- Docente: Elisa Montaguti
- Credits: 10
- SSD: SECS-P/08
- Language: Italian
- Moduli: Elisa Montaguti (Modulo 1) Sara Valentini (Modulo 2)
- Teaching Mode: Traditional lectures (Modulo 1) Traditional lectures (Modulo 2)
- 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
- Teacher's notes
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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
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Peter S. Fader e Bruce S. Hardie (2012) “Reconcyling and Clarifying CLV Formulas” pp-1-9.
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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
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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.
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Lattin, James, Douglas Carroll and Paul Green “Analyzing Multivariate Data” Capitolo 13.
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S Chib, PB Seetharaman, A Strijnev(2004) “Model of brand choice with a no-purchase option calibrated to scanner-panel data”, Journal of Marketing Research, (41), May, 184-196.
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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
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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.
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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
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Case Harvard Business Review: Pilgrim Bank (A), (B), (C). it can be purchased at the following link link: https://hbsp.harvard.edu/import/62840
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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%.
Office hours
See the website of Elisa Montaguti
See the website of Sara Valentini