Module manager: Professor Sourindra Banerjee
Email: s.banerjee1@leeds.ac.uk
Taught: 1 Jul to 31 Aug View Timetable
Year running 2025/26
GPA of 2.5 (US) or equivalent and enrolled at a university
This module is not approved as a discovery module
AI Powered Marketing Analytics examines the application of artificial intelligence to the analysis and interpretation of marketing data, enabling students to derive meaningful insights without reliance on specialised or costly analytical software. The module is designed to support evidence‑based decision‑making and strategic marketing analysis through the use of AI‑enabled tools. The module is suitable for students seeking to develop analytical competence in the practical use of University approved Generative Artificial Intelligence (GenAI) tools (for example Microsoft Copilot) for data cleaning, analysis, and visualisation. Emphasis is placed on accessibility and real‑world applicability, ensuring that learners can engage with marketing analytics in a structured and practical manner. Through guided instruction and applied examples, students develop the ability to utilise an AI‑powered marketing assistant to support statistical tasks across a range of marketing contexts.
Objectives of the module are:
1. Introduce AI-Enhanced Data Analysis Tools
To familiarize learners with using University approved GenAI tools for reading, manipulating, and visualising marketing data, enabling efficient exploration and presentation of insights.
2. Develop Competence in Marketing Analytics Techniques
Factor analysis, cluster analysis, and regression models—to solve real-world marketing problems.
3. Support Marketing Decision Making To understand consumer choices and optimise the marketing mix (with a blend of Product, Place, Price and Promotion). Supporting data-driven decisions in sales, product development and segmentation, and campaign management.
On successful completion of the module students will be able to:
1. Introduction to Data Handling and Visualisation
Learners will be able to read, clean, manipulate, and visualise marketing datasets using University approved GenAI tools, enabling effective data-driven storytelling and decision-making.
2. Advanced Statistical Techniques for Marketing Insights
Learners will apply descriptive and bivariate statistical techniques using University approved GenAI tools to uncover patterns, relationships, and trends in consumer and market data. Learners will utilise factor and cluster analysis via University approved GenAI tools for market segmentation strategies.
3. Marketing Decisions
Learners will build and interpret choice models using logistic regression and marketing mix models using linear regression in University approved GenAI tools for marketing decisions
On successful completion of the module students will be able to:
1. Academic skills: Gain proficiency in applying descriptive statistics, data visualisation and regression models to interpret marketing data.
2. Digital skills: Enhance digital literacy by using AI tools (like Co-Pilot) which enable students to work on data rich environments.
3. Work Ready: Make data-driven decisions in dynamic marketing environments.
4. Technical: Use prompt engineering and output evaluation.
Indicative content:
1. Reading and manipulating data
2. Tabulation and descriptive statistics
3. Data Transformation and Distributions
4. Understanding new product development by using factor analysis
5. Understanding segmentation by using cluster analysis
6. Choice models using logistic regression: Innovation of New Products
7. Marketing mix models using linear regression
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Fieldwork | 1 | 8 | 8 |
| Seminar | 8 | 3 | 24 |
| Private study hours | 68 | ||
| Total Contact hours | 32 | ||
| Total hours (100hr per 10 credits) | 100 | ||
Practice datasets will be provided in the class. Students will analyse the datasets and some students will be asked to discuss the analysis in the class. Module leader will provide feedback on the class discussions.
Module leader will provide guidance for assignment data analysis via emails outside of the class.
Module leader will also provide sample reports of marketing companies which will provide students ideas on how they can frame their report.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Report | 2,000 words | 100 |
| Total percentage (Assessment Coursework) | 100 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
Check the module area in Minerva for your reading list
Last updated: 18/05/2026
Errors, omissions, failed links etc should be notified to the Catalogue Team