Information Systems and Database Marketing

Code:

2GEC07

Acronym:

SIDM

Keywords
Classification	Keyword
OFICIAL	Computer Science

Instance: 2023/2024 - 2S

Active?	Yes
Responsible unit:	Agrupamento Científico de Matemática e Sistemas de Informação
Course/CS Responsible:	Master in Sales Management

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
GCOM	40	Official Bologna Syllabus	1	-	7,5	56	202,5

Teaching language

Portuguese

Objectives

Information Systems:

 

• Familiarize students with the "State-of-the-Art" Information Technologies

• Raising awareness on the current role and future of Information Technology in the company, not only to the management and operational levels, as the level of business format in order to enhance the competitive advantages for companies

• Discuss the Business Intelligence and Datawarehouse approaches in the Big data analytics context

• Familiarize students with relational and nonrelational databases

• Study the Web 2.0 and Electronic commerce. Concepts, contours and characteristics

Database Marketing:

 

• Learn concepts of databases, data mining and database marketing.

• Learn key concepts of database marketing: LTV, RFM, etc.

• Identify marketing problems that can be addressed with data mining.

• Valuing the objective assessment of marketing communications.

• Understand the phases of a data mining project.

• Acquire basic skills for development of data mining projects using Power Bi and RapidMiner.

 

 

Learning outcomes and competences

Students should be familiar with the "State-of-the-Art" Information Technologies, namely in terms of Business Intelligence and its role in the companies at both operational and management as the potential for change in business models and development advantages competitive.

After the activities of the course, students should be able to apply modern techniques of data mining in the context of customer characterization and to model the operations of segmentation, targeting and market basket analysis.

Working method

Presencial

Program

Module Information Systems:

1) Evolution of Information Technology and its role in business

Information Technology and the Global Environment

Companies and Information Technologies

Information Technologies and competitive advantages

The impact of information technology in the organization

2) Familiarity with the "State-of-the-Art" Information Technologies

Information Technology

Key Business Applications - ERP, CRM, EIS

WEB 2.0, Enterprise Portals

Internet and Web

3) Analysis of investments in Information Technology

Setting up the project

The team

the partners

Planning and control

4) Major trends of IT and SI - BI, DW, WEB Revolution , Knowledge Management

Relational and Nonrelational databases; 
ETL

Database Marketing module:

•Introduction: What knowledge can be extracted from the data? Data analysis, Data mining, Decision Support.  Key concepts of database marketing: LTV, RFM, etc.

• Fundamental concepts: basic-principles of success for a data-driven
business; Acquiring and sustaining competitive advantage via data science; The
importance of careful curation of data science capability.

• Data: data preparation

- sources (relational databases, data warehouses, databases, text, clickstreams)

- preparation (cleaning, transformation)

• Mining:

--Clustering/agrupamento (Eg for customer segmentation)

- Forecast (classification and regression)

- Basket analysis (cross-selling, association analysis, collaborative filtering).

• Projects DM

- Rating: generic metrics (eg error, ROC analysis) and application-specific (eg customer value, service value, quality of marketing models)

- Methodologies: (CRISP, SEMMA)

- Tools (free and commercial): PowerBi and RapidMiner 

 

 

Mandatory literature

Ralph Kimball; The data warehouse toolkit. ISBN: 978-1-118-53080-1
Provost, Foster, Fawcett, Tom; Data Science for Business, What You Need to Know about Data Mining and Data-Analytic Thinking, O'Reilly Media, 2014
Foster Provost and Tom Fawcett; Data Mining and Data-Analytic Thinking

Complementary Bibliography

Campos, P., Moreira, J., Santos, S.; web analytics, E-Agro innovation, 2020
Arthur M. Hughes; Strategic Database Marketing, 3rd Edition, McGrawHill
Turban, Efraim; Information technology for management. ISBN: 978-1-118-09225-5
Michael Berry e Gordon Linoff; Data Mining Techniques: For Marketing, Sales and Customer Support, Wiley
Olivia Parr Rud; Data Mining Cookbook: Modeling Data for Marketing, Risk, and Customer Relationship Management, Wiley

Teaching methods and learning activities

Theoretical exposition along with practical examples. 

Software

Power BI
MS-Access
MS - Excel
Rapid MIner

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation	Weight (%)
Exame	50,00
Participação presencial	10,00
Trabalho escrito	40,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Estudo autónomo	20,00
Frequência das aulas	14,00
Trabalho escrito	10,00
Total:	44,00

Eligibility for exams

Written assignment and final exam, or final exam only

Calculation formula of final grade

The student will only get approval if he gets a minimum of 7 points in the exam.

The classification in Database Marketing module is calculated as follows:

0.4 x assignment_classification + 0.5 x exam_classification + 0.1 x presencial participation

 

Examinations or Special Assignments

An assignment which consist of exploration with the techniques presented in a data set. 

The work will be performed in groups of 2 or 3 students.