Digital Data Analysis

mlsmm2231  2019-2020  Mons

Digital Data Analysis
Note from June 29, 2020
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
5 credits
30.0 h
Q2
Teacher(s)
Language
French
Prerequisites
/
Main themes
The digital environment is characterized by an abundance of data available in the company's systems, but also outside on social networks, ecommerce sites, or competitors sites. These data once collected, assembled, and analyzed appropriately can provide information on consumer behavior, activities of competitors, or companies' performance.
Today more than ever, It is essential to monitor the company's performance on its website, on social networks, across all its digital activities. The analysis of such digital data requires both technical and analytical skills, coupled with a strong business acumen and a sense of marketing and management.
One of the key skills of the (digital) marketer of tomorrow will be the ability to identify pertinent data that can help in its thinking, deploy the data collection tools, select the analytical method of this specific digital data, and to implement the analyses necessary to build actionable business recommendations.
The primary objective of the course is to provide the knowledge and tools to identify, collect, and analyze relevant and useful data to implement and use the knowledge and results to create or adapt the marketing strategy of the company:
  • On one hand, around its performance and its competitive position.
  • On the other hand, around clients' behavior in general and more particularly in the digital environment (e-behavior).
  • The course will also focus on understanding the opportunities and limitations of different web analysis tools available for the business.
Aims

At the end of this learning unit, the student is able to :

1 By the end of the class, students will have a thorough understanding of the methods taught and will be able to apply them to digital marketing issues in order to formulate pertinent managerial recommendations.
 

The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
Teaching methods
Lectures
Case study
Evaluation methods
Ongoing evaluation
Bibliography
Références de base :
  • Digital Marketing Analytics, Making Sense of Consumer Data in a Digital World, Chuck Hemann & Ken Burbary, Que Publishing, Pearson, ISBN-13:   978-0-7897-5030-3.  Disponible sur Amazon et autres sites commerciaux.
  • WEB Analytics Demystified, Eric T. Peterson, ISBN: 0-9743584-2-8.  Disponible en téléchargement gratuit sur le site de l'auteur: www.webanalyticsdemystified.com
 
Pour aller plus loin :
  • Big Data: Using smart Big Data analytics and metrics to make better decisions and improve performance, Bernard Marr, ISDN-13: 978-1-118-96583-2, Willey
  • Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results, Bernard Marr, ISDN-13: 978-1119231387, Willey
  • Data Science for Business, Foster Provost & Tom Fawcett, ISDN-13: 978-1-449-36132-7, O'Reilly
Faculty or entity


Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Sigle
Credits
Prerequisites
Aims
Master [120] : Business Engineering

Master [120] : Business Engineering

Master [120] in Management

Master [120] in Management