Marketing Information Systems
Code: 103155Credits: 6
| Degree programme | Type | Course |
|---|---|---|
| Advertising and Public Relations | OP | 4 |
Contact lecturer
- Name :
- Leila Mohammadi
- Email :
- leila.mohammadi@uab.cat
Group languages
You can consult this information at the end of the document.
Prerequisites
Average knowledge of the English language, to write and express themselves orally with some fluency
Objectives
Overall, this course aims to:
- Understand the role of information systems in organizations and their importance in marketing decision-making.
- Explain how a marketing information system is developed and operates within an organization.
More specifically, the course is designed to enable students to:
- Analyze marketing problems and break them down into simpler elements to facilitate information search and use.
- Apply basic information management knowledge to design and develop a Marketing Information System (MIS).
- Evaluate the opportunities and limitations of information systems in real-world contexts.
- Identify and use current sources and research techniques in marketing.
- Apply data analysis and interpretation methods to support marketing decision-making.
Learning outcomes
- CM21 (Design marketing plans that take suppliers, budgets, and cost control into account.) Design marketing plans that take suppliers, budgets, and cost control into account.
- KM27 (Recognise the value of brands and audiences as strategic linchpins in the relationship between advertisers and consumers.) Recognise the value of brands and audiences as strategic linchpins in the relationship between advertisers and consumers.
- SM22 (Analyse the consumer to determine the most suitable insight for the design of an advertising campaign strategy.) Analyse the consumer to determine the most suitable insight for the design of an advertising campaign strategy.
Contents
Marketing Information Systems (MIS)
- Concept and functions of marketing information systems
- Structure and components of an MIS
Sources of marketing information
- Marketing databases
- Internal and external sources
- New technologies and digital environments (Internet and social media)
Audit and information management
- Marketing audit
- Information quality and management
Big Data and data analysis
- Concept of Big Data
- Applications of Big Data in marketing
- Case studies
CRM and relationship marketing
- Concept and functionalities of CRM systems
- Customer relationship management
- Case studies
Consumer-centered information systems
- Customer Journey Map
- Customer Life Cycle
- Introduction to neuromarketing
Business Intelligence and decision-making
- Business Intelligence systems
- Data analysis and visualization
Global environment and evolution of information systems
- Impact of globalization
- Current trends in marketing and information systems
Learning activities and methodology
| Title | Hours | ECTS | Learning outcomes |
|---|---|---|---|
| Theory sessions | 15 | 0.6 | KM27, SM22 |
| Group work preparation for internships | 62 | 2.48 | CM21, KM27 |
| Seminars and exercises review | 4.5 | 0.18 | CM21, KM27, SM22 |
| Internship tutorials | 4 | 0.16 | CM21 |
| Practical sessions | 33 | 1.32 | CM21, KM27, SM22 |
| Research, readings, identification of models for seminars | 25 | 1 | KM27, SM22 |
The course is taught entirely in English, and students are expected to participate both orally and in writing in this language.
Marketing Information Systems is an interdisciplinary field integrating knowledge from marketing, business management, market research, and information technologies. The course follows a theoretical-practical approach.
The teaching methodology combines different learning strategies:
- Lectures
- Problem-based learning (PBL)
- Collaborative learning
- Case studies
- Tutorials and supervision
Learning activities include:
- Theoretical sessions
- Practical sessions
- Independent work
- Group work
A detailed schedule outlining the content of each session will be presented on the first day of the course and will be available on the course’s Virtual Campus, where students will find the various teaching materials deemed appropriate by the instructors and necessary information for effective course monitoring. Should the teaching modality change for reasons of force majeure according to the competent authorities, the teaching staff will inform students of any modifications to the course schedule and teaching methodologies.
Note: The course content will be sensitive to issues related to gender perspective and the use of inclusive language.
Assessment
Continuous assessment activities
| Title | Weight | Hours | ECTS | Learning outcomes |
|---|---|---|---|---|
| Attendance/participation/practices evaluated on the lecture theory | 20% | 3 | 0.12 | CM21, KM27, SM22 |
| Final Exam | 50% | 2 | 0.08 | CM21, KM27, SM22 |
| group work of the subject | 30% | 1.5 | 0.06 | CM21, KM27, SM22 |
The course is assessed through continuous assessment.
