Module overview
This module introduces some key concepts about the use of some basic statistical and analytical techniques within the marketing context. Students will learn through a combination of lectures, group work, practical (computer-lab) sessions (where needed), and self-study. After studying this module, students will be able to apply these techniques to analyse data in practice.
Aims and Objectives
Learning Outcomes
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- how to use analytic techniques to evaluate the quality of data for supporting marketing decisions;
- how to apply standard analytical tools to support marketing decisions.
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- implement analytic models to support marketing decision making.
- differentiate suitable approaches for a range of analytics tasks;
Subject Specific Practical Skills
Having successfully completed this module you will be able to:
- apply marketing concepts and evaluate them by using marketing analytics techniques.
- apply and critically evaluate marketing analytics techniques and use them to draw practical recommendations;
Transferable and Generic Skills
Having successfully completed this module you will be able to:
- define business and marketing problems and apply analytics to the problem.
- explain concepts clearly and critically apply findings;
Syllabus
- Data types and their sources in marketing context
- Data visualisation – Theory and Practice
- Marketing Dashboard
- Customer Clustering and Affinity Analysis
- Regression for Marketing Analytics (Basic Predictive Analytics)
- Data-driven Marketing Decision Making
- Overview of MarTech related to marketing analytics
Learning and Teaching
Teaching and learning methods
The basic principal of the teaching and learning strategy for this unit is to encourage you to actively engage in the subject matter through guided self-discovery of the material which will include: Reading; lecture slides; case studies; discussion and debate.
Type | Hours |
---|---|
Practical classes and workshops | 10 |
Wider reading or practice | 15 |
Follow-up work | 32 |
Revision | 30 |
Lecture | 24 |
Completion of assessment task | 28 |
Preparation for scheduled sessions | 11 |
Total study time | 150 |
Resources & Reading list
Textbooks
Grigsby, Mike (2018). Marketing Analytics: A Practical Guide to Improving Consumer Insights Using Data Techniques. Kogan Page Publishers.
Winston, W. L (2014). Marketing Analytics: Data-Driven Techniques with Microsoft Excel. Wiley.
Murray, Daniel G (2018). Tableau your data!. John Wiley & Sons.
Assessment
Formative
This is how we’ll give you feedback as you are learning. It is not a formal test or exam.
Class Exercise
- Assessment Type: Formative
- Feedback: Formative feedback will be provided to students during lectures, class exercises, computer laboratories, office hours, and via email when questions are asked of the module leader.
- Final Assessment: No
- Group Work: No
Summative
This is how we’ll formally assess what you have learned in this module.
Method | Percentage contribution |
---|---|
Essay | 100% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
Method | Percentage contribution |
---|---|
Essay | 100% |
Repeat
An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.
Method | Percentage contribution |
---|---|
Essay | 100% |
Repeat Information
Repeat type: External