PostGraduate Marketing Research e CRM
Education objectives
The Postgraduate Program in Marketing Research and CRM, is for managers, experts and other marketing professionals, who wish to obtain and deepen their skills in Marketing Research and CRM, using the most advanced methods of collection, analysis and treatment of information.
The goal of this program is to train technical staff and managers to:
- Plan, create, and conduct market studies or any other market research;
- Select and apply methods of collection of marketing data;
- Analyze, interpret, and communicate the results of market research;
- Manage, explore, interpret and communicate marketing information that is present in information systems or from other
sources of marketing information; - Create, implement, and manage customer relationship policies.
This Postgraduate Program gives access to the Master degree program in Data Driven Marketing, with a specialization in Marketing Research and CRM.
Applications - 5th call
To complete the application, the applicant must register in NOVA IMS' Applications Portal, fill the form, upload their Curriculum Vitae, pay the application fee (€ 51), and submit the application in the end from June 18th and July 18th, 2024. The selection process is based on the analysis of the applicant's academic and professional curriculum..
General characterization
DGES code
E148
Cicle
Postgraduate programmes
Degree
None
Access to other programs
This Postgraduate gives access to the Master Degree in Data-Driven Marketing, with a specialization in Marketing Research and CRM.
Coordinator
Paulo Miguel Rasquinho Ferreira Rita
Opening date
September 2024
Vacancies
Fees
€4.200
Schedule
Afer working hours
Teaching language
English
Degree pre-requisites
To earn the Postgraduate Program diploma in Marketing Research e CRM, Students have to complete 60 ECTS, of which 41,5 are Mandatory Course Units and the remaining 18,5 ECTS will be chosen by the students from a wide range of available course units.
Conditions of admittance
To enter this program, applicants must meet the following requirements: hold a bachelor’s degree in a compatible field (completed by September 2024); be proficient in English (spoken and written).
Evaluation rules
The assessment method will be continuous assessment, i.e. through individual or group work, projects, quizzes, tests/exams, etc.
At the beginning of each academic year, it is up to each teacher of each Curricular Unit (CU) to define how assessment will be carried out in their CUs, and this information is then made available via the student platform.
Structure
1º year - Autumn semester | ||
---|---|---|
Code | Name | ECTS |
200163 | Experimental Design | 4.0 |
200187 | Marketing Strategy and Innovation | 7.5 |
200189 | Descriptive Analytics in Marketing | 7.5 |
Options | ||
200204 | Social Network Analysis | 3.5 |
200268 | Applied Network Analysis | 7.5 |
200012 | Business Intelligence I | 7.5 |
200201 | Data Science for Marketing | 7.5 |
200032 | DataBase Management Systems | 7.5 |
200197 | Brand Management | 3.5 |
200014 | Business Process Management | 7.5 |
200073 | Information Management Systems | 3.5 |
200196 | Digital Marketing & E-Commerce | 7.5 |
400082 | Digital Analytics | 7.5 |
200192 | Data Privacy, Security and Ethics | 4.0 |
200194 | Digital Transformation | 3.5 |
1º year - Spring semester | ||
---|---|---|
Code | Name | ECTS |
200049 | Market Research | 7.5 |
200188 | Marketing Engineering and Analytics | 7.5 |
200190 | Predictive Analytics in Marketing | 7.5 |
Options | ||
200203 | Machine Learning in Marketing | 7.5 |
200202 | Big Data for Marketing | 7.5 |
200013 | Business Intelligence II | 7.5 |
200170 | Consumer Behavior Insights | 7.5 |
400019 | Customer Relationship Management Systems | 7.5 |
200162 | Data Visualization | 7.5 |
200071 | Knowledge Management | 3.5 |
200316 | Generative AI Aplied to Marketing | 3.5 |
400081 | Social Media Analytics | 7.5 |
200200 | Search Engine Optimization | 4.0 |
200296 | Process Mining Powered By Nokia | 7.5 |
200184 | Sampling Theory and Methods | 7.5 |
200298 | Data-driven decision making | 4.0 |
200317 | UX Research | 7.5 |