Big Data/Data Mining Applied to Marketing

llsms2004  2020-2021  Louvain-la-Neuve

Big Data/Data Mining Applied to Marketing
Due to the COVID-19 crisis, the information below is subject to change, in particular that concerning the teaching mode (presential, distance or in a comodal or hybrid format).
5 credits
30.0 h
Q2
Teacher(s)
Language
English
Prerequisites
1 basic marketing course
Main themes
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
Aims

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 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”.
Content
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

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

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

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

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).
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
Bibliography


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
Sigle
Credits
Prerequisites
Aims
Master [120] in Management

Master [120] : Business Engineering

Master [120] : Business Engineering

Master [120] in Management