20593 - INNOVATION AND MARKETING ANALYTICS
Course taught in English
Go to class group/s: 23
Lezioni della classe erogate in forma blended (in parte online e in parte in presenza)
To feel comfortable in this course you should have good knowledge of Python.
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 ideally divided in two main parts. In the first part, students will learn how to gather primary and secondary data that can be used to conduct innovation and marketing activities. In the second part, students will learn how to analyze this data with the main statistical techniques involved in the development and marketing of new products.
Part 1A: Secondary data acquisition for Innovation and Marketing Analytics
- Market research with Twitter data
- Code profiling & remote servers
- Web scraping
Part 1B: Primary data acquisition for nnovation and Marketing Analytics
Part 2: Analyses related to Innovation (new product design and management) and Marketing
- Customer segmentation analysis
- Pricing analysis
- Demand forecasting
- Familiarize students with the concept of API and web scraping
- Recognize the main strategic and marketing issues that a company faces during the entire new product development process.
- Estimate competition among firms and the optimal positioning of a new product
- Segment the market through unsupervised machine learning algorithms
- Learn different methodologies to set the price of new products
- Forecast market demand for a new product
- Identify which data are needed in the different phases of the new product development process.
- Effectively scrape data from different sources, like Twitter, Facebook or Google.
- Performing traditional marketing research analyses through Big Data.
- Using integrated analytics to monitor competition among firms, predict future trajectories within the market, and identify emerging trends.
- Face-to-face lectures
- Online 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: managers and entrepreneurs on the topics of innovation.
- 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.
|Continuous assessment||Partial exams||General exam|
- The group project consists of adopting the methodologies learned in class to a marketing problem. The project is presented in detail at the beginning of the semester along with the assessment criteria. This project is 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.