Insegnamento a.a. 2021-2022

Part 1A: Secondary data acquisition for Innovation and Marketing Analytics

- Acquisition of Digital trace data

- Data wrangling

- Basic test analysis

Part 1B: Primary data acquisition for nnovation and Marketing Analytics

- Experimentation

- Conjoint

Part 2: Data analysis using causal inference

- Empirical Analysis of Experiments in Firms

- Demand Estimation

• Understand the concept of digital trace data
• Obtain digital trace data
• Perform data wrangling
• Learn different methodologies to set the price of new products
• Estimate demand

• An understanding of what is digital trace data and how to obtain it

• Familiarity with data wrangle, and visualization

• An understanding of basic text analysis

• Performing traditional marketing research analyses through Big Data
• An understanding of how to run and analyze experiments inside firms
• Ability to estimate demand

• Face-to-face lectures
• Guest speaker's talks (in class or in distance)
• Exercises (exercises, database, software etc.)
• Group assignments

During the course, in addition to face-to-face lectures, the following activities are completed:

• Guest speakers: data science practitioners with experience in experimentation in organizations and demand estimation.  
• Exercises on real data collected by students or provided by the instructors. These exercise allow students to practice the concepts learned in class.
• Group assignment(s) that allows students to use all the knowledge acquired throughout the course.

• We will also ask students to deliver a take-home problem set that will put in practice some of the concepts learned in the class.
• The group project consists of adopting the methodologies learned in class to real company problems. The projects are used to verify the ability of students to apply the knowledge developed during the course and how to present it effectively.
• The exam is held in written form. It is made up of open-ended and multiple-choice questions referring to the concepts, models and cases discussed in class. The open-ended and multiple-choice questions aim to verify learning of the analytical and management abilities and their correct comprehension, and to assess the ability to apply the knowledge that  students learned during the course.

The assessment method for non-attending students is based on a final exam in written form. It is made up of open-ended and multiple-choice questions referring to the concepts, models and cases contained in the textbooks and exam materials. The open-ended and multiple-choice questions aim to verify learning of the analytical and management abilities and their correct comprehension, and to assess the ability to apply the knowledge that students learned when studying the course material.

Class notes and articles from academic journals distributed by the instructors and posted on Bboard.

T.W. MILLER, Marketing Data Science, Pearson.