Big Data/Data Mining Applied to Marketing

Enseignants

Chevalier Ludovic;

Langue
d'enseignement

Anglais

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.

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

En raison de la crise du COVID-19, les informations de cette rubrique sont particulièrement susceptibles d’être modifiées.

Conferences, lectures, group project, exercises, articles, in-class/at-home activities, readings, self-study, discussions, case studies

Modes d'évaluation
des acquis des étudiants

En raison de la crise du COVID-19, les informations de cette rubrique sont particulièrement susceptibles d’être modifiées.

Continuous evaluation

• Date: 19/05/21
• Type of evaluation: Group assignment.
• Comments:

Evaluation week

• Oral:
• Written:
• Unavailability or comments:

Examination session

• Oral:
• Written: Written exam on site (max. 2h)
• Unavailability or comments: If impossible to organize exam in Louvain-la-Neuve due to Covid, a 10 min individual Oral exam through Teams will be organized instead.

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