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

Hours

Weekly - Available soon

Total - Available soon

Teaching language

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

Prerequisites

None.

Bibliography

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

Programs

Programs where the course is taught: