Other sections in Masters:
Using management science to support marketing decision making and planning.
Marketing Analytics is ideal for you if you wish to work on marketing problems to support decision making in organisations. We emphasise the analytical aspects of marketing problems, as well as the fundamentals of Management Science and Marketing. There is a large demand for business and market analysts with good quantitative and modelling skills, particularly in retail and marketing. Graduates from this course can expect to work as market or marketing analysts, business consultants or analysts, and marketing researchers.
This is a flexible degree scheme in which you choose subjects that interest you and also attend compulsory core modules. You will develop marketing and business modelling skills in SAS, SPSS and Excel, and will cover topics such as forecasting, market research and data-based analysis, customer relationship management and data mining.
You will improve your professional skills as well as your analytical abilities. The taught modules end in May and are followed by a significant real-life project that runs until mid-September. In this, you will develop professional skills by working for 16 weeks as a consultant to one of our client companies, whilst being supervised by a member of staff. This is an excellent preparation for the world of work.
12-month course, starts in October
Average class size
Designed for graduates interested in careers in marketing analysis, marketing research, CRM and credit analysis
The MSc in Marketing Analytics consists of compulsory and optional modules, plus a project. The compulsory modules ensure that you have a firm grounding in important subjects. The wide range of optional modules allows you to specialise in subjects that particularly interest you.
During your first term from October to December, you will study four core modules:
This module aims to introduce you to the marketing discipline, briefly explaining the history of marketing practice and the contemporary issues that are beginning to transform those practices. In the process you will explore some of the core concepts that form the foundation of the field and see how these have been developed as practical and useful theoretical tools and devices.
Students will be introduced to the latest version of Microsoft spreadsheet: Excel 2016.
The module will cover the basics of ‘dynamic’ model building, including skills such as data visualization, handling, filtering and analysis, using a variety of key functions and charting tools. In addition there are advanced techniques such as optimisation, simulation, and the use of Visual Basic for Applications (VBA) to automate models and construct decision support models. The course will also introduce the basics of simulation programming, using VBA. The course will make extensive use of case-studies and workshop-orientated learning tasks.
At the heart of many management problems lies data that need to be described, analysed and interpreted. An operational researcher/management scientist needs to be able to do this soundly and efficiently. The overall objective of this module is to develop the ability to describe, analyse and interpret data soundly, making effective use of computer software. This module assumes some prior knowledge of statistics (although we know that some of you have plenty!) and some familiarity with simple algebra and calculus is assumed. This module emphasises the practical application of statistical methods, and makes extensive use of SPSS.
This module introduces you to the basic principles of marketing and places particular emphasis on the role of analytical modelling in supporting marketing decisions in organisations.
It is hoped that you will be in a position at the end of this course to fully appreciate how analytical models enable managers to make better marketing decisions instead of relying on their intuition or gut feel. We also provide a brief overview of some of the key topics on other modules that you will cover in more detail in the second term.
The second part of the course runs from January to May, and includes at least three from the following four modules, plus a choice of two or three optional modules on the next tab (totalling six modules).
Every managerial decision concerned with future actions is based upon a prediction of some aspects of the future. Therefore Forecasting plays an important role in enhancing managerial decision making.
After introducing the topic of forecasting in organisations, time series patterns and simple forecasting methods (naïve and moving averages) are explored. Then, the extrapolative forecasting methods of exponential smoothing and ARIMA models are considered. A detailed treatment of causal modelling follows, with a full evaluation of the estimated models. Forecasting applications in operations and marketing are then discussed. The module ends with an examination of judgmental forecasting and how forecasting can best be improved in an organisational context. Assessment is through a report aimed at extending and evaluating student learning in causal modelling and time series analysis.
This module develops modelling skills on synthetic and empirical data by showing simple statistical methods and introducing novel methods from artificial intelligence and machine learning.
The module will cover a wide range of data mining methods, including simple algorithms such as decision trees all the way to state of the art algorithms of artificial neural networks, support vector regression, k-nearest neighbour methods etc. We will consider both Data Mining methods for descriptive modelling, exploration & data reduction that aim to simplify and add insights to large, complex data sets, and Data Mining methods for predictive modelling that aim to classify and cluster individuals into distinct, disjoint segments with different patterns of behaviour.
The module will also include a series of workshops in which you will learn how to use the SAS Enterprise Miner software for data mining (a software skill much sought after in the job market) and how to use it on real datasets in a real world scenario.
Marketing research enables an organisation to better understand the market environment.
The main objective of conducting marketing research is to enable decision makers to make better decisions than if the latter were based only on intuition and guesses. At the heart of marketing research is information which facilitates the decision making process.
