Companies across many industries and markets are interested in building their capability in the big data area. The unprecedented levels of technological change brought on by the digital revolution mean the skills and approaches that elevated marketing executives to senior roles even a few years ago are no longer sufficient to propel them further. The attractiveness of the MSc Consumer Analytics and Marketing Strategy to the marketplace will result from its practical focus on furnishing firms with graduates that have highly sought-after analytical skills in the big consumer data area. There is recognition among managers that big data specialists require the insight to change strategy decisions. The novelty of this MSc programme is that brings together consumer analytics skills with softer skills necessary to utilise data for marketing strategy decisions. In Semester one, modules will teach the foundations of consumer analytics and marketing strategy theory. Semester two modules place more emphasis on applying high-level analytical tools and techniques to scan, analyse, and solve practical problems. Modules such as Digital and Interactive Marketing and Marketing Research Consultancy Project involve practices with real data and simulations. In Semester three students can either take a Dissertation or a Project that involves using company data provided by the CDRC.
To achieve this, the MSc programme includes unique contributions from the School of Geography (i.e., Centre for Spatial Analysis and Policy) and the Marketing and Management (i.e., Centre for Decision Research, Global and Strategic Marketing Research Centre) Divisions of LUBS.
[Learning Outcomes, Transferable (Key) Skills, Assessment]
View Timetable
Students are eligible for the award if they pass the 150 credits of modules (including any pass for progression modules) and achieve an overall average mark of 50 or above, calculated across the 180 credits of modules.
Students will be required to study the following 120 credits of compulsory modules:
Code | Title | Credits | Semester | Pass for Progression |
---|---|---|---|---|
GEOG5042M | Geographic Data Visualisation & Analysis | 15 | Semester 1 (Sep to Jan) | |
GEOG5917M | Big Data and Consumer Analytics | 15 | Semester 2 (Jan to Jun) | |
GEOG5927M | Predictive Analytics | 15 | Semester 2 (Jan to Jun) | |
GEOG5937M | Applied GIS and Retail Modelling | 15 | Semester 1 (Sep to Jan) | |
LUBS5402M | Consumer Behaviour | 15 | Semester 1 (Sep to Jan) | |
LUBS5403M | Marketing Analytics | 15 | Semester 2 (Jan to Jun) | |
LUBS5453M | Research Methods for Marketing | 15 | Semester 2 (Jan to Jun) | |
LUBS5465M | Marketing Strategy | 15 | Semester 1 (Sep to Jan) |
Students will be required to study 45 credits from List A and 15 credits from List B.
List A - Candidates are required to study 45-credits from the following modules
Code | Title | Credits | Semester | Pass for Progression |
---|---|---|---|---|
GEOG5967M | Dissertation - Consumer Analytics | 45 | 1 Feb to 31 Aug | |
LUBS5499M | Marketing Academic / Applied Dissertation | 45 | 1 Feb to 30 Sep |
List B - Candidates are required to study 15-credits from the following optional modules
Code | Title | Credits | Semester | Pass for Progression |
---|---|---|---|---|
GEOG5255M | Geodemographics and Neighbourhood Analysis | 15 | Semester 2 (Jan to Jun) | |
LUBS5417M | Persuasive Writing and Speechmaking | 15 | Semester 2 (Jan to Jun) | |
LUBS5419M | Advanced Quantitative Research Techniques in Marketing | 15 | Not running in 202425 | |
LUBS5427M | Corporate Social Responsibility and Sustainability | 15 | Semester 2 (Jan to Jun) | |
LUBS5432M | Brand Management and Corporate Identity | 15 | Semester 2 (Jan to Jun) | |
LUBS5435M | Public Relations and Corporate Communications | 15 | Not running in 202425 | |
LUBS5440M | Sales Promotion and Sponsorship | 15 | Semester 2 (Jan to Jun) | |
LUBS5443M | Change Management and Communication | 15 | Semester 2 (Jan to Jun) | |
LUBS5447M | Social Media Marketing | 15 | Semester 2 (Jan to Jun) | |
LUBS5449M | Digital and Interactive Marketing | 15 | Semester 2 (Jan to Jun) | |
LUBS5990M | Machine Learning in Practice | 15 | Semester 2 (Jan to Jun) | |
LUBS5991M | Creating Effective Data Visualisations | 15 | Semester 2 (Jan to Jun) |
Last updated: 07/08/2024 15:42:49
Errors, omissions, failed links etc should be notified to the Catalogue Team