20593 - INNOVATION AND MARKETING ANALYTICS
Department of Marketing
Course taught in English
Go to class group/s: 31
Course Director:
CATARINA RIBEIRO SISMEIRO
CATARINA RIBEIRO SISMEIRO
Suggested background knowledge
For a fruitful and effective learning experience, it is strongly recommended a basic preliminary knowledge in statistics and probability, likelihood-based inference, simple and multiple linear regression, generalized linear models, and basic machine learning. Students should also be familiar with basic python or any alternative language.
Mission & Content Summary
MISSION
Innovation and Marketing Analytics are integral to modern business operations. They provide valuable intelligence and insights on customers, competitors, and the wider environment, while encouraging innovative practices and suggesting strategies for future offerings. These elements enable managers to build better customer relationships and address contemporary business challenges. This course mission is to establish the foundations for analytically grounded marketing decision-making and innovative practices, supporting critical thinking. It aims to help students navigate a data-rich environment and effectively use data to solve common business challenges in a competitive market. The focus will be on modern tools and current debates about the nature and direction of marketing, with particular emphasis on the role of human analysts. In a technologically advanced world dominated by algorithm-based narratives, it is crucial to highlight the importance of analysts.
CONTENT SUMMARY
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Introduction: an introduction to data-driven decision making and the role of the business analyst
Part 1: Theoretical and modelling foundations to support marketing mix decision making using aggregate and disaggregate data: Modelling price and promotion effects Advertising and communication modelling and analysis and modern attribution challenges Targeting decisions and audience analysis Part 2: Extracting value from unstructured data in marketing and innovation context: Extracting value from review data Analysing media content Search and keyword analysis |
Intended Learning Outcomes (ILO)
KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
- Discuss business problems with a deeper understanding of their underlying mechanisms.
- Explain the trade-offs between different approaches and know which algorithms and data to select to solve specific business challenges.
- Recognize the business impact of using data, data analysis and AI to support decision-making and innovation.
- Identify and illustrate ethical issues associated with collecting and using data for business purposes.
APPLYING KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
- Analyse business data and apply theoretical knowledge to practical problems.
- Derive meaningful insights using the appropriate methodologies and tools.
- Translate theoretical knowledge into practical solutions.
- Solve real-world problems encountered in professional practice.
- Apply soft skills such as effective teamwork, clear communication, and collaboration when working on projects in professional settings.
- Apply analytical thinking by recognizing patterns and trends, leading to the identification of optimal solution.
- Implement pragmatic solutions recognizing the multiple constraints one might encounter in specific business contexts.
Teaching methods
- Lectures
- Guest speaker's talks (in class or in distance)
- Practical Exercises
- Individual works / Assignments
- Collaborative Works / Assignments
DETAILS
- Lectures will include theory and will also feature the presentation of real-life business problems, hands-on exercises and interactive activities.
• Guest speakers will present on how companies are applying data and data-science to solve their business problems.
• Students will complete individual exercises to gain experience in implementation details and analytical thinking.
• Students will work together in groups to gain experience in implementation details and analytical thinking, and to develop soft skills such as effective teamwork, clear communication and presentation skills, all required for collaboration when working on projects in professional settings.
Assessment methods
| Continuous assessment | Partial exams | General exam | |
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ATTENDING STUDENTS
Group Work (30%):
This includes exercises (throughout the course), final group project and final presentation. Exercises, project and the final presentation documents can be submitted only once for each assignment. The dates of the presentations and the deadlines for each submission will be indicated during the course. The maximum grade available to students who do not submit a project is 21/30. Maximum of four to five people per group.
A written exam (70%):
The exam could include short open questions, multiple choice-questions, and/or some programming. The exam covers material seen in the lectures, exercises, labs, and other set of readings provided by the Professor.
NOT ATTENDING STUDENTS
A written exam (100%):
The exam could include short open questions, multiple choice-questions, and/or some programming. The exam covers material in the lecture slides, exercises posted on the course page, and other set of readings provided by the Professor including the book (the book Verhoef, Peter, Edwin Kooge, and Natasha Walk (2016), Creating Value with Big Data Analytics: Making Smarter Marketing Decisions, Routledge, is a required reading for non-attending students.)
Teaching materials
ATTENDING STUDENTS
NOT ATTENDING STUDENTS
Lecture slides and notes, programming codes, reading materials and selected chapters of relevant books are uploaded on the e-learning platform. A set of recommended (but not required) readings will also be provided.
The book Verhoef, Peter, Edwin Kooge, and Natasha Walk (2016), Creating Value with Big Data Analytics: Making Smarter Marketing Decisions, Routledge, is a required reading for non-attending students.
Last change 03/02/2025 18:12