20593 - INNOVATION AND MARKETING ANALYTICS
Course taught in English
Go to class group/s: 23
Class-group lessons delivered on campus
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 has two main goals. First, to augment students’ knowledge with social media listening, namely the collection and analysis of data that comes from social networks such as Twitter and Instagram. Second, to guide students through the new product development process and the main challenges of innovation, a key driver of the long-term success of any business. The course teaches students how to conduct all the traditional steps involved in the new product development process and the related marketing research with data from social networks and by adopting Machine Learning algorithms.
This course covers the entire process of innovation and development of new business ideas.
- The first part focuses on opportunity identification and acquisition of customer knowledge with a specific emphasis on how to use data from social networks to identify open gaps in the market and listen to customers.
- The course then deals with how to measure the creativity of novel ideas by analyzing texts and images. In this part, students are introduced to basic concepts in Computer Vision.
- The last part of the course deals with the introduction of new products in the market: product positioning according to customer feedback, new product diffusion through influencers, and best practices to launch products through social networks.
In short, the topics discussed during the course include:
- Understanding Markets with Social Media Data.
- Social Media Listening: Fetching Twitter, Facebook, and Google data.
- Design Thinking: Traditional Observation and Observation through Social Networks.
- Assessing and Measuring Creativity: Text Analysis.
- Identifying Influencers.
- Launching Innovation in the Market: Best Social Media Marketing Strategies.
- 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.
- Reproduce marketing research analyses through Big Data within the main phases of the new product development process.
- 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
- 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 in class by managers and entrepreneurs on the topics of innovation.
- Exercises on real data collected by students. These exercise allow students to practice the concepts learned in class.
- Group assignment that allows students to use all the knowledge acquired throughout the course.
The group project consists of developing an innovative project to carry out a concept at the distribution level. 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.