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Marketing Engineering and Analytics

Objectives

 

  • Understand analytics behind managing and marketing brands based on thorough marketing research and analysis leading to decision making
  • Understand and discuss the concept of marketing analytics;
  • Argue the relevancy of a customer analytics strategy given a specific business environment and industry;
  • 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

Mariana Guerra Ferreira

Hours

Weekly - Available soon

Total - Available soon

Teaching language

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

Prerequisites

  None.

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

 

  • M01: Concepts
  • M02: Case studies and Exercises
  • M03: Simulation Game

Evaluation method

 

  • Simulation Game (30%) - Teamwork
  • Customer Analytics Project (40%) - Group Project
  • Exam (30%) - Individual (Minimum grade in this assessment: 8 out of 20)

Subject matter

 

  • T01: Marketing Management Simulation
    • Marketing Research
    • Decision Making Process
      • International Marketing
      • B2C and B2B target segments
      • Features and design choices
      • Marketing communications
      • R&D investments
  • T02: Data preprocessing
    • Preparing the customer signature 
    • Exploring the variables
    • Summarizing transactional variables
    • Deriving new variables
    • Missing values
    • Outliers
  • T03: Customer Segmentation
    • A-priori approach
      • Cohort Analysis 
      • Quintile-based analysis
      • RFM
    • Hierarchical and Non-Hierarchical Methods
      • Hierarchical Clustering Algorithms
      • K-Means Algorithm
  • T04: Predictive Analytics
    • Methodology
    • Instance Based Methods
    • Decision Trees
    • Regression
    • Neural Networks