1. Início
  2. NOVA Information Management School
  3. Pós-Graduação em Marketing Intelligence
  4. Métodos Analíticos e Engenharia de Marketing

Métodos Analíticos e Engenharia de Marketing

Objetivos

  • Understand and discuss the concept of marketing analytics;
  • Understand the steps from an operational information system to an analytical base table;
  • Perform customers¿ segmentation;
  • Develop predictive models for, e.g., next-best offer, cross-sell and up-sell campaigns and churn.

Caracterização geral

Código

200188

Créditos

7.5

Professor responsável

Frederico Miguel Campos Cruz Ribeiro de Jesus

Horas

Semanais - A disponibilizar brevemente

Totais - A disponibilizar brevemente

Idioma de ensino

Português. No caso de existirem alunos de Erasmus, as aulas serão leccionadas em Inglês

Pré-requisitos

n.a.

Bibliografia

  • Cesim (2021) SimBrand: Marketing Management Simulation. Cesim, Finland.
  • Linoff, Gordon & Berry, Michael (2011). Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management. Wiley.
  • Hair, J. F., Babin, B. J., Black, W. C., & Anderson, R. E. (2018). Multivariate Data Analysis. Cengage.

Método de ensino

This course will be leaded by theoretical and practical classes, and an applied developed group project.

Método de avaliação

  • Customer Analytics Project (40%) ¿ Group Project
  • Exam (60%) ¿ Individual

Notes:

  • Minimum passing overall grade: 10 out of 20 points
  • Minimum grade for each item (cut-off point): 8 out of 20 points.
  • Customer Analytics Project¿ need to be performed during the semester in teams/groups and are not eligible for Resit Exam. Only Exam (60%) can have a second attempt.

Conteúdo

  1. Data preprocessing
    1. Preparing the customer signature
    2. Exploring the variables
    3. Summarizing transactional variables
    4. Deriving new variables
    5. Missing values
    6. Outliers
  2. Customer Segmentation
    1. A-priori approach
      1. Cohort Analysis
      2. Quintile-based analysis
      3. RFM
    2. Hierarchical and Non-Hierarchical Methods
      1. Hierarchical Clustering Algorithms
      2. K-Means Algorithm
  3. Predictive Analytics
    1. Methodology
    2. Instance Based Methods
    3. Decision Trees
    4. Regression
    5. Neural Networks