Marketing Analytics

Course description Marketing Analytics
Year: 2018-2019
Catalog number: 4353MAAN3
Teacher(s):
  • N. van Weeren MSc
Language: English
Blackboard: Yes
EC: 3
Level: 500
Period: Semester 2, Block IV
Hours of study: 21:00 hrs
  • Yes Elective choice
  • Yes Contractonderwijs
  • Yes Exchange
  • Yes Study Abroad
  • No Evening course
  • No A la Carte
  • No Honours Class

Admission requirements

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.

Description

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.

Course objectives

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.

Timetable

The schedule can be found on the Leiden University student website

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

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 these components:
* Participation (presence + interaction): 20%
* Homework assignments: 20%
* Paper: 60%
Every component has to be graded at least 5.0 and overall average at least 5.5 to pass (see Course and Examination Regulations).

Blackboard

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)

Signing up for classes and exams

You have to sign up for classes and examinations (including resits) in uSis. Check this link for more information and activity codes.

There is only limited capacity for external students. Please contact the programme Co-ordinator

Contact information

Programme Co-ordinator: ms. Esme Caubo

Languages