WM9K4-15 Digital Analytics & Marketing Technology
Introductory description
Modern digital marketing practice is as much dependent on a suite of technologies and information systems, as it is on strategies and marketing technique. Commonly these include a mixture of digital and data technologies that underpin each stage of the marketing lifecycle, from initial research through to campaign analysis and customer retention. The ultilisation of such data and techniques has become an essential toolkit for implementing and optimising modern digital marketing strategy.
Module aims
The module aims to expose participants to the latest in marketing and big data technologies, and
apply them to a range of digital marketing scenarios. To do this module seeks to provide digital marketing students with an overview and first-hand experience of a range of these technologies including:
- market analytics tools,
- marketing automation,
- artificial intelligence,
- machine learning,
- digital analytics & web analytics,
- data visualisation,
- technical SEO
Outline syllabus
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
a) What is marketing technology?
- Marketing technology fundamentals
- Marketing technology stack
- Marketing automation
b) Big data analytics
- Big data fundamentals
- Artificial intelligence and machine learning
- Data visualisation
c) Marketing analytics
- Google analytics and digital analytics techniques
- Digital Ads data
- Customer segmentation
d) Marketing automation
- AI in digital marketing
- Chatbots and personalisation
- Automation technologies
f) A practical simulation of the above topics
Learning outcomes
By the end of the module, students should be able to:
- Demonstrate a comprehensive understanding of the uses of digital analytics and marketing technologies in modern business
- Critically analyse the systematic and operational risk associated with a business’ information architecture, and develop appropriate mitigation and management strategies
- Critically evaluate a range of real-world marketing technology solutions, and determine their applicability and suitability to a range of different use cases
- Interpret complex business requirements and develop appropriate, higher-level solutions and designs
- Collaboratively and critically analyse business issues and propose and evaluate practical solutions with digital analytics skill and marketing technology
Indicative reading list
View reading list on Talis Aspire
Interdisciplinary
A mixture of technology/computing topics and business topics
International
Topics are of high international demand
Subject specific skills
Digital analytics, clustering, visualisation, marketing analytics, marketing tech stack
Transferable skills
Presentation skills, data analysis, research, teamwork, critical thinking
Study time
Type | Required |
---|---|
Lectures | 20 sessions of 1 hour (13%) |
Seminars | 10 sessions of 1 hour (7%) |
Online learning (independent) | 60 sessions of 1 hour (40%) |
Assessment | 60 hours (40%) |
Total | 150 hours |
Private study description
No private study requirements defined for this module.
Costs
No further costs have been identified for this module.
You must pass all assessment components to pass the module.
Assessment group A2
Weighting | Study time | Eligible for self-certification | |
---|---|---|---|
Digital Marketing Data Analytics and use of Marketing Technology | 30% | 18 hours | No |
Analyse and interpret a given digital marketing scenario and data, and propose the use of marketing technology considering the given scenario. Peer Marking Process will be adopted in this assessment. |
|||
Assignment | 70% | 42 hours | Yes (extension) |
A business-style report discussing core topics in digital analytics and marketing technology |
Feedback on assessment
Verbal feedback will be provided for the group assessment. Written feedback will be provided for the individual assignment.
There is currently no information about the courses for which this module is core or optional.