7B001E - AI, Analytics and Smart Campaigns 01 Sep 2026 - 31 Jul 2032 | Version 0

Associated Module Information

Module Code: 7B001E
Module Title: AI, Analytics and Smart Campaigns
Faculty: Faculty of Business and Creative Industries
Faculty Group: Global Governance
Faculty Sub Group: Global Governance
Module Leader: Michael Parsons
Module Team: Adeyemi Aromolaran
First Intended Intake: SEP 2026 Final Year of Intake:
Date Closed:
Credit Value: 30 Credit Level: 7
Language: English
Percentage of Module Taught in Welsh: 0
Equivalent Module:
HECOS codes: 100075 - marketing
HECOS Code Weighting: 100

Document Version Information

Version 0
Valid From 01 Sep 2026
Valid To 31 Jul 2032

Module Aims

  1. To critically evaluate the strategic role of artificial intelligence and data analytics in shaping consumer behaviour and marketing decision-making, with emphasis on ethical implications and global digital contexts. 

 

  1. To develop students’ ability to design and justify data-informed, ethically sound marketing strategies that align with organisational goals and demonstrate professional integrity. 

 

  1. To equip students with the analytical mindset, technical proficiency, and reflective capabilities required to lead innovative, evidence-based marketing campaigns in digitally transformed 

Content Summary

This module explores how artificial intelligence (AI) and data analytics are transforming strategic marketing decision-making. Students will gain hands-on experience with data analysis tools and techniques, learning how to extract actionable insights from consumer data, digital platforms, and marketing campaigns. The module covers key topics such as predictive analytics, customer segmentation, marketing automation, and ethical data use, with a strong emphasis on real-world applications and strategic impact. By integrating theory with practice, students will develop the analytical mindset and technical skills needed to design data-driven marketing strategies that enhance customer engagement and business performance. This module supports the overall course aim of preparing future marketing leaders to harness digital innovation and evidence-based decision-making in a rapidly evolving landscape. 

Learning and Teaching Methods

Activity Type Hours
Lectures 9
Seminar 12
Practical Classes and Workshops 15
Groupwork 20
Guided Study 60
Problem/Challenge based learning 120
Formative Assessment 4
Summative Assessment 60
Total Hours Selected 300

Learning Outcomes

# Learning Outcome
LO1 Critically evaluate the role of artificial intelligence in shaping buyer behaviour, with a focus on AI-driven personalisation, predictive analytics, and the ethical implications of data usage in marketing.
LO2 Design and justify ethically sound, data-informed marketing strategies that demonstrate professional integrity and alignment with organisational goals in a global and inclusive context.

Module Requisites

N/A

Assessment Criteria

Assessment Category Assessment Type Description Duration Word Count Weight (%) Best of? Pass Mark
Asynchronous Assessment Portfolio Element 1: Group Consultancy Plan (30%) - 1,350 words per member (max) Element 2: Individual Strategic AI Audit & Proposal (50%) - 3,000 words (max) Element 3: Student Choice Individual Refection (20%) - 12 mins (max) 0 N/A 100 No 40

Assessment Matrix

Assessment Type Learning Outcomes
LO1 LO2
Portfolio ✔ ✔

Reading List

Week 1 Topic: Introduction to AI and Data-Driven Marketing 

Essential Reading 

Daffner, B. (2024) ‘Are You AI-Ready? A Roadmap to Mastering Marketing Technology in a Data-Driven World’, Journal of Digital & Social Media Marketing, 12(2), pp. 117–128.  

Mariani, M. Vega, R. and Wirtz, J. (2022) ‘AI in Marketing, Consumer Research and Psychology: A Systematic Literature Review and Research Agenda’, Psychology & Marketing, 39(4), pp. 755–776.  

Supplementary Reading 

Davenport, T. H. (2018) ‘Artificial intelligence for the real world’, Harvard Business Review.?January–February. 

De Bruyn, A., Viswanathan, V., Shan Beh, Y., Kai-Uwe Brock, J. & von Wangenheim, F. (2020) ‘Artificial intelligence and marketing: pitfalls and opportunities’, Journal of Interactive Marketing.?51:91-105. DOI: https://doi.org/10.1016/j.intmar.2020.04.007. 

Fountaine, T., McCarthy, B. & Saleh, T. (2019) ‘Building the AI-powered organization’, ?Harvard Business Review.?July–August. 

Marvi, R., Pantea F. and Cuomo, M. (2024) ‘Past, Present and Future of AI in Marketing and Knowledge Management’, Journal of Knowledge Management, 29(11), pp. 1–31.  

Rosário, T. and Raimundo, R. (2025) ‘The Integration of AI and IoT in Marketing: A Systematic Literature Review’, Electronics (Basel), 14(9), pp. 1854. 

 

Week 2 Topic: Data Sources, Quality, and Governance 

Essential Reading 

Rajan, P. (2024) ‘Integrating IOT analytics into marketing decision making: A smart data-driven approach’, International Journal of Data Informatics and Intelligent Computing,?3(1), pp.12-22. 

Schmuck, M. (2022) ‘Data Governance Issues in Digital Marketing: A Marketer’s Perspective’, Expert Journal of Marketing, 10(2), pp. 124-142. 

Wang, J., Liu, Y., Li, P., Lin, Z., Sindakis, S. and Aggarwal, S. (2024) ‘Overview of data quality: Examining the dimensions, antecedents, and impacts of data quality’, Journal of the Knowledge Economy,?15(1), pp.1159-1178. 

Supplementary Reading 

Alhitmi, K. (2024) ‘Data Security and Privacy Concerns of AI-Driven Marketing in the Context of Economics and Business Field: An Exploration into Possible Solutions’, Cogent Business & Management, 11(1), pp. 1-9. 

