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2024/25 Taught Postgraduate Module Catalogue

LUBS5403M Marketing Analytics

15 creditsClass Size: 310

Module manager: Ashutosh Singh

Taught: Semester 2 (Jan to Jun) View Timetable

Year running 2024/25

This module is not approved as an Elective

Module summary

The module focuses on applying various statistical models to facilitate marketing activities and strategies, involving the development of metrics to monitor marketing performance and active learning in 2-hour workshops.


This module aims to introduce critical marketing analytics methods to provide students with practical experience in applying essential techniques in managing, analysing, and presenting market datasets to improve the efficiency and effectiveness of strategic marketing.

Learning outcomes
1. Critically explain how analytical techniques and statistical models can enhance marketing management.
2. Apply marketing analytic techniques to analyse and evaluate marketing concepts and processes
3. Critically evaluate different statistical methods for analysing marketing-related datasets.
4. Accurately use a range of key methods of marketing analytics to solve marketing decision problems.

Skills outcomes
The market is buzzing with marketing analytics and big data modules. This module attempts to capture this trend and keep our modules relevant for specialised master programmes. The analytical and data analysis skills apply to both MSc and MA students. R programming software is used in each workshop, and students will be familiar with R programming for statistical analysis after attending all workshops.


Introduction to Marketing Analytics
Understanding Consumers: Cluster analysis for segmentation; factor analysis for Perceptual mapping; calculating customer lifetime value
Developing new products: Conjoint analysis
Measuring return on marketing investment: Market response models
Big data analytics in Marketing: Decision trees, Machine learning models
Digital Data analytics: Search and social media analytics, A/B Testing, Multivariate testing, Social listening, Sentiment and text analysis
The future of marketing analytics

Teaching methods

Delivery typeNumberLength hoursStudent hours
Private study hours120.00
Total Contact hours30.00
Total hours (100hr per 10 credits)150.00

Private study

Students will be provided with a detailed reading list as well as a recommended textbook. These readings will correspond to the lectures and will aid their understanding of the lectures. Workshops (Seminars) will be used to ensure that students comprehend the learnt technique from lectures. Exercises will be provided during the workshops. Given the limited amount of time in the lectures and workshops, students will have to conduct private learning on different modules and programming languages.

Opportunities for Formative Feedback

During the lectures, progress will be monitored by in-class exercises and unassessed pop quizzes. The practical will adopt a more flipped learning approach. Computer-based exercises will be used in the workshop corresponding to the statistical modelling part of the module. Students must work individually to apply the models learnt during the lecture to address the marketing problems. Immediate feedback will be provided. Besides, different exercises will be used to supplement different models to solve various marketing issues. Weekly office hours will be available so students can ask questions regarding the main teaching sessions to discuss progress and identify/respond to areas of difficulty. The discussion forum on Minerva will provide another opportunity to monitor students learning process.

Methods of assessment

Assessment typeNotes% of formal assessment
ReportIndividual 3,000 word report100.00
Total percentage (Assessment Coursework)100.00

Report information: A dataset will be given to students. They will work on the dataset individually using appropriate analytic tools to solve marketing problems. Resit will be the 3,000-word report for 100% of the module. The dataset used for the final assessment is different from the workshop datasets.

Reading list

The reading list is available from the Library website

Last updated: 29/04/2024 16:16:22


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