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MODULE DESCRIPTOR
Module Title
Big Data And Marketing Analytics
Reference CBM208 Version 1
Created April 2018 SCQF Level SCQF 11
Approved July 2018 SCQF Points 15
Amended ECTS Points 7.5

Aims of Module
This module examines the use and application of big data and analytics in a marketing context. It reviews key concepts, platforms and techniques that will enable you to manage and analyse big data to inform marketing decisions.

Learning Outcomes for Module
On completion of this module, students are expected to be able to:
1 Critically evaluate big data and analytics platforms and principles and their application in a marketing environment
2 Define and analyse commonly used metrics and KPIs in digital analytics and use these insights to improve marketing performance
3 Demonstrate a critical understanding of emerging concepts in marketing and assess their impact within an organisational context

Indicative Module Content
Marketing analytics frameworks and tools; marketing data types and value; machine learning and marketing; predictive analytics; algorithmic marketing; marketing automation; programmatic advertising; message and content optimisation; real-time bidding; online campaign optimisation; customer profiling, segmentation and personalisation; eCRM; salesforce analytics; social network analysis; sentiment analysis; building up the analytics function; managing marketing analytics.

Module Delivery
The module is delivered in taught mode by lectures, interactive group discussions, case studies and self-directed study. The module is delivered in distance learning mode by self-directed study from web-based learning materials and online support.

Indicative Student Workload Full Time Part Time
Contact Hours 36 36
Non-Contact Hours 114 114
Placement/Work-Based Learning Experience [Notional] Hours N/A N/A
TOTAL 150 150
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: Students will critically evaluate a big data problem in a specific marketing context, including development of a marketing analytics solution to an industry-relevant scenario.

MODULE PERFORMANCE DESCRIPTOR
Explanatory Text
The module is assessed by one component: C1 - Coursework - 100% weighting. Module Pass Mark = Grade D (40%)
Module Grade Minimum Requirements to achieve Module Grade:
A 70% or above
B 60% - 69%
C 50% - 59%
D 40% - 49%
E 35% - 39%
F 0% - 34%
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 CHAFFEY, D. & F. ELLIS-CHADWICK (2016). Digital Marketing: Strategy, Implementation and Practice. Upper Saddle River: Pearson
2 CHAFFEY, D. & PR SMITH (2017). Digital Marketing Excellence: Planning, Optimizing and Integrating Online Marketing. Florence: Taylor and Francis
3 FINLAY, S. (2014). Predictive Analytics, Data Mining and Big Data: Myths, Misconceptions and Methods. New York: Palgrave
4 SIEGEL;, E. (2016). Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die. New Jersey: Wiley
5 SPONDER, M. and KHAN G.F.(2017). Digital Analytics for Marketing. New York: Routledge
6 VERHOEF, P.; E. KOOGE and N. Walk (2016). Creating Value with Big Data Analytics. New York: Routledge
7 WINSTON, W. L. (2014). Marketing Analytics: Data-Driven Techniques with Microsoft Excel. Indianapolis: Wiley
8 ZAFARANI, R., ABBASI, M.A., LIU, H. (2014). Social Media Mining: An Introduction. New York: Cambridge University Press

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