This course provides an introduction to the application of information technology and data science in the marketing domain, also known as data-driven marketing (DDM) or marketing analytics.
This multi-disciplinary field has seen a growing interest over the last years. Businesses and organisations are facing both huge opportunities ánd 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 and an omni-channel approach.
Here is where Marketing Analytics comes into play, by building a holistic customer view based on the digital footprint, advanced analytics and machine learning/AI techniques, and using that to drive highly targeted customer interactions.
Yet, recent incidents and the new EU privacy regulation demand that consumers get more insight and control over their personal data. This calls for a more balanced exchange of data for value to the customer, based on the principles of privacy by design.
The hybrid nature of marketing analytics and the rapid developments in technology call for agile, cross-functional approaches and a blending of marketing and IT skill sets.
We will cover a broad spectrum of topics by linking current business practices to concepts and vice versa. Starting with the fundamentals of customer-centricity, we will explore how data is connected to customer journeys, address customer data management including privacy aspects, (digital) marketing technologies and advanced analytical techniques. We will use a hands-on approach to understand the practical application of marketing analytics in some typical use cases. Finally we will explore the organisational impact for realising the huge potential of marketing analytics.
Students are expected to have a good understanding of the basic concepts, frameworks and instruments of Marketing Management, i.e. have attended the course Marketing & Corporate Communications with a sufficient end grade.
By the end of the course, the student should be able to:
understand the strategies, basic concepts, frameworks and processes of DDM and marketing analytics;
identify and apply customer data (incl. privacy regulations);
identify and use DDM technologies to improve marketing performance and customer engagement;
show their ability to organise, plan and design for data-driven marketing practice.
The schedule can be found on the Leiden University SBB website
Detailed table of contents can be found on uSis.
Mode of instruction
The programme will consist of 6 lectures that cover the following topics.
1. Introduction: course overview, data-driven marketing concepts, trends, 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: Marketing software platforms, Analytical tools, Big Data platforms and technologies;
5. DDM Lab: Customer Segmentation, Predictive modelling, Machine Learning, Web analytics;
6. Organising for DDM: Marketing and ICT organisations, Sourcing, Agile marketing, Marketing Science skills
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 these components:
Participation (presence + interaction): 20%
Homework assignments: 20%
Every component has to be graded at least 5.0 and overall average at least 5.5 to pass (see Course and Examination Regulations).
The teacher will inform the students how the inspection of and follow-up discussion of the exams will take place.
The presentations and background articles will be available on Blackboard. References to articles and books will be provided during the lectures.
- 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)
Their are only 30 places left for this course. Full is full.
You need to do two steps:
1. Fill in this link;
2. Sign up for classes and examinations (including resits) in uSis (in time).
There is only limited capacity for external students. Please contact the programme Co-ordinator
For all your questions you can contact firstname.lastname@example.org
Note: If you are an ICTiBPS student, you can contact the programme coordinator of ICTiBPS for any questions about your program.
Students are responsible for enrolling/unenrolling themselves for (partial) exams/retakes.
Students are responsible for enrolling themselves for (partial) exams/retakes.
The deadline for enrolling for an exam/retake is 10 calendar days before the exam/retake takes place (exam date - 10 = deadline enrolling date).
Students who do not enroll themselves for an exam/retake by the deadline are not allowed to take the exam/retake.
Students fail the course if any of the components that make up the final mark of the course is assessed below 5.0.
The final grade is expressed as a whole or half number between 1.0 and 10.0, including both limits. The result is not to be expressed as a number between 5.0 and 6.0.
If one of the components of the final mark constitutes a component that assesses attendance or class participation, students cannot take a retake for this component. Therefore, students fail the course if their mark for this component is less than 5.0.
It is not possible to do retakes for group assignments. Therefore, if students fail the group assignment component, they fail the course.
Students pass the course if the final mark is 6.0 or higher (5.49 will rounded down to a 5 and a 5.5 will be rounded up to a 6.0).
For courses, for which class participation is an assessment component, students may not be penalised for an absence if the student has a legitimate justification for this absence. The student must notify the program coordinator via email (email@example.com) of such an absence BEFORE the lecture, describing the reason for missing the lecture. If the student does not notify the program coordinator before the lecture, the student will be penalised. Students may be required to provide further documentation to substantiate their case, and class attendance requirements are only waived under exceptional circumstances such as illness.
Students who are entitled to more exam/retake time must report to firstname.lastname@example.org 10 days before the exam/retake takes place.