Digital Marketing Data Mining and Analytics - BUSN9138

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Module delivery information

Location Term Level1 Credits (ECTS)2 Current Convenor3 2024 to 2025
Spring Term 7 15 (7.5) Yu-Lun Liu checkmark-circle


This module covers data mining techniques and their use in marketing decision making. In this module students will gain practical experience and will critically apply software commonly used in contemporary organisations to support marketing strategies based on marketing data.

Topics to be covered are likely to include: Introduction to data mining (e.g., cluster analysis, PCA/factor analysis) for digital marketing; Data pre-processing, visualisation and exploratory analysis used to provide insight into marketing activities; Key marketing tasks: e.g., segmentation, profiling; Data Mining Methods for Classification; Data mining predictive models and their application; Accessing and collecting data from the Web and introduction to text mining; Web-analytics and data mining models in real-world applications.


Contact hours

24 hours lectures and labs



Method of assessment

Individual Assignment (2000 words) (40%)
Individual Data Analysis Excel Project (60%)

Indicative reading

Grigsby, M (2015). Marketing analytics: A practical guide to real marketing science. 1st ed. Kogan Page. ISBN-10: 0749474173.

Witten, I. H., Frank, E., Hall, M. A., Pal, C. J. (2016). Data Mining: Practical Machine Learning Tools and Techniques. 4th ed. Morgan Kaufmann. ISBN-10: 0128042915

See the library reading list for this module (Medway)

Learning outcomes

Demonstrate a systematic understanding of the potential of data mining for gaining marketing insight and supporting marketing decision making; Critically evaluate concepts and tools needed to analyse and interpret digital marketing data; Practice with leading data mining methods and their application to marketing challenges in a variety of contexts; Critically apply the practical experience and the theoretical insights needed to reveal patterns and valuable information embedded in large data sets to support digital marketing decision-making and activities.


  1. Credit level 7. Undergraduate or postgraduate masters level module.
  2. ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
  3. The named convenor is the convenor for the current academic session.
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