28208 - Marketing Models

Academic Year 2020/2021

  • Docente: Sara Valentini
  • Credits: 10
  • SSD: SECS-P/08
  • Language: Italian
  • Moduli: Sara Valentini (Modulo 1) Elisa Montaguti (Modulo 2) Annamaria Tuan (Modulo 3)
  • Teaching Mode: Traditional lectures (Modulo 1) Traditional lectures (Modulo 2) Traditional lectures (Modulo 3)
  • Campus: Bologna
  • Corso: Second cycle degree programme (LM) in Statistics, Economics and Business (cod. 8876)

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.

To do so, we will use, among others, the following statistical methods:

-multiple regression

-logistic regression

-multinomial and conditional logit

-nested models.

 

Readings/Bibliography

  1. Teacher's notes
  2. Blattberg R.C, B. Kim e S.A. Neslin “Database Marketing,” Springer 2008 pp 105-117; pp-245-287; Capitolo 10 – The Predictive Modeling Process; Capitolo 11 – Statistical Issues in Predictive Modeling; Capitolo 24 – Managing Churn

  3. Peter S. Fader e Bruce S. Hardie (2012) “Reconcyling and Clarifying CLV Formulas” pp-1-9.

  4. 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 Chapters: "Model Specification" ; “Data”; Individual Demand Models

  5. Scott A. Neslin and Harald J. van Heerde (2009), "Promotion Dynamics", Foundations and Trends® in Marketing: Vol. 3: No. 4, pp 178-199; 214-224.

  6. Lattin, James, Douglas Carroll and Paul Green “Analyzing Multivariate Data” Capitolo 13.

  7. 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 (JMR) . Nov91, Vol. 28 Issue 4, p417-428

  8. De Vries, Lisette, Sonja Gensler, and Peter SH Leeflang. "Popularity of brand posts on brand fan pages: An investigation of the effects of social media marketing." Journal of interactive marketing 26.2 (2012): 83-91.

  9. Gensler, Sonja, Peter C. Verhoef, and Martin Böhm. "Understanding consumers’ multichannel choices across the different stages of the buying process." Marketing Letters 23.4 (2012): 987-1003

  10. Case Harvard Business Review: Pilgrim Bank (A), (B), (C). it can be purchased at the following link  link: https://hbsp.harvard.edu/import/62840

  11. Aaker, Kumar, Day Leone “Marketing Research” ed. 10th, chapter 13

Teaching methods

    The course involves both lectures and weekly lab sessions   

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

Assessment methods

The course assessment is based on a written exam.

Students will have the opportunity to carry out non mandatory assignments that can contribute to the final assessment for no more that 10%.

PLEASE NOTE THAT THE EXAMS PLANNED FOR SEPTEMBER 2020 WILL BE HELD REMOTELY 

Office hours

See the website of Sara Valentini

See the website of Elisa Montaguti

See the website of Annamaria Tuan