Big Data Analytics using Geographic Informations Systems


Course Objective

• Demonstrate a command of a range of GIS/spatial research skills and
tools to analyze large quantities of secondary data with spatial
• Demonstrate a critical understanding of the concepts and theories
used, as well as key topics covered, in the top academic journals on the
spatial aspects of marketing concepts (e.g., spread of innovation, WOM)
and consumer behavior (e.g., travel).
• Communicate and collaborate effectively with an international cohort
of classmates to design and produce case solutions or other
presentations, provided in English, to both academic and professional

Course Content

Big Data Analytics are a rapidly increasing form of research in many
academic areas, including Marketing. The exploding availability of data,
cloud services as platforms to scale and connect, and the rise of
artificial intelligence (e.g., machine learning) reshape customer and
marketing intelligence dramatically. As outlined by Wedel and Kannan
(2016) in their overview of applications in Marketing, Big Data
Analytics is also a very broad area of expertise, with each sub field
offering very specific challenges and opportunities. Text mining and
sentiment analysis will, for instance, require a different background
and involve different software packages and tools than (social) network
analysis, clickstream data and SEA, or advanced sales promotions

To prepare you to a deep enough level, this course focuses on one
specialization: Location Intelligence, for several reasons:
• Location Intelligence is identified by Wedel and Kannan (2016) as the
most recent form of Big Data Analytics in Marketing (Figure 2).
• A lot of Marketing data has location attached to it, whether the
addresses of customers, the locations of outlets and competitors, or
important environmental characteristics and geodemograhics/lifestyle
segmentation. Also, a lot of Marketing phenomenon are particularly
driven by spatial aspects, such as channel choice (online vs offline),
assortment strategies, or the adoption of innovation.
• There is a lot of free, open location data available, both in the
Netherlands (e.g., PDOK), as well as internationally (e.g., ESS in
combination with maps of NUTS regions).
• It best fits the CMA course as foundation, as it – unlike other forms
of Big Data Analytics – does not require basic knowledge in, say, Python
or SQL.
• Maps with patterns and trends are a great way of visual communication
and can be very impactful in the Marketing decision making.
• This course is only offered at the Vrije Universiteit and thus sets
you apart from graduates of other MSc Marketing programs.

The course has two main components:
• It teaches the use of QGIS ( as a freely available
program (running on both Windows and Mac) to explore, analyse and
visualize spatial data on maps.
• It discusses a range spatial aspects in Marketing, to show when, where
and how these kinds of analyses are relevant to Marketing research and

In addition, we discuss general aspects of Big Data Analytics such as
privacy and security.

Teaching Methods

Lectures and computer tutorials

Method of Assessment

Written examination: 70%
Assignment: 30%
each to be completed with a minimum score of 5.0

Recommended background knowledge

Customer and Marketing Analytics (period 2)

General Information

Course Code E_MKT_BDAGIS
Credits 6 EC
Period P4
Course Level 400
Language of Tuition English
Faculty School of Business and Economics
Course Coordinator prof. dr. J. Boter
Examiner prof. dr. J. Boter
Teaching Staff prof. dr. J. Boter

Practical Information

You need to register for this course yourself

Last-minute registration is available for this course.

Teaching Methods Lecture, Practical
Target audiences

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