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The University of Southampton

MANG6229 Multivariate Statistics for Data Mining

Module Overview

The module aims to introduce you to the key concepts of marketing metrics and how they can be used effectively in the business environment within which marketers now operate. To provide a course of study which engages you in the practice and theory of marketing metrics and develop the quantitative analytical skills needed to manage marketing productivity.

Aims and Objectives

Module Aims

to introduce you to methods for data exploration & data reduction. These include both multivariate statistical methods and heuristic methods from computational intelligence and artificial intelligence. These methods aim to simplify and add insights to large, complex data sets. They also provide methods of prediction, classification, and clustering. This allows us to forecast which individuals are likely to perform a certain event (churn, purchase etc.) or indicate ways of categorising individuals into distinct, disjoint segments with different patterns of behaviour. The module will introduce you as to how SAS Enterprise Guide or SPSS can be used to undertake these tasks and will ensure you are able to interpret the statistical output.

Learning Outcomes

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

  • the types of multivariate analysis that arise in marketing;
  • interpreting the output of statistical techniques used for the main multivariate applications;
  • explaining and being able to select the appropriate techniques for addressing the multivariate problems that arise.
Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

  • identify the statistical models appropriate for analysing the various decisions that confront a marketer;
  • understand the strengths and limitations of the various models used;
  • assess the relevance of the statistical package output to the decisions being addressed.
Transferable and Generic Skills

Having successfully completed this module you will be able to:

  • demonstrate an ability to use SAS Enterprise Guide or SPSS and to interpret its output in the relevant techniques;
  • manage time and tasks effectively in the context of individual study and team work;
  • explain concepts clearly and critically apply findings and disseminate the important aspects of a statistical analysis.


Introduction to data mining. The SEMMA process of data mining. Data mining methods for data reduction: cluster analysis, principal component analysis and factor analysis. Data methods for regression. Data mining methods for classification: discriminant analysis, logistic regression, decision trees and artificial neural networks for classification. Evaluating classification accuracy. Data methods for segmentation. Patterns and association rules: market basket analysis. Use of SAS Enterprise Guide or SPSS to undertake these tasks.

Learning and Teaching

Teaching and learning methods

You will learn through a combination of lectures and/or guest presentations, group work, practical (computer-lab) sessions (where needed), and self-study.

Independent Study60
Total study time75

Resources & Reading list

Hastie, T., Tibshirani, R., and Friedman, J. (2013). The Elements of Stastical Learning. 

Matignon R (2007). Data Mining using SAS Enterprise Miner. 

Hand D, Mannila H, Smyth P (2001). Principles of Data Mining. 



Peer Group Feedback


MethodPercentage contribution
Coursework  (3000 words) 100%


MethodPercentage contribution
Coursework  (3000 words) 100%


MethodPercentage contribution
Coursework 100%

Repeat Information

Repeat type: Internal & External


Costs associated with this module

Students are responsible for meeting the cost of essential textbooks, and of producing such essays, assignments, laboratory reports and dissertations as are required to fulfil the academic requirements for each programme of study.

In addition to this, students registered for this module typically also have to pay for:


Recommended texts for this module may be available in limited supply in the University Library and students may wish to purchase the mandatory/additional reading text as appropriate.

Please also ensure you read the section on additional costs in the University’s Fees, Charges and Expenses Regulations in the University Calendar available at

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