Dr. Naveen Donthu
CBA 1339 (35 Broad Street)
phone: 651 1043; fax: 651 4198
e-mail: ndonthu@gsu.edu
URL: http://www.gsu.edu/~mktnnd
Course Objectives
1.. Make students better modelers of marketing phenomenon.
2.. Introduce students to statistical and mathematical techniques not likely to
be covered in traditional
"statistics/multivariate" course sequence (e.g., Simulation methods,
Logit/Probit Analysis,
Non-Parametric Methods, Data Envelope Analysis, Conjoint Analysis, MDS, etc.).
3. Survey of models in various areas of marketing (e.g., Pricing, New Products,
Advertising,
Distribution, Salesforce, etc.).
4. Illustrate modeling software (e.g., Conjoint Analyzer, PC-MDS, LIMDEP, QSB,
LINDO, UNIFIT,
etc.).
5. Develop skills and ability to critique marketing literature and define
research problems.
6. Survey latest techniques and trends in marketing research methodology (e.g.,
Database Marketing,
Neural Networks, Scanner Data, etc.).
Class Format
Lectures (about 50%), Discussions, Presentations, Computer Programs, Videos,
etc.
Work Load
1. Read about 4 to 5 papers or chapters per week.
2. Prepare and present about 2 papers per week.
3. Mini class projects/assignments.
4. Final Exam.
5. Final Research paper.
Books / Reading Material
Marketing Models by Lilien, Kotler, and Moorty (Prentice Hall)
Papers (for reading and presentations) will be assigned before each class.
Student Evaluation
1. Class Participation 10%
2. Class Presentations 30%
3. Exam 20%
4. Research Paper and Presentation 40%
Topics
1. Conjoint Analysis
2. Multidimensional Scaling
3. Correspondence Analysis
4. Logit/Probit Models
5. Data Envelopment Analysis
6. Simulation Analysis
7. Time Series Analysis
8. Meta Analysis
9. Operations Research Methods
10. Non-parametric Methods
11. New Product Models
12. Diffusion Models
13. Advertising Models
14. Media Planning Models
15. Pricing Models
16. Salesforce Models
17. Distribution / Logistics Models
18. Location Models
19. Forecasting Models
20. Competition Models
21. Artificial Intelligence, Expert Systems and Neural Networks
22. MIS and Database Marketing
23. Scanner / Panel Data Analysis