28208 - Marketing Models

Course Unit Page

SDGs

This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.

Quality education Decent work and economic growth Responsible consumption and production

Academic Year 2021/2022

Learning outcomes

At the end of the course students will be able to analyze consumer behavior while using both individual and aggregate data. More specifically students will be able to specify and estimate marketing models aiming at supporting decision making for targeting, marketing mix activities and planning.

 

Course contents

This course will cover important marketing phenomena such as:

-targeting

-market/consumer response to sales promotions

-market/consumer response to communications

-brand and product category choice

while analyzing data collected both at the individual and aggregate (e.g. market) level.

In Module 1 topics will be convered by means of a quantitative approach. To do so, we will use, among others, the following statistical methods:

-multiple regression

-logistic regression

-multinomial and conditional logit

-nested models.

During Module 2 topics wil be covered with a qualitative approach through case studies and practical experiences. 

Readings/Bibliography

  1. Teacher's notes
  2. Leeflang, P., Wieringa, J.E., Bijmolt, T.H.A., Pauwels, K.H “Modeling Markets Analyzing Marketing Phenomena and Improving Marketing Decision Making” International Series in Quantitative Marketing 2015: Capitolo 2 "Model Specification"; Capitolo 3 “Data”; Capitolo 8 “Individual Demand Models”; Capitolo 9 “Examples of Database Marketing Models”.

    3. Blattberg R.C, B. Kim e S.A. Neslin “Database Marketing,” Springer 2008: Capitolo 10 “The Predictive Modeling Process”; Capitolo 11 “Statistical Issues in Predictive Modeling”; Capitolo 24 “Managing Churn”

    4. Chintagunta, Pradeep K.; Jain, Dipak C.; Vilcassim, Naufel J (1991) “Investigating Heterogeneity in Brand Preferences in Logit Models for Panel Data” Journal of Marketing Research, 28(4), p. 417-428

    5. De Vries, Lisette, Sonja Gensler, and Peter SH Leeflang. (2012) "Popularity of brand posts on brand fan pages: An investigation of the effects of social media marketing." Journal of Interactive marketing, 26(2), 83-91.

    6. Gensler, Sonja, Peter C. Verhoef, and Martin Böhm. (2012) "Understanding consumers’ multichannel choices across the different stages of the buying process." Marketing Letters, 23(4), 987-1003.

    7. Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69-96.

    8. Visentin, M., Tuan A., Di Domenico G. (2021). "Words matter: How privacy concerns and conspiracy theories spread on twitter." Psychology & Marketing 38(10):1828-1846.

    9. Labrecque, L. I., Swani, K., & Stephen, A. T. (2020). The impact of pronoun choices on consumer engagement actions: Exploring top global brands' social media communications. Psychology & Marketing, 37(6), 796-814

    10. Pezzuti, T., Leonhardt, J. M., & Warren, C. (2021). Certainty in language increases consumer engagement on social media. Journal of Interactive Marketing, 53, 32-46

     

Teaching methods

    The course involves both lectures and weekly lab sessions   

During the lab several software will be used including:  STATA and R

Assessment methods

The course assessment is based on a written exam. 

Office hours

See the website of Annamaria Tuan

See the website of Fabio Rizzi