In this module we focus on the collection and use of information in marketing research but pay particular attention to the context of the problem. You will also have the opportunity to learn some transferable skills that relate to gathering and using information for business intelligence and decision making.
This module builds on the concepts learnt in this first term and covers topics such as visualising and understanding multidimensional problems; markets and competitors, market share modelling, marketing forecasting, retailing, promotional modelling and game theory for marketing.
This module will help students to apply the concepts of marketing modelling in its application to complex consumer and business markets. Students will also appreciate how such type of modelling work can help managers make better technical marketing-related decisions such as store location or the amount and type of promotional activity to promote brands.
You will also choose two or three optional modules from those listed below.
This module enables you to develop an appreciation of the dynamics of the international marketplace and the complex and challenging forces shaping this environment. It uses the principles and frameworks underpinning international marketing to assess how key marketing and consumer behaviour theories, models and concepts apply in an increasingly globalised and internationally competitive marketplace.
This module provides an integrated and critical overview of key concepts and techniques associated with ‘Marketing and consumer behaviour online’. It is not assumed that students have prior academic and/or applied grounding in marketing, and marketing issues related to the e-business environment (if necessary, background reading and materials will be provided). In this module we put emphasis on the fact that an online environment marketing strategy is, or is becoming, increasingly critical for most organisations. However, the integration of marketing within the e-business technological platform and interface tends not to be given enough attention in organisations. Marketing managers need to be conversant and confident with the dynamics of online consumer behaviour and the current limitations of this new channel environment, but without neglecting the basis of consumer behaviour.
This module will develop an understanding of the strategic role that sourcing decisions can play in supply chain management in order to gain sustainable competitive advantage in a global environment. It will look at alternative ways in which the upstream supply chain can be successfully configured and coordinated in different contexts, including the strategic role of IT in supporting this.
The aim of this module is to teach the skills required to apply simulation successfully to help improve the running of an organisation, whether in the public or private sectors, manufacturing or services.
Modern simulation packages are a valuable aid in building a simulation model and this module will employ a widely used and up-to-date discrete event simulation package. However, without the proper approach, the results of a simulation project can be incorrect or misleading. This module looks at each of the tasks required in a simulation project. It emphasises the practical application of simulation, with a good understanding of how a simulation model works being an essential part of this.
Python is a simple, yet very powerful, high level computer programming language that becomes immensely popular in our days. It is widely used in many scientific areas for data exploration and at the same time it is the preferred programming language among a wide range of modern organisations.
This course is an introduction to computer programming using Python for students without any prior programming experience. It introduces the basic principles of computer programming but is doing it with an emphasis on examples from the areas of business analytics and finance.
SAS is one of the leading providers of business intelligence and predictive analytics software.
There is increasing demand in the graduate labour market for analysts with SAS programming skills.
This module is primarily a 'support' course on the various postgraduate programmes and courses. This module will provide an introduction to SAS programming whilst giving the participants experience of using an advanced data base and a versatile statistical programming language. SAS, through our relationship with them, are supporting this course.
This module will consider a number of Operational Research (OR) techniques which have in the main been devised to tackle problems in which it is important to model the effects of stochastic influences.
The aim will be to provide sufficient information about the techniques to give an understanding of their potential and limitations, and of the steps likely to be involved in using them. The techniques will be selected from: Markov processes; stochastic simulation; queuing models; risk analysis and replacement, inspection and maintenance models. The first two techniques are generally applicable in many problem areas, whereas the latter three refer to models specifically developed for particular sorts of problems.
Optimisation, sometimes called mathematical programming, has applications in many fields, including operational research, computer science, statistics, finance, engineering and the physical sciences. Commercial optimisation software is now capable of solving many industrial-scale problems to proven optimality.
The module is designed to enable students to apply optimisation techniques to business problems. Building on the introduction to optimisation in the first term, students will be introduced to different problem formulations and algorithmic methods to guide decision making in business and other organisations.
The purpose of this module is to understand and use in somewhat simple contexts, some of the basic models from logistics.
Algebraic formulations will be used as vehicle for describing models and discussing their relationships. There will be a focus on modelling, the use of professional software, and the understanding of results. For problems where exact solutions are hard to achieve even for simple instances of the problem, heuristics will be discussed. The main topics which are covered are: facility location, network design, warehousing and inventory control, vehicle routing and scheduling, and environmental considerations in transportation and distribution operations.
Successfully managing its operations and its supply chain activities is central to any organisation’s ability to compete for revenues and resources.
The aim of this module is to introduce a series of innovations and capabilities in operations and supply chain management that have been associated with excellence and outstanding performance.
