Facebook pixel
Info
Foto sezione
Logo Bocconi

Course 2021-2022 a.y.

20593 - INNOVATION AND MARKETING ANALYTICS

DSBA
Department of Marketing

Course taught in English

Go to class group/s: 23

DSBA (6 credits - II sem. - OB  |  SECS-P/08)
Course Director:
QIAONI SHI

Classes: 23 (II sem.)
Instructors:
Class 23: QIAONI SHI


Suggested background knowledge

To feel comfortable in this course you should know basic Python and Stata.


Mission & Content Summary
MISSION

This course is offered in the second semester of the MSc in Data Science and Business Analytics (DS&BA). By then, students have deep knowledge of different programming languages such as Python and R, as well as of statistical models to identify correlational and causal relations in data. The course is divided in two main parts. In the first part, students will learn how to gather data that can be used to conduct innovation and marketing activities. In the second part, students will learn how to analyze this data using frontier of research statistical techniques.

CONTENT SUMMARY

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

 

 


Intended Learning Outcomes (ILO)
KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
  • 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
APPLYING KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
  • 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

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

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.

Assessment methods
  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
  •     x
  • Group assignment (report, exercise, presentation, project work etc.)
  •     x
    ATTENDING STUDENTS
    • 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.
    NOT ATTENDING STUDENTS

    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.


    Teaching materials
    ATTENDING STUDENTS

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

    NOT ATTENDING STUDENTS

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

    Last change 19/12/2021 03:41