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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. Relevant marketing issues such as customer surveys, profiling/segmentation, communications, campaign measurement, satisfaction, loyalty, profitability, social media and other current topics will be discussed with regard to how data mining and analytics can be used to improve marketing decision making. Students will get hands on experience using data analytic techniques using software commonly used in marketing departments and organisations. Thus, this module seeks to equip students with key skills needed to manage real marketing decisions based on marketing data.

Aims and Objectives

Module Aims

to introduce the key concepts of data-based marketing with a particular focus of their application to customer marketing problems. The issues of customer surveys, profiling/segmentation, communications, campaign measurement, satisfaction, loyalty, profitability and social media will be addressed in turn with discussions of how data mining and analytics can be used to improve decision making for marketing activities. You will get hands on experience using data analytic techniques and software to make marketing decisions based on data.

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 marketing insights and for supporting marketing decision making.
Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

  • explain and evaluate concepts and tools needed to evaluate, analyse, and interpret marketing data;
  • plan the resources needed to evaluate and analyse data, critically apply findings, and 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 marketing activities;
  • manage yourself, time, and resources effectively.
Subject Specific Practical Skills

Having successfully completed this module you will be able to:

  • develop technical and analytical skills;
  • apply and critically evaluate marketing concepts by using marketing intelligence techniques to draw practical recommendations.

Syllabus

Topics include • 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. As with all programmes in the Faculty, student learning will also be supported by published course materials and the University's virtual learning environment VLE.

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

In-class activities

Summative

MethodPercentage contribution
Project  (3000 words) 100%

Repeat

MethodPercentage contribution
Project  (3000 words) 100%

Referral

MethodPercentage contribution
Project  (3000 words) 100%

Repeat Information

Repeat type: 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 mandatory/ additional 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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