The University of Southampton
Courses

MANG3061 Data Mining for Marketing (SIM)

Module Overview

Companies nowadays have collected a large volume of data from various sources. This module aims to introduce the key concepts of using the ‘Big Data’ to conduct marketing activities. In particular, it focuses of the use of data mining techniques to manage customer relationship. The CRM issues of profiling/segmentation, communications, campaign measurement, satisfaction, loyalty, profitability and other current issues will be addressed in turn with discussions of how data mining and analytics can be used to improve decision making for each. Students will get hands on experience using data analytic techniques to make CRM decisions each week thereby equipping them to manage real customer groups. To assist with data analytics students will be introduced the SPSS/PASW as a current piece of analytics software widely used in marketing departments and organisations.

Aims and Objectives

Module Aims

This module will introduce the key concepts of data-based marketing with a particular focus of their application to customer relationship management (CRM). The CRM issues of profiling/ segmentation, communications, campaign measurement, satisfaction, loyalty, profitability and other current issues will be addressed in turn with discussions of how data mining and analytics can be used to improve decision making for marketing activities. Students will get hands on experience using data analytic techniques to make CRM decisions, thereby equipping them to manage real customer groups. To assist with data analytics, students will be introduced to SPSS/PASW as a current piece of analytics software widely used in marketing departments and organisations.

Learning Outcomes

Knowledge and Understanding

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

  • The potential of data mining for gaining actionable customer insights and supporting CRM
Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

  • Explain concepts clearly and critically apply findings;
  • Plan the resources needed to evaluate and analyse data, and to disseminate the outcomes
Transferable and Generic Skills

Having successfully completed this module you will be able to:

  • Demonstrate the ability to persuade, convince and argue effectively
  • Analyse datasets to provide insight into CRM activities
  • Manage time and tasks effectively in the context of individual study and group work activities and take responsibility for carrying out agreed tasks in preparing a group project on an agreed topic.

Syllabus

• Data preparation: Data storage, extraction, integration and pre-processing • Key marketing tasks, e.g., segmentation and profiling • Exploring/visualising data using software • Descriptive vs. predictive analytics: Application of analytical techniques to marketing decisions

Learning and Teaching

Teaching and learning methods

Typically you will study the module over 4 weeks with the first 2 weeks having intensive teaching in the form of lectures, and seminars which will include individual and group practical exercises, workshops, case studies. The following 2 weeks will be organised self-study (reading designated texts, journal papers and critical discussion with peers), and preparation of assessed coursework. You will be expected to play a highly interactive part in lectures and seminars. Where an examination is involved this will take place at a designated time. Sample exercises will prepare you for assessment. As with all programmes in the Faculty, student learning will also be supported by published course materials and Blackboard: the University's virtual learning environment.

TypeHours
Preparation for scheduled sessions18
Lecture24
Practical classes and workshops10
Completion of assessment task54
Follow-up work24
Wider reading or practice20
Total study time150

Resources & Reading list

Linoff, G.S. and Berry, M.J.A (2011). Data mining techniques: for marketing, sales and customer relationship management. 

Field, A. (2009). Discovering Statistics Using SPSS. 

Baesens, B. (2014). Analytics in a Big Data World. 

Davenport, T.H. & Harris, G. H. (2007). Competing on Analytics: the New Science of Winning. 

Winston, W.L. (2014). Marketing Analytics: data driven techniques with Microsoft Excel. 

Assessment

Formative

Exercise

Summative

MethodPercentage contribution
Examination  (2 hours) 50%
Group project  ( words) 50%

Repeat

MethodPercentage contribution
Individual Coursework  ( words) %

Referral

MethodPercentage contribution
Coursework  ( words) 100%

Repeat Information

Repeat type: Internal & External

Costs

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:

Books and Stationery equipment

Recommended texts for this module may be available in limited supply in the University Library and students may wish to purchase the core/ recommended 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 www.calendar.soton.ac.uk.

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