88184 - Laboratory Of Marketing Analytics

Course Unit Page

  • Teacher Elisa Montaguti

  • Credits 3

  • SSD SECS-P/08

  • Teaching Mode Traditional lectures

  • Language Italian

  • Campus of Forli

  • Degree Programme Second cycle degree programme (LM) in Economics and management (cod. 9203)

  • Course Timetable from Apr 17, 2024 to May 23, 2024

Academic Year 2023/2024

Learning outcomes

This course will provide you with an introduction to the main analytical tools to support marketing decisions. We will study various tools for generating marketing insights from empirical data in such areas as segmentation, targeting and positioning, customer lifetime analysis, product and price decisions using conjoint analysis. This will be a hands-on course, in which you apply the tools studied to actual business situations. You will also complete a group project in which you collect and analyze data or introduce your fellow students to a marketing analytics technique not studied in class.

By the end of the course, you will be able to: analyze the structure of a market and identify the main segments; represent how consumer perceive products in a market; calculate the CLTV.

Course contents

-The concept of value for the customer and the firm

-Customer Value and measurement of the CLTV

-Modelling market response

-Consumer perception and product positioning: building perceptual maps


1)Fader, Peter, Bruce Hardie e Michael Ross “The customer-base audit” Wharton School Press, 2022. Capitoli 1 e 2.

2)Sunil Gupta, Dominique Hanssens, Bruce Hardie, Wiliam Kahn, V. Kumar, Nathaniel Lin, Nalini Ravishanker, and S. Sriram (2006) “Modeling Customer Lifetime Value” Volume 9, Issue 2

3) Eva Ascarza, (2018), “Retention futility: Targeting high-risk customers might be ineffective”. Journal of Marketing Research, 2018

4) Duncan Simester, Artem Timoshenko, Spyros I. Zoumpoulis (2019) Targeting Prospective Customers: Robustness of Machine-Learning Methods to Typical Data Challenges. Management Science 66(6):2495-2522

5) Case: Artea: Designing Targeting Strategies Eva Ascarza; Ayelet Israeli

Optional material: 

6) Blattberg R.C, B. Kim e S.A. Neslin “Database Marketing,” Capitoli 5 e 6.

7) Charan, Ashok, "Marketing Analytics" Chapter 1

Teaching methods

The course involves both lectures and weekly lab sessions

During the lab the following software will be used: Excel and SPSS

Assessment methods

The course assessment is based on a written exam in which both students' understanding of the theoretical constructs and their ability to use and comment on the above mentioned methodologies will be evaluated.

The assessment of the written exam will be based on the following grid: 

La graduazione dei punteggi nella prova scritta seguirà la seguente logica:

<18 failed

18-23 sufficient

24-27 good

28-30 very good

30 e lode honors


Students have the opportunity to carry out a final project that can contribute to the final assessment up to 40%

Teaching tools

See the website of Elisa Montaguti [https://www.unibo.it/sitoweb/elisa.montaguti]

Office hours

See the website of Elisa Montaguti