villas-Boas, S. (2010), A arger Slice or a arger Pie? An Empirical Investigation of Bargaining Power in the Distribution Channel. Marketing Science, Vol. 29(1), Examples on Modeling Marketing Phenomena Draganska, M. and Klapper, D. (2011), Choice Set Heterogeneity and the Role of Advertising: An Analysis with Micro and Macro Data, Journal of Marketing Research, Vol. 48(August),