Lim, H. (2020).?7 types of data bias in machine learning.?Available: https://lionbridge.ai/articles/7-types-of-data-bias-in-machine-learning/ [2021, June 6]. 

O’Hara, C. (2021) Customer Data Platforms: Use People Data to Transform the Future of Marketing Engagement. Hoboken, N.J: Wiley. 

Roselli, D., Matthews, J. & Talagala, N. (2019) Managing bias in AI: what should businesses do??Forbes.?29 May. Available: https://www.forbes.com/sites/cognitiveworld/2019/05/29/managing-bias-in-ai-what-should-businesses-do/?sh=3cce26471440 [2021, 30 March]. 

Wei, J. (2020).?Bias in Natural Language Processing (NLP): a dangerous but fixable problem.?Medium. 2 September. Available: https://towardsdatascience.com/bias-in-natural-language-processing-nlp-a-dangerous-but-fixable-problem-7d01a12cf0f7 [2021, March 30]. 

 

Week 3 Topic: Analytical Tools and Techniques 

Essential Reading 

Singh K., Rahul, P., and Asha, T. (2025)?Marketing Analytics Using Excel: A Beginner’s Guide. First edition. London: SAGE Publications, Ltd. UK. 

Grigsby, M. (2022). Marketing Analytics: A Practical Guide to Improving Consumer Insights Using Data Techniques. 3rd ed. London: Kogan Page, Limited. 

Supplementary Reading 

Adeniran, I.A., Efunniyi, C.P., Osundare, O.S. and Abhulimen, A.O. (2024) ‘Transforming marketing strategies with data analytics: A study on customer behaviour and personalization’, International Journal of Management & Entrepreneurship Research, 6(8), pp.41-51. 

Ijomah, T.I., Idemudia, C., Eyo-Udo, N.L. and Anjorin, K.F. (2024) ‘Harnessing marketing analytics for enhanced decision-making and performance in SMEs’, World Journal Of Advanced Science And Technology,?6(1), pp.1-12.  

 

Week 4 Topic: Predictive Analytics and Customer Segmentation 

Essential Reading 

D’Arco, M.  (2019) ‘Embracing AI and Big Data in Customer Journey Mapping: From Literature Review to a Theoretical Framework’, Innovative Marketing, 15(4), pp.102–115. 

Kasem, M.S., Hamada, M. and Taj-Eddin, I. (2024) ‘Customer profiling, segmentation, and sales prediction using AI in direct marketing’, Neural Computing and Applications,?36(9), pp.4995-5005. 

Pande, P. (2025) ‘Big Data Analytics in E-Commerce Driving Business Decisions Through Customer Behavior Insights.” ITM web of conferences, 76, pp. 1-11.  

Supplementary Reading 

Adeniran, I.A., Efunniyi, C.P., Osundare, O.S. and Abhulimen, A.O. (2024) ‘Transforming marketing strategies with data analytics: A study on customer behaviour and personalization’, International Journal of Management & Entrepreneurship Research,?6(8), pp.41-51. 

Kumar, V., Rajan, B., Venkatesan, R. & Lecinski, J. (2019) ‘Understanding the role of artificial intelligence in personalized engagement marketing’,?California Management Review.?61(4):135-155. DOI: https://journals.sagepub.com/doi/10.1177/0008125619859317. 

 

Week 5 Topic: AI Applications in Digital Marketing 

Essential Reading 

Hamdan, A. and Esra A. (2024) Artificial Intelligence and Transforming Digital Marketing. 1st ed. 2024. Cham: Springer Nature Switzerland. 

Venkatesan, R., and Lecinski, J. (2021) The AI Marketing Canvas: A Five-Stage Road Map to Implementing Artificial Intelligence in Marketing. Stanford, California: Stanford Business Books. 

Supplementary Reading 

Acatrinei, C. (2025) ‘Artificial Intelligence in Digital Marketing: Enhancing Consumer Engagement and Supporting Sustainable Behaviour Through Social and Mobile Networks’, Sustainability, 17(14), pp. 6638. 

Manolica, A. (2025) ‘Adoption of AI in Digital Marketing: Comparing Gen Z and Gen Y through the Technology Acceptance Model’, Brain. Broad research in artificial intelligence and neuroscience, 16(3), pp. 102. 

Overgoor, G., Chica, M., Rand, W. & Weishampel, A. (2019) ‘Letting the computers take over: using AI to solve marketing problems’, ?California Management Review.?61(4):156-185. DOI: https://journals.sagepub.com/doi/10.1177/0008125619859318. 

 

Week 6 Topic: Strategic Decision-Making with Data 

Essential Reading 

Hossain, Q., Yasmin, F., Biswas, T.R. and Asha, N.B. (2024) ‘Data-Driven Business Strategies: A Comparative Analysis of Data Science Techniques in Decision-Making’, Scholars Journal of Economics, Business and Management,?9, pp.257-263. 

Nnaji, U.O., Benjamin, L.B., Eyo-Udo, N.L. and Etukudoh, E.A. (2024) ‘A review of strategic decision-making in marketing through big data and analytics’, Magna Scientia Advanced Research and Reviews, 11(1), pp.084-091.  

Supplementary Reading 

Badmus, O., Rajput, S., Arogundade, J. and Williams, M. (2024) ‘AI-driven business analytics and decision making’, World Journal of Advanced Research and Reviews,?24(1), pp.616-633. 

Nugroho, D. and Angela, P. (2024) ‘The impact of social media analytics on some strategic decision making’, IAIC Transactions on Sustainable Digital Innovation (ITSDI), 5(2), pp.169-178. 

Fountaine, T., McCarthy, B. & Saleh, T. (2019) ‘Building the AI-powered organization’, ?Harvard Business Review.?July–August.