Big Data/Data Mining Applied to Marketing

llsms2004  2019-2020  Louvain-la-Neuve

Big Data/Data Mining Applied to Marketing
Note du 29 juin 2020
Sans connaitre encore le temps que dureront les mesures de distances sociales liées à la pandémie de Covid-19, et quels que soient les changements qui ont dû être opérés dans l’évaluation de la session de juin 2020 par rapport à ce que prévoit la présente fiche descriptive, de nouvelles modalités d’évaluation des unités d’enseignement peuvent encore être adoptées par l’enseignant ; des précisions sur ces modalités ont été -ou seront-communiquées par les enseignant·es aux étudiant·es dans les plus brefs délais.
5 crédits
30.0 h
Q2
Enseignants
Chevalier Ludovic;
Langue
d'enseignement
Anglais
Préalables
1 basic marketing course
Thèmes abordés
Introduction
Humanity has generated and stored more data in the last 24 months than in the millions of years before that. World's data production, analysis, and consumption are growing exponentially and this trend is not slowing down anytime soon.
In such environment understanding and working with data has become crucial for companies to survive, innovate and grow. For this reason, companies are more and more demanding of data literate workforce - and marketing is no exception.
 
The fundamental pillars of marketing ' acquire and retain customers - will not change, but the means available to marketers to achieve their objectives are changing fundamentally. This course will introduce and delve into one of the most promising new mean available to marketers to achieve their objectives: Big Data.
Themes that will be addressed are:
Digital marketing (campaign/strategy), Big data, Data mining, Artificial Intelligence, AdWords, Analytics, SEA/SEO/SEM, Technologies, Multi-channel communication
Acquis
d'apprentissage

A la fin de cette unité d’enseignement, l’étudiant est capable de :

1 On successful completion of this program, each student will acquire the following skills :
  • Knowledge, reasoning and critical thinking
  • Project management
  • Communication and interpersonal skills
  • Leadership and team working
  • Analytical skills
At the end of this course, you should be able to understand and use big data in order to:
  • Identify growth opportunities.
  • Personalise and automate marketing efforts.
  • Predict ROI of future marketing campaigns.
 

La contribution de cette UE au développement et à la maîtrise des compétences et acquis du (des) programme(s) est accessible à la fin de cette fiche, dans la partie « Programmes/formations proposant cette unité d’enseignement (UE) ».
Contenu
The content of the lectures (first part) will be divided into 6 Modules:
  1. Understanding big data and data mining.
  2. Structure and language of a database.
  3. Collecting data and working with data.
  4. Data mining applied to marketing.
  5. Focus on successful big data marketing.
  6. Impact of Artificial Intelligence in marketing.
Méthodes d'enseignement
Conferences, lectures, group project, exercises, articles, in-class/at-home activities, readings, self-study, discussions, case studies
Modes d'évaluation
des acquis des étudiants
Evaluation methods will be detailed later on.
This year (2019-2020) the course is divided into two parts: weekly lectures and, in parallel, a group project. The evaluation of the first part consists of an individual written exam based on the lectures given throughout the quarter. The methods of evaluation will be specified on Moodle.
The weight of the total mark will be split as followed:
  • 60% for the individual exam.
  • 40% for the group assignment.
Students that get less than 8/20 to their individual exam will automatically receive the same mark for their group assignment marks (on an individual level).
Autres infos
Pré-requis Marketing de base Evaluation : Préparation des études de cas par groupe et/ou en individuel Support : Textbook référencé (Malaval, Mktg B2B) et transparents/cas fournis via iCampus Références : Fournies durant le cours Encadrement : Réception hebdomadaire du professeur Autres : - Eléments d'internationalisation X contenu international X études de cas internationales Interventions d'entreprises X conférence X étude de cas X intervenant du monde de l'entreprise X visite d'entreprise
Bibliographie


Slides provided through Moodle.
Additional references on the topic will be communicated later to the students.
 
Reference books (recommended but not compulsory):

The Big Data-Driven Business: How to Use Big Data to Win Customers, Beat Competitors, and Boost Profits by Russel Glass.
Big Data Marketing: Engage Your Customers More Effectively and Drive Value by Lisa Arthur.


(For even more:
Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die by E. Siegel
Big Data: A Revolution That Will Transform How We Live, Work, and Think by V. Mayer-Schönberger and K. Cukier
Data-driven Marketing: The 15 Metrics Everyone in Marketing Should Know by Mark Jefferey.)
Faculté ou entité
en charge
CLSM


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

Intitulé du programme
Sigle
Crédits
Prérequis
Acquis
d'apprentissage
Master [120] : ingénieur de gestion

Master [120] : ingénieur de gestion

Master [120] en sciences de gestion

Master [120] en sciences de gestion