Choice comes in many varieties and forms. It can be discrete in the sense of the selection of just one item or multiple items. Since discrete choice modeling (DCM) has been adopted by marketing, it has been used to examine the choices that consumers, households, firms, and other agents make. Using DCM techniques researchers can model how different agents respond to competing products. DCM allows marketers to examine the share impact of product configuration, service bundling, pricing and promotion on customers. For the first time, businesses have a quantitative tool for designing and pricing products to optimize competitive advantage. If you ask marketing researchers of any business what advanced research tools they actually use, chances that discrete choice models will be picked is increasing.
Prof. Dr. Selin Akca
Master students, assigned to “Wahlpflichtbereich BWL 4”
Each spring semester
Work load statement:
|Course attendance||2 Sessions à 9h
2 Sessions à 7h
|Session preparation||4h per session||16h|
|Literature study||7h per session||28h|
|Case Study Presentation and Documentation||14h|
This lecture will focus on the following contents: Choice theory, Binary choice, Multinomial choice, Nested Logit model, Mixed Logit model, Empirical Application: Conjoint Analysis, Empirical Application: Scanner Panel Data.
Train, K. : “Discrete Choice Methods with Simulation”, Cambridge University Press, 2009 (Book can be downloaded from Kenneth Train's website at Berkeley http://eml.berkeley.edu/books/choice2.html).
Manski, C. and D. McFadden (1981), Structural Analysis of Discrete Data with Econometric Applications, MIT Press, Cambridge. (Book can be downloaded from McFadden's website at Berkeley http://elsa.berkeley.edu/users/mcfadden/discrete.html).
Leeflang, P.S.H. / Wittink, D.R. / Wedel, M. / Naert, P.A. : „Building Models for Marketing Decisions”, Springer, 2000.
Marketing Analytics I (Introduction to Data Driven Marketing), Introductory courses to Statistics and/or Empirical Research Methods .
Join our courses and make up your mind if you want to participate. Then officially register using the “Buchungstool” and the registration tools at the University of Zurich.
Presentation, and written documentation of a case study (numerical exercise based on a published paper), and active class participation.
Dates and Location:
Fr. 25.09.2020, 10:00 - 14:00h
Sa. 26.09.2020, 10:00 - 14:00h
Fr. 16.10.2020, 10:00 - 14:00h
Sa. 17.10.2020, 10:00 - 14:00h
Fr. 06.11.2020, 10:00 - 14:00h
Fr. 20.11.2020, 13:00 - 16:00h
Fr. 4.12.2020, 13:00 - 16:00
Location: tba "Vorlesungsverzeichnis"
Before you’ll come to our class, please visit
the Comprehensive R Archive Network and take care that you have successfully installed R 3.1.0. Please also find the GUI R Studio 2.98 on https://www.rstudio.com and install it onto your personal laptop.
Don’t forget to officially register yourself using the registration tools at the University of Zurich.
This information in the syllabus supports the official information in the electronic university calendar (VVZ – Vorlesungsverzeichnis). In cases of doubt, the official information at the VVZ is valid.