Continuous assessment
The final grade is based on:
- Participation, attendance and assessed practical activities: 25%
- Group project: 30%
- Final exam (theoretical-practical): 50%
To pass the course, students must obtain a minimum grade of 3.5 out of 10 in the final exam.
Reassessment
Students are entitled to reassessment if they have been evaluated on activities accounting for at least two-thirds of the total grade.
- Only the final exam can be reassessed.
- The minimum grade required is 3.5.
- The reassessment consists of a theoretical-practical exam.
Single assessment
This course includes a single assessment system.
Single assessment will take place on a single evaluation date. On this date, students must complete the theoretical and practical exam, as well as submit and present the final assignment.
The final grade is based on:
- Final theoretical-practical exam: 50%
- Final assignment: 30%
- Oral presentation: 20%
Each part must be passed independently.
Reassessment of single assessment
Students are entitled to reassessment if they have been evaluated on activities accounting for at least two-thirds of the total grade.
- Only the final exam can be reassessed.
- The reassessment consists of a theoretical-practical exam.
Not assessable
A student will be graded as not assessable if they have not completed activities representing at least 33% of the total assessment.
Second enrollment
From the second enrolment onwards, assessment may consist of a synthesis exam covering the learning outcomes of the course.In this case, the final grade will correspond to the grade obtained in this exam. Details will be communicated to students in due time.
Use of Artificial Intelligence
In this course, the use of Artificial Intelligence (AI) technologies is permitted as an integral part of assignment development, provided that the final outcome demonstrates a significant contribution from the student in terms of analysis and personal reflection. Students must clearly identify any content generated using AI, specify the tools employed, and include a critical reflection on how these technologies have influenced both the process and the final result of the assignment. Failure to disclose the use of AI in this assessed activity will be considered a breach of academic integrity and may result in a partial or total penalty to the assignment grade, or more serious sanctions in severe cases.
Irregularities and plagiarism
The performance of any irregularity in an evaluation act (academic fraud, plagiarism or improper use of AI, unless this use is expressly authorized by the teaching guide), which may lead to a significant variation in the grade, assumes that this act will be graded with a 0. In the event that the teaching guide foresees that in order to pass the subject it is an essential requirement to have obtained a minimum grade in this evaluation act or that there are several irregularities in the evaluation acts of the same subject, the final grade of this subject is 0. Apart from this, a disciplinary process may be instructed to the student that incurs any of these irregularities.
Any student suspected of submitting assignments that have been generated by AI, written by others or copied; include unattributed AI-generated content, or fall outside the permitted scope, may be asked to provide the preliminary work or other materials that can demonstrate it is original work and the result of their own authorship. They may also be asked to separately explain or justify their work. Teachers may also use AI detection systems or carry out any verification tasks they deem appropriate. If, after review, the instructor detects irregularities, the assignment may be graded zero, and the student may be subject to further disciplinary action.
Bibliography
- Andreu, R.; Ricart, J.E.; Valor, J. (1997). Information Systems Strategy. McGraw-Hill.
- Emery, J.C. (1990). Information Systems for Management. Díaz de Santos.
- Garmendia Aguirre, F. (2007). Marketing Information Systems. ESIC.
- Gómez, A.; Suárez, C. (2011). Information Systems: Practical Tools for Management (4th ed.). Ra-Ma.
- Kimball, R.; Ross, M. (2013). The Data Warehouse Toolkit. Wiley.
- Kotler, P.; Keller, K.L. (2016). Marketing Management (15th ed.). Pearson.
- Provost, F.; Fawcett, T. (2013). Data Science for Business. O’Reilly.
Additional materials and readings will be provided through the Virtual Campus during the course.
Software
No specific software is required for this course.
However, during practical sessions, students may use digital tools and online resources for the analysis of marketing information and data (e.g., spreadsheets or data visualization tools).
Course groups and languages
The information provided is provisional until November 30. After this date, you will be able to consult the language of each group through this link. To access the information, you will need to enter the course CODE
| Type of teaching | Group | Language | Semester | Shift |
|---|---|---|---|---|
| (TE) Theory | 5 | English | first semester | afternoon |
| (PLAB) Practical laboratories | 51 | English | first semester | afternoon |