Admission requirements
Students are expected to have a good understanding of the basic concepts, frameworks and instruments of classic marketing, i.e. have attended the course Marketing & Corporate Communications with a sufficient end grade.
Description
This course provides an introduction to the application of information technology and data science in marketing, also known as data-driven marketing. This multi-disciplinary field has seen a growing interest in the last years. Businesses and organisations are facing both huge opportunities and challenges provided by the massive use of powerful digital media by their prospects and customers. Classic marketing approaches are becoming less effective, a clear need exists for establishing interactive, personal relationships and relevant dialogues, fuelled by a superb user experience, an omni-channel approach and a holistic customer view based on big data and machine learning techniques. These needs have been translated now to a growing demand for agile, cross-functional approaches and a blending of marketing and technology skill sets.
We will cover a broad spectrum of topics by linking current business practices to theory and vice versa. Starting with the fundamentals of customer-centricity, we will explore the organisational models and practices, (digital) marketing technology and analytics, and customer data management including privacy aspects.
Course objectives
By the end of the course, the student should be able to:
understand the strategies, basic concepts and frameworks of data-driven marketing;
identify and apply customer data (incl. privacy regulations) and DDM technologies;
show their ability to organise, plan and design for data-driven marketing practice.
Timetable
The schedule can be found on the Leiden University student website
Detailed table of contents can be found in blackboard.
Mode of instruction
The programme will consist of 6 lectures that cover the following topics.
1. Introduction: course overview, the concept of data-driven marketing, trends, DDM processes and applications;
2. DDM strategy: Customer centricity, In-bound marketing, Customer journey, Customer Experience, Customer value;
3. Data & Privacy: Customer Data & Integration, Data Quality, Data Protection and Privacy aspects;
4. DDM Technology: Architecture domains, Marketing platforms, Open Source vs. licensed software, Data Mining tools, Big Data platforms and technology;
5. DDM Lab: Customer Segmentation, Predictive modelling, Machine Learning, Web analytics;
6. Organising for DDM: Marketing and ICT organisations, In- and out-sourcing, Agile marketing, Marketing Science skills
Assessment method
Participation in class and during the working sessions will be taken into account for the final grade.
The final exam will consist of a major, written assignment
(delivery deadline: 2 weeks after the final lecture).
Grading for this course will be based on:
Participation (presence + interaction): 20%
Homework assignments: 20%
Paper: 60% (minimum grade 6 required to pass)
Blackboard
Reading list
The presentations and background articles will be available on Blackboard. References to articles and books will be provided during the lectures.
Optional reading
•Book: Creating Value with Big Data Analytics – Making Smarter Marketing Decisions (P. Verhoef, E. Kooge, N. Walk / Routledge, 2016 / ISBN 978-1-315-73475-0)
–Available as e-book via amazon.nl or bol.com
Signing up for classes and exams
You can enrol via uSis . More information about signing up for classes and exams can be found here.
There is only limited capacity for external students. Please contact the programme Co-ordinator
Contact information
Programme Co-ordinator: ms. E. Caubo