Marketing Analytics

Duration

30h Th

Number of credits

Lecturer

Stéphanie Aerts

Language(s) of instruction

English language

Organisation and examination

Teaching in the second semester

Schedule

Schedule online

Units courses prerequisite and corequisite

Prerequisite or corequisite units are presented within each program

Learning unit contents

Did you know that...
- in 2016, 2.5 million gigabytes of new data were produced every day.
- over 90% of all the data in the world was created in the past 2 years.
- the total amount of data being captured and stored by industry doubles every 1.2 year.
... but what in the world do businesses do with all of this data?
The main question is:  How to transform raw "stupid" data into valuable "actionable" information to help decision making? The answer is : « By using Marketing Analytics techniques ! »
Marketing analytics involves the creation and use of quantitative data to derive consumer insights and make decisions. It is often heralded as a critical resource necessary for effective marketing.
Common applications in marketing and sales are the analysis of customer loyalty, the analysis of the market basket, the identification of prospective customers..  Common applications in eCommerce are recommendation tools, converting clicks into customers...  Other common applications in Supply Chain Management are revenue management (e.g. airline ticketing), capacity management, diagnosis of production faults, forecasting demand, predictive modeling, advanced reporting based on ERP systems... Common applications in finance are fraud detection, credit risk analysis, risk profile, pricing...
In this course, you will learn :
Good practices in data analysis
Descriptive analytics tools:

• Advanced reporting and visualisation tools, 
• Market Basket Analysis
• Clustering/Segmentation

• Regression (linear and logistic)
• Decision trees

Learning outcomes of the learning unit

This course will help students to :

• Gain the attitudes, skills, knowledge and understanding specific to the chosen concentration field.
• Establish a strategy to optimize the value chain of a company.
• Use modelization methods efficiently.
• Develop scientific precision and a critical sense (arguing).
• Take advantage of data digitalization.
• Acquire the capacity to research autonomously and methodically the information needed to solve a complex, transversal management problem.
• Integrate autonomously researched information, tools, knowledge and context to build and propose original, creative and viable solutions to concrete complex management problems whether real or simulated.
• Provide concrete solutions to a management problem, integrating modelization methods and/or a dimension of technology, innovation or production.
• aqcuire a professional capacity for written communication.

Prerequisite knowledge and skills

Basic computer skills.
Basic statistics (descriptive statistics, and elements of probability)

Planned learning activities and teaching methods

Emphasis will be placed on the understanding and real-life application of the analytics methods. To this end, all the theoretical concepts will be diretcly illustrated on small examples and students will practice on real-data examples. They will acquire a basic theoretical knowledge but also learn to use professional tools like Excel, Tableau or SAS Enterprise Miner.
The students will also have to complete a group project on a provided dataset. This project will run throughout the year and will be broken down into small steps. 
A flipped classroom approach is used for some chapters:  The students have to prepare some topics before the lecture thanks to an online tutorial and/or slides. The lecture will focus on the difficulties encountered during the preparation, as well as on the interpretation of the results.
The project and the preparations are compulsory.  If a student has not completed all his/her assignments, he/she will not be allowed to sit the exam (failure mark).

Mode of delivery (face-to-face ; distance-learning)

All lectures are given in a computer room.

Recommended or required readings

All required documents will be published on Lol@.
Recommended:
Introduction to Data Mining, Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Pearson International.

Assessment methods and criteria

First session
Three parts:

• Understanding of the methods - 1/3
• Practice on the computer - 1/3
• Project - 1/3

• Understanding of the methods - 1/2
• Practice on computer - 1/2

Work placement(s)

Organizational remarks

The examinations are organized twice a year during the official sessions.  All the students sit the exam in Liège.  Due to the nature of this course, we do not accept to organize any additional exam at a different date and/or at a different location (even for students from abroad).

Contacts

S. Aerts, HEC-ULg, N1 (334) Stephanie.Aerts@uliege.be