Skip to main content

Unit information: Big Data in Marketing Intelligence in 2020/21

Please note: you are viewing unit and programme information for a past academic year. Please see the current academic year for up to date information.

Unit name Big Data in Marketing Intelligence
Unit code EFIMM0059
Credit points 20
Level of study M/7
Teaching block(s) Teaching Block 2 (weeks 13 - 24)
Unit director Dr. Pantano
Open unit status Not open




School/department School of Management
Faculty Faculty of Social Sciences and Law

Description including Unit Aims

This unit aims to introduce students to the purpose, application and value of market data to an organisation and particularly to those working in marketing. It will explore the different forms that data takes and the relative use it has within different contexts. A distinction will be made between large datasets that can be contained within conventional analytical frameworks and ‘big data’ (i.e. Google, Facebook) whose volume, velocity and variety means that it presents data management challenges and cannot be contained within an easily formatted structure. It reviews the range of ways in which data might be identified and harvested and explores ways of filtering data to optimise the quality of the final dataset. Methods of combining data sets will be considered as well as ways of enhancing them to facilitate evaluation and analysis. Various approaches to data modelling will be considered and their application in a range of marketing contexts will be critically assessed.

Intended Learning Outcomes

On completion of this unit, students will be able to:

ILO 1: Distinguish between data contained in large data sets and ‘big data’ and reflect upon the practical, legal and ethical challenges associated with the collection, management and analysis of each.

ILO 2: Given a set of market insight objectives, compose a strategy for identifying and harvesting appropriate data.

ILO 3: Distinguish between data that has value and relevance to a given context and that which has not, and synthesise data from multiple sources into a single database.

ILO 4: Consider various methods of data presentation, analyse the data and present the results in a form that is appropriate and comprehensible to a given set of stakeholders.

Teaching Information

Teaching will be delivered through a combination of synchronous and asynchronous sessions including lectures, tutorials, drop-in sessions, discussion boards and other online learning opportunities

Assessment Information

Summative assessment on this unit is comprised of two elements:

50% data analysis group report (2,500 words) (ILOs 2, 3, 4) and

50% Individual Report (2,500 words) (ILOs 1, 2, 3).

In the group report, students will conduct a data analysis exercise for a certain (existing) company. In particular, the report will include (i) the clear identification of data that has value and relevance to the given contexts and that which has not, (ii) critical discussion of the range of possible strategies, (iii) identification of appropriate data and related synthesis from multiple resources, (iv) effective analysis with the most appropriate method, and (v) presentation of the in a form that is appropriate and comprehensible to the given set of stakeholders.Students would also provide evidence of equality of contribution (peer assessment), it should include the report progress of group coursework, which includes the participation rate from each individual. Participation and individual contributions will be assured and assessed through in class group activities, formative feedback and assessment, peer contributions and questioning individuals. In this way, not contributing students at an early stage will be identified and properly informed about subsequent warning actions and possible fail in receiving the group mark.

In the individual report, students will individually (i) identify the main practical, legal and ethical challenges associated with the collection, management and analysis of the data (conducted in the previous group exercise), (ii) compare and contrast the data that has value and relevance to the given context and that which has not, and (iii) recommend to the CEO the possible marketing actions to improve the value delivered to customers by providing attractive shopping experience to customers, and on possible additional data (from which sources and why) to be analysed with the related analytical tool to enhance the quality of recommendations for the CEO.

Reading and References

Core text for this Unit

Marr, B. (2015). Big Data: Using SMART big data, analytics and metrics to make better decisions and improve performance. John Wiley & Sons.

Recommended reading

Marr, B. (2016). Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results. John Wiley & Sons.

Kitchin, R. (2014), The data revolution: big data, open data, data infrastructures & their consequences, SAGE Publications Ltd, London.

Foreman, J.W. (2013), Data Smart: Using Data Science to Transform Information Into Insight, John Wiley and Sons, London

Liebowitz, J. (2013), Big data and business analytics, CRC Press, Boca Raton, FL.

Academic and Practitioner Journals

Journal of Management Information Systems

Journal of Management Science

Decision Support Systems

Journal of Consumer Research

European Journal of Information Systems

Computers in Human Behavior

Information and Management

International Journal of Information Management

Applied Intelligence 

Harvard Business Review

The Economist



Other news media

The BBC website (

Any/all broadsheet newspapers