The curriculum comprises four semesters over two years. It provides the use of data and of advanced data science methods to create sound and accurate solutions and recommendations for a range of marketing problems, and the reflective and critical thinking skills to be able to propose an appropriate solution that suits all stakeholders.
Year 1
Cover the foundations for insights, data, AI and marketing strategy. There will be a focus on:
- substantive theory of consumer and company behaviours
- machine learning and AI methods for marketing data
- insight and action generation for the involved stakeholders.
At the end of this course, you will be able to:
- Understand the data science process (Cross-Industry Standard Process for Data Mining)
- Analyse marketing problems from a practical perspective
- Apply basic data science methods to marketing problems
- Obtain marketing-relevant insights using data science methods
Course content:
- Marketing and data fundamentals: marketing problems and decision-making; data wrangling; descriptive statistics; data visualization
- Programming: data structures; control flow; loops; functions
- Statistics: linear regression; logistic regression; principal components analysis; cross-validation
At the end of this course, you will be able to:
- Select the most suitable techniques to solve real-world problems by evaluating learning algorithms and performing model selection
- Apply explainable AI methods to interpret the model results
- Create policy recommendations for organisations by transforming results produced by machine learning algorithms into insights
Course content:
- Penalised regression and performance estimation: overfitting, complexity control, performance estimation, penalised linear and logistic regression
- Decision trees and ensembles: decision trees, random forests, boosting
- Neural networks: multilayer perceptrons, deep learning
- Explainable AI: variable importance, partial dependence, accumulated local effects, Shapley values
At the end of this course, you will be able to
- Analyse customer and market data using advanced marketing analytics to derive actionable insights
- Employ segmentation, targeting, and positioning strategies using advanced marketing frameworks
- Develop innovative data-driven marketing mix strategies to address real-world business challenges
- Synthesize sustainable and ethical marketing strategies by integrating business objectives with societal and environmental considerations
Course content:
- Customer and market insights: introduction to strategic marketing, the five Cs of marketing (company, customers, competitors, collaborators, and climate)
- Segmentation, targeting and positioning.
- Strategic marketing mix decisions: product management; pricing and channels; advertising and promotion; sustainable marketing.
Year 2
Do a deep dive into the execution of marketing strategy. There is also a course that focuses on bringing organisations forward by considering current societal and organisational forces and the inherent strategic uncertainty in the future.
At the end of this course, you will be able to
- Understand the concepts, theory, and key issues for managing omnichannel customer experiences
- Solve business problems in omnichannel marketing and customer experience management
- Appraise the go-to-market systems of companies or organisations
- Demonstrate analytical and business communication skills, oral and written
Course content:
- Channel Selection: course overview; going to market; the go-to-market model and its evaluation; channel selection; selling direct or through intermediaries; managing multiple channels; grey markets; and response to grey markets.
- Channel Management: managing channel partners; vertical integration (i.e., make versus buy); competing in the channel; direct-to-consumer (DTC) channels; retailing; e-commerce; and omnichannel.
- Management within the channel: cutting out the middleman; evaluating DTC channels; new intermediaries in distribution.
- Channel experience: understanding the customer experience; market basket analysis; recommendation systems (e.g., content, and collaborative filtering approaches).
At the end of this course, you will be able to
- Understand the differences between correlation, causation, and reverse causation
- Design a data collection process that ensures a causal analysis is feasible
- Appraise the assumptions needed to ensure proper causal inference
- Interpret estimates of causal effects, given a set of assumptions.
Course content:
- Causal inference fundamentals: introduction; instrumental variables; synthetic control; matching; regression discontinuity.
- Experimentation and optimisation: A/B testing; multi-armed bandits; conjoint analysis.
- Causal machine learning: causal machine learning methods; marketing applications.
At the end of this course, you will be able to
- Apply machine learning methods to measure responses to marketing actions
- Evaluate data-driven insights into competitor responses, market structure and market heterogeneity
- Evaluate different marketing mix decisions using data
Course content:
- Introduction to marketing mix models: effects of pricing, promotions, and advertising; univariate time series.
- Dynamics, attribution, and allocation: advertising stock; interactions and attribution; pricing and budget allocation with aggregate-level data.
- Advanced marketing response analytics: market structure with cross-elasticities; machine learning for multivariate time series; individual choice models.
At the end of this course, you will be able to
- Use a step-by-step methodology to achieve strategic foresight
- Understand how consumers respond to emerging technologies
- Develop innovative ideas based on the analysis of the future
- Evaluate the ethical, legal, and privacy challenges that arise from the use of data and in AI in marketing.
Course content:
- Foresight fundamentals: strategic foresight theory; step-by-step research methodology
- Innovation: new business models; innovation and emerging technologies; consumer responses to emerging technologies
- Ethical, legal, and privacy issues: ethical and legal issues; privacy issues with AI and data in marketing
- Team presentations, key takeaways and ‘your future vision’ ideas
What career development advantages do you gain by choosing this MSc?
The adoption of artificial intelligence and the growing use of data is positively affecting the job market in several ways, most notably by creating a strong new demand for skilled professionals. This trend is evident across various sectors and is reshaping workforce dynamics.
For example, graduates can enter roles such as:
- data translator (working at the intersection of marketing and data science),
- marketing analyst
- marketer or digital marketer, and other roles within the consulting industry.
When you graduate from this online master programme, you will be equipped with new knowledge, the latest skills and a fresh perspective.
Knowledge
- Hold substantive knowledge on company and consumer behaviour.
- Know and use the methodological aspects of data and AI.
- Carry a critical understanding of the strategies and responsibilities that organisations face in relation to how data and AI are used to support marketing decisions.
Skills
- Be able to effectively use data and AI support marketing decision making.
- Be competent in getting actionable insights from data-driven analyses
- that are grounded in theory.
- Work collaboratively to tackle the challenges and responsibilities that come with access to data and AI.
Reasoning
- Have a solution-oriented approach to using data and AI to improve marketing in their own organisations.
- Participate in societal and organisational debates relevant to the use of data and AI for marketing or start new enterprises to create positive change.
This online master is taught by experienced faculty members who are experts in their fields and active researchers. Get to know a few of them and what you can learn with them.
This online master programme gives you the opportunity to not only learn from some of the best teachers, but also to regularly engage and interact with them.
Dr Pieter Schoonees
Academic Director
Module: Foundations of Marketing, Data and AI
Prof. Dr Bas Donkers
Programme Coordinator
Dr Anastasija Tetereva
Assistant Professor
Module: Machine Learning Methods