These include strategic procurement management, lean production principles, supply chain contract design and negotiation, supply chain financing and risk management, and managing business process flows.
The module will look at these subjects in both manufacturing and service contexts.
The globalisation of operations and supply chains is today prominent in almost every industry sector. Organisations are facing up to the huge challenge of learning to compete in this wider context and are internationalising in greater numbers, faster, and in more ways than ever before. This module will develop an understanding of the strategic role that sourcing decisions can play in supply chain management in order to gain sustainable competitive advantages in a global environment. It will look at alternative ways in which the upstream supply chain can be successfully configured, coordinated and managed in different contexts, including the strategic role of IT in supporting this. We will also be exploring offshoring vs re-shoring and the total cost of ownership to determine the most efficient way to source within a supply chain. The course will also look at some key issues relating to supply chain sustainability in order to develop an appreciation of their importance to sourcing decision making.
Pricing is a fundamental business discipline that integrates corporate strategy, marketing, finance, and operations across the whole business process. It is one of the most powerful but least understood disciplines within many organisations. This course aims to introduce this multi-discipline subject to the masters students across different programmes related to Management Science and to those who plan to start their careers as business analysts.
In this pricing course, we will largely focus on the quantitative skills in pricing analytics. We will also associate the pricing analytics to behaviour science (e.g., consumer behaviour) and social science (e.g., social networking). The course has three levels of analytics (descriptive/predictive/prescriptive), connecting theory to practice through the business contexts and data analysis.
From May to September you will work on a 60-credit project, usually based in an organisation, on which you write a report for the client and an academic dissertation. You will submit it at the start of September, at the end of your Masters programme.
At the end of the taught modules on all three Masters programmes in Management Science you will complete a three-four month project, which will be organisation-based (with organisations from both public and private sectors), organisation-driven or research-based. Eligibility for organisation-based projects will be competitive and based on merit and marks achieved.
These projects are an essential part of the learning on each of the MSc programmes and give you a chance to use the analytical methods and professional skills developed on the taught modules. Completion of a project will be an asset on your CV and some students go on to work for the organisation that sponsors their project. Recent examples of projects include simulation, product launch, optimisation and risk analysis at Jaguar Land Rover, Lego and Barclaycard.
SAS, a world leader in business intelligence software, sponsors two student prizes.
Our programme-specific scholarship for 2019 entry include the Academic Excellence, UK-EU and International scholarships aimed at high-achieving students with a strong academic or personal profile. We'll automatically consider you for these scholarships when you apply and if you are shortlisted we'll be in touch with the next steps, so it's best to apply as soon as possible. We also offer other scholarships - visit our Apply for Masters page to find out more.
The Department of Management Science provides an extensive careers service and postgraduate students may expect to be eagerly sought after by employers. More than 40 major employers normally contact the Department each year to recruit. Many former students now hold senior positions in their organisation – as management scientists or as managers.
The Department has strong links with companies and organisations, many of which employ our alumni. Companies visit the department to make presentations and interview students as part of their selection process. In other instances, opportunities are advertised via the noticeboards and electronically on the student VLE. You are also asked to contact company recruitment offices directly.
Management Science, Operational Research, Logistics, Supply Chain and Market Analysis are seen by leading companies as the start of a fast track to promotion for high flyers with a numerate or scientific background. Experience has shown that if you have a postgraduate qualification, you will not only obtain a better starting job but will also receive more rapid promotion.
Organisations in which students have been offered jobs in the last three years or so include:
Accenture, Calanais Energy, Cap Gemini, Hartley McMaster, KMPG, Indicia, OEE Consulting Ltd, PA Consulting, QinetiQ.
Santander, Barclaycard, Halifax plc, HSBC, Lloyds TSB, NatWest Consultancy, PricewaterhouseCoopers, Royal Bank of Scotland, Woolwich.
AEA Technology - Rail, British Airways, BT, Digital, London Underground, National Air Traffic Services, T-Mobile, Royal Mail, Post Office Consulting.
Allied Domecq plc, Great Universal Stores, Littlewoods, J Sainsbury, Tesco, Unipart.
Corus, Coats Viyella, GlaxoSmithKline, Nestle, Procter & Gamble, Scottish Courage.
CIS Consultancy - Metropolitan Police, Ministry of Defence - DSTL, Home Office, North Essex Health Consortium, Oxfam.
MSc Management Science and Marketing Analytics (Marketing Analytics), 2017
MSc Management Science and Marketing Analytics (Marketing Analytics), 2013
MSc Management Science and Marketing Analytics (Marketing Analytics), 2012
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