Marketing Engineering and Analytics

Objectives

  • 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.

General characterization

Code

200188

Credits

7.5

Responsible teacher

Frederico Miguel Campos Cruz Ribeiro de Jesus

Hours

Weekly - Available soon

Total - Available soon

Teaching language

Portuguese. If there are Erasmus students, classes will be taught in English

Prerequisites

n.a.

Bibliography

  • 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.

Teaching method

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

Evaluation method

  • 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.

Subject matter

  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