Digital Data Analysis

MLSMM2231  2017-2018  Mons

Digital Data Analysis
5.0 credits
30.0 h
2q

Teacher(s)
Ongaro Marco ;
Language
Français
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

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
Base readings:
  • 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
 

To explore further:

  • 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)

Program title
Sigle
Credits
Prerequisites
Aims
Master [120] in Management
5
-

Master [120] in Business Engineering
5
-

Master [120] in Business Engineering
5
-

Master [120] in Management
5
-