Big Data/Data Mining Applied to Marketing

Teacher(s)

Chevalier Ludovic;

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

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

Evaluation methods

Evaluation methods will be detailed later on.
This year (2017-2018) the course is divided into two parts that are equally weighed: 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 for the second part (i.e. this year the Digital Masters challengeorganised by bloovi.me and Google: For more information click here) will be specified on Moodle.

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

CLSM