Module Database Search



MODULE DESCRIPTOR
Module Title
Marketing Analytics
Reference CB3103 Version 1
Created August 2023 SCQF Level SCQF 9
Approved July 2018 SCQF Points 15
Amended May 2023 ECTS Points 7.5

Aims of Module
This module explores the use and application of analytics in a marketing context. It reviews key concepts, platforms and techniques that will enable you to understand, interpret and apply marketing data.

Learning Outcomes for Module
On completion of this module, students are expected to be able to:
1 Understand data and analytics platforms and principles and their application in a marketing environment
2 Identify, define, and analyse commonly used metrics and KPIs in digital and marketing analytics
3 Demonstrate knowledge of how data can be used in combination with fundamental marketing concepts in a practical context

Indicative Module Content
Marketing analytics frameworks and tools; marketing data types and value; machine learning, AI and marketing; predictive analytics; algorithmic marketing; marketing automation; programmatic advertising; message and content optimisation; online campaign optimisation; customer profiling, segmentation and personalisation; eCRM; social network analytics; marketing analytics in organisations.

Module Delivery
The module is delivered in taught mode by lectures, interactive group discussions, case studies and self-directed study

Indicative Student Workload Full Time Part Time
Contact Hours 36 N/A
Non-Contact Hours 114 N/A
Placement/Work-Based Learning Experience [Notional] Hours N/A N/A
TOTAL 150 N/A
Actual Placement hours for professional, statutory or regulatory body    

ASSESSMENT PLAN
If a major/minor model is used and box is ticked, % weightings below are indicative only.
Component 1
Type: Coursework Weighting: 100% Outcomes Assessed: 1, 2, 3
Description: Group Portfolio Assessment

MODULE PERFORMANCE DESCRIPTOR
Explanatory Text
The calculation of the overall grade for this module is based on 100% weighting of C1. An overall minimum grade D is required to pass the module.
Module Grade Minimum Requirements to achieve Module Grade:
A The student needs to achieve an A in C1.
B The student needs to achieve an B in C1.
C The student needs to achieve an C in C1.
D The student needs to achieve an D in C1.
E The student needs to achieve an E in C1.
F The student needs to achieve an F in C1.
NS Non-submission of work by published deadline or non-attendance for examination

Module Requirements
Prerequisites for Module None.
Corequisites for module None.
Precluded Modules None.

INDICATIVE BIBLIOGRAPHY
1 BILAL, A. et al. (2021). Social Big Data Analytics: Practices, Techniques, and Applications. Singapore: Springer
2 CAO, J. (2023). E-Commerce Big Data Mining and Analytics. Singapore: Springer
3 CHAFFEY, D. & F. ELLIS-CHADWICK (2022). Digital Marketing: Strategy, Implementation and Practice. Upper Saddle River: Pearson
4 CHAFFEY, D. & PR SMITH (2023). Digital Marketing Excellence: Planning, Optimizing and Integrating Online Marketing. Florence: Taylor and Francis
5 FINLAY, S. (2014). Predictive Analytics, Data Mining and Big Data: Myths, Misconceptions and Methods. New York: Palgrave
6 MU, H. (2019). Highly Effective Marketing Analytics: A Practical Guide to Improving Marketing ROI with Analytics. New York: Business Expert Press
7 SPONDER, M. and KHAN G.F.(2017). Digital Analytics for Marketing. New York: Routledge
8 VERHOEF, P.; E. KOOGE and N. Walk (2016). Creating Value with Big Data Analytics. New York: Routledge

Robert Gordon University, Garthdee House, Aberdeen, AB10 7QB, Scotland, UK: a Scottish charity, registration No. SC013781