Big Data/Data Mining Applied to Marketing

llsms2004  2023-2024  Louvain-la-Neuve

Big Data/Data Mining Applied to Marketing
5.00 credits
30.0 h
Bourgies Fabien;
1 basic marketing course
Main themes
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
Learning outcomes

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

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.
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.
Teaching methods
Conferences, lectures, group project, exercises, articles, in-class/at-home activities, readings, self-study, discussions, case studies
Evaluation methods
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.
Other information
Prerequisites Basic Marketing Evaluation : Case studies preparation (group and/or individual) Support : Textbook recommended (Malaval, B2B Mkt) and slides provided through iCampus References : Provided during the class Pedagogic team : Professor's weekly open door Other : - Internationalisation - international content - international case study Corporate features - conference - case study - corporate guest - company visit

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.)
Faculty or entity

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

Title of the programme
Learning outcomes
Master [120] in Management

Master [120] : Business Engineering

Master [120] in Management

Master [120] : Business Engineering

Master [120] in Management (with work-linked-training)