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 ContentBig 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
• 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 (www.qgis.org) 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 MethodsLectures and computer tutorials
Method of AssessmentWritten examination: 70%
each to be completed with a minimum score of 5.0
Recommended background knowledgeCustomer and Marketing Analytics (period 2)
|Language of Tuition||English|
|Faculty||School of Business and Economics|
|Course Coordinator||prof. dr. J. Boter|
|Examiner||prof. dr. J. Boter|
prof. dr. J. Boter
You need to register for this course yourself
Last-minute registration is available for this course.
|Teaching Methods||Lecture, Practical|
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