20593 - INNOVATION AND MARKETING ANALYTICS
Department of Marketing
GAIA RUBERA
Prerequisites
Mission & Content Summary
MISSION
CONTENT SUMMARY
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 will be 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 Tweets and Instagram Pictures
- Design Thinking: Traditional Observation and Observation through Social Networks
- Assessing and Measuring Creativity: Text and Image Analysis
- Product Positioning and Analysis of Product Reviews
- Identifying Influencers
- Launching Innovation in the Market: Best Social Media Marketing Strategies
Intended Learning Outcomes (ILO)
KNOWLEDGE AND UNDERSTANDING
- 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
APPLYING KNOWLEDGE AND UNDERSTANDING
- Identify which data are needed in the different phases of the new product development process
- Effectively scrape data from different sources, including social networks like Twitter or Instagram
- 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
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 will be completed:
· Guest speakers in class by managers and entrepreneurs on the topics of innovation. Th
· Exercises on real data collected by students. These exercise allow students to practice the concepts learned in class
· Group assignment that will allow students to use all the knowledge acquired throughout the course
Assessment methods
Continuous assessment | Partial exams | General exam | |
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ATTENDING STUDENTS
The group project consists of developing an innovative project to carry out a concept at the distribution level. The project will be presented in detail at the beginning of the semester along with the assessment criteria. This project will be used to verify the ability of students to apply the knowledge developed during the course and how to present it effectively.
The exam will be held in written form. It will be 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 will be 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 BlackBoard
NOT ATTENDING STUDENTS
Marketing Data Science, Thomas W. Miller, Pearson