Module Database Search
MODULE DESCRIPTOR | |||
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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 | |||
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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 | |
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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 |
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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 |
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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 |
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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 | |||||
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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 | |
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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 | |
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Prerequisites for Module | None. |
Corequisites for module | None. |
Precluded Modules | None. |
INDICATIVE BIBLIOGRAPHY | |
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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 |