Course description
Title of the Teaching Unit
CRM and Data Marketing
Code of the Teaching Unit
22MMK60
Academic year
2025 - 2026
Cycle
Number of credits
5
Number of hours
60
Quarter
1
Weighting
Site
Anjou
Teaching language
English
Teacher in charge
DEVENTER Claire
Objectives and contribution to the program
Learning Objective 1: Understand AI concepts and mechanisms in marketing
1.1 Define the key characteristics and concepts related to artificial intelligence in marketing.
1.2 Explain the different technical mechanisms underlying AI (e.g., rule-based systems, machine learning, generative AI) and how they influence customer experience and marketing practices.
Transversal skills addressed: Critical thinking, Decision-making.
Bloom’s taxonomy level: Remember, Understand.
Learning Objective 2: Analyze and evaluate the use of AI in marketing
2.1 Apply different AI techniques to address marketing challenges (e.g., customer scoring, product recommendation, customer service personalization).
2.2 Analyze the suitability of various AI approaches for specific marketing problems, considering both technical strengths and business constraints.
2.3 Evaluate how AI implementations may introduce risks (e.g., bias, exclusion, misuse of data) and compare how these risks vary across contexts and technologies.
Transversal skills addressed: Critical thinking, Decision-making, Designing desirable futures.
Bloom’s taxonomy level: Apply, Analyze, Evaluate.
Learning Objective 3: Design a responsible and innovative AI marketing project
3.1 Conceptualize a responsible, relevant, and feasible AI-based marketing initiative that creates value for both companies and society.
3.2 Formalize this initiative into a roadmap and a proof of concept.
3.3 Develop and present an ethical charter for AI integrated into the project.
Transversal skills addressed: Self-development, Designing desirable futures, Critical thinking, Decision-making, Entrepreneurship, Communication, Collaboration.
Bloom’s taxonomy level: Create.
Prerequisites and corequisites
Content
The course is divided into two main parts:
1. Technical implementations of artificial intelligence: exploration of different types of AI, their functioning, and their limitations.
2. Uses of artificial intelligence in marketing and service management: analysis of concrete applications, their impact on individuals, and the related managerial and human challenges.
Teaching methods
The course consists of three types of activities:
A) Lectures: presentation of the fundamental concepts of artificial intelligence and its applications in marketing and service management, illustrated with concrete examples.
B) Thematic exercise sessions: hands-on practice of the notions covered in class through case studies and guided discussions. Students apply different AI approaches, analyze their relevance, and evaluate their ethical and managerial limitations.
C) Group project: design of an innovative marketing initiative that integrates AI in a responsible way. Students formalize their project as a proof of concept and present a mini ethical charter for AI.
Assessment method
1) Formative assessments
1.1 In-class discussions: allow students to check their understanding of AI concepts and practice identifying their marketing implications.
1.2 Thematic exercises: self-assessment based on the resolution of case studies and collective analysis, with instructor feedback to guide critical and ethical reflection.
1.3 Project coaching session: opportunity to receive targeted feedback on specific parts of the project at mid-term, upon students’ request.
2) Certificative assessments
2.1 Individual written exam: includes multiple-choice questions and case studies. It assesses students’ understanding of concepts, their ability to analyze marketing situations involving AI, and to evaluate their managerial and ethical implications.
2.2 Group project: design of a marketing initiative that integrates AI responsibly. The project is formalized as a proof of concept and a mini ethical charter. The evaluation focuses on the project’s relevance, creativity, theoretical grounding, and quality of communication, as well as on students’ ability to integrate ethical and managerial reflection into their work.
3) Use of generative AI
The use of generative AI tools or unauthorized digital devices is strictly forbidden during the written exam.
In all other contexts of this course, the use of generative AI tools is permitted, provided it is done responsibly and transparently:
3.1 Students may use AI as a support tool to clarify concepts, reformulate ideas, explore directions, or check their understanding, similar to a discussion with a peer or instructor.
3.2 AI may not replace personal or group work: all analyses, ethical reflections, and creative outputs must remain the students’ own work.
3.3 For the technical project, students may use AI tools to help generate parts of the KNIME workflow in order to test ideas. However, the design of the project (choice of algorithms, methods, and purpose) must be conceived by the students themselves.
3.4 Any substantial use of AI must be explicitly acknowledged in the assignment or report, e.g., by adding a note specifying the tool used and how it was used.
3.5 Students remain fully responsible for the quality, accuracy, and originality of their work.
References
A selection of scientific articles on Moodle (mandatory readings)