2020/21 Taught Postgraduate Module Catalogue

LUBS5403M Marketing Analytics

15 creditsClass Size: 500

Module manager: Yeyi Liu
Email: busyli@leeds.ac.uk

Taught: 1 Jan to 31 May View Timetable

Year running 2020/21

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.

Objectives

This module aims to introduce students the key methods of marketing analytics, to provide students a practical experience of applying key methods in managing, analysing, and presenting market datasets for improvement of the efficiency and effectiveness of strategy 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 for marketing analytics and big data modules. This module is an attempt to capture this trend and keep our modules relevant for specialised master programmes. The analytical and data analysis skills are relevant to both the MSc and the MA students.

Syllabus

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 tress, 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 type	Number	Length hours	Student hours
Workshop	10	2.00	20.00
Lecture	10	1.00	10.00
Private study hours			120.00
Total Contact hours			30.00
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. Practical will used to ensure that students comprehend the learnt technique from lectures. Exercises will be provided during the practical. Given the limited amount of time in the lectures and practical, 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. In the practical corresponding to the statistical modelling part of the module, computer-based exercises will be used. Students will be required to work individually to apply the models learnt during the lecture to address the marketing problems. Immediate feedback will be provided.
Besides, difference exercises will be used to supplement how different models could be adopted to solve different marketing issues.
Weekly office hours will be available so that students could arrange to drop in outside the main teaching sessions to discuss progress and identify/respond to areas of difficulty.
Discussion forum on Minerva will provide another opportunity to monitor students learning the process.

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
Report	Individual 3,000 word report	100.00
Total percentage (Assessment Coursework)		100.00

Report information - A dataset will be given to students. They will be working on the dataset individually by using appropriate analytic tools to solve marketing problems. Resit will be 3,000 word report for 100% of the module. A different dataset will be provided.

Reading list

The reading list is available from the Library website

Last updated: 05/01/2021 14:54:08

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