20723 - MARKETING ANALYTICS

Current business environments demand more analysis and rigor in marketing decision making. Modern marketing decision making requires an analytics approach to be correctly implemented, putting together concepts, data, analyses, and simulations to learn about the marketplace and design effective marketing plans. The course aims to prepare the students to handle the most important marketing decisions by using the principles of marketing analytics, which imply: i) understanding the meaning of the decision; ii) collect the right data to inform the choice; iii) and perform the quantitative analyses to make better marketing plans, better product designs, and better marketing decisions. The course is based on an integrated software platform that combine, for each key marketing decision, book materials, datasets, analytical models, and business cases, all simultaneously available to students.

The course is based on applying marketing analytics principles to a set of strategic marketing decisions:

1 - Customer Value Assessment (CLV)

2 - Segmentation and Targeting 

3 - Market positioning

4 - Forecasting model for product diffusion

5 - Product choice: conjoint models

6 - Price setting

7 - Promotion budget optimization

8 - Sentiment analysis

All these topics will be treated conceptually (what is), analytically (what data and analysis are needed) and empirically (what decisions should be concretely taken). 

• Understand the conceptual roots of key marketing decisions
• Learn which data and information are necessary to set up an analytical decision making process
• Know how to analyze quantitative data to arrive at a profitable decision

• Identify the challenges associated to the key strategic marketing decisions
• Set up and frame the data to inform a marketing decision making process
• Make sense of the collected data mastering the proper implementation of quantitative analyses
• Synthesize and craft a systematic reporting to support marketing decision making

• Face-to-face lectures
• Exercises (exercises, database, software etc.)
• Case studies /Incidents (traditional, online)
• Group assignments

In addition to face-to-face lecturers, this course includes:

- Exercises: all marketing decisions will be taught using datasets and analytical models integrated in the online platform available to students;

- Case studies on real decision making problems will be discussed to appreciate the meaningfulness of the models and the analytical tools learned in the course;

- Case-group assignments: at the end of the course, students will gather in groups and engage in a real decision making problem, that they have to empirically solve and then present the solution to the class

Individual exam on selected parts and group assignments.

Full individual exam

• Gary L. Lilien; Arvind Rangaswamy; Arnaud De Bruyn (2017) "Principles of Marketing Engineering and Analytics" 3rd Edition.

Topics of the syllabus discussed in class

• Gary L. Lilien; Arvind Rangaswamy; Arnaud De Bruyn (2017) "Principles of Marketing Engineering and Analytics" 3rd Edition .

All content of the syllabus