Module manager: Ashutosh Singh
Email: A.Singh1@leeds.ac.uk
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2026/27
This module is not approved as a discovery module
This module introduces students to marketing analytics and data-driven decision-making in the digital age. It shows how marketers use data from online platforms and basic AI tools to understand customer behaviour, evaluate campaigns, and improve marketing performance. Students will learn to collect, interpret, and visualise data using accessible software, while practising simple methods such as segmentation, A/B testing, and performance analysis. No prior experience is required. The module bridges marketing theory and real-world application, building confidence with spreadsheets, analytics, and introductory AI tools. The ethical and responsible use of data and AI is a key theme, encompassing privacy, bias, transparency, and compliance. By the end of the course, students will be able to interpret data, generate actionable insights, and evaluate the opportunities and limitations of digital and AI tools in marketing. The module provides a strong foundation for further study and future careers in marketing, analytics, or digital business.
This module helps students learn how to identify marketing problems clearly and make smart, evidence-based decisions using data. It introduces the basic tools and techniques that marketers use in real business situations, building confidence with practical, beginner-friendly activities that connect questions to analytical answers.
Students also learn how to plan simple research projects and collect helpful information from both primary sources and secondary sources. Students will learn to apply fundamental analytical approaches to interpret data and draw insights. This equips them with the skills to support evidence-based decision-making in marketing contexts. This enables them to interpret results and apply them to marketing decisions.
On successful completion of the module students will be able to:
1. Identify and frame marketing problems that can be addressed through data and research, formulating clear analytics questions and hypotheses linked to marketing goals and KPIs.
2. Describe and apply the marketing analytics workflow, from problem framing to data collection, preparation, analysis, interpretation, and decision-making.
3. Collect and analyse marketing data from primary and secondary sources using entry-level digital and AI-assisted tools, applying basic statistical techniques where appropriate.
4. Interpret and communicate insights by presenting findings and actionable recommendations in a clear, structured, and decision-oriented manner.
On successful completion of the module students will be able to:
1. Demonstrate digital literacy by evaluating datasets, creating basic visualisations, and using spreadsheets or other digital tools to support marketing analysis.
2. Communicate and collaborate effectively by presenting data-driven insights clearly in written reports or presentations and working productively in small teams to meet deadlines.
3. Apply critical and ethical thinking by analysing marketing data responsibly, avoiding common reasoning errors, and recognising issues of privacy, bias, and data integrity.
4. Reflect and improve performance by responding to feedback, managing workload effectively, and developing stronger academic and decision-making skills.
Indicative topics:
- Understanding Marketing Analytics: Concepts, Scope, and Applications
- The Marketing Analytics Process
- Types of Marketing Data
- Introduction to Key Metrics
- Basics of Data Quality and Cleaning for Marketing Decisions
- Fundamentals of Data Visualisation and Dashboarding
- Using Analytics to Support Segmentation, Targeting, and Positioning
- Ethical and Privacy Considerations in Marketing Data Use.
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lecture | 10 | 1 | 10 |
| Seminar | 5 | 1 | 5 |
| Private study hours | 85 | ||
| Total Contact hours | 15 | ||
| Total hours (100hr per 10 credits) | 100 | ||
Throughout the module, students will undertake a series of structured learning activities conducted outside formal class hours. These are designed to promote self-directed learning, provide ongoing feedback, and help track individual academic progress. Each activity will be uploaded to Minerva with clear submission deadlines, and students are expected to complete and submit their work within the specified timeframe to receive formative feedback.
-Independent Problem-Solving Exercises (fortnightly, 10–15 minutes)
Every two weeks, short analytical exercises will be uploaded to Minerva following the lecture session. Students will complete and submit their responses by the stated deadline. Immediate automated feedback will help identify correct and incorrect responses, while the module leader will post collective feedback summarising key trends and areas for improvement across the cohort.
-Online Practical Challenges (fortnightly, 30–45 minutes)
To reinforce skills developed in workshops, students will engage in data analysis challenges conducted outside class using relevant software (Excel). These challenges will be made available online with clear instructions and submission dates. Students will upload their completed work to Minerva, where they will receive structured formative feedback focusing on technical accuracy, interpretation, and practical application. Optional follow-up discussions will address common issues identified in submissions.
-Reflective Learning Logs (updated fortnightly)
Students will maintain a personal reflective log, updated regularly outside class, to record progress, challenges, and applications of module concepts. Logs will be submitted electronically by fortnightly deadlines, allowing the module leader to provide developmental feedback. This will help students track growth and prepare for summative assessments.
-Individual Feedback Consultations (Optional, 15–30 minutes)
Students may book optional one-to-one consultations (online or in person) to discuss their independent submissions and receive personalised feedback. These sessions provide an opportunity to clarify understanding, address challenges identified through online activities and set targets for continued improvement.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Report | 2,000-word individual report | 100 |
| Total percentage (Assessment Coursework) | 100 | |
The individual report will take the form of a marketing analytics project proposal for a simulated company. This assessment provides students with an opportunity to apply theories and frameworks related to marketing analytics and demonstrate how they can be used to support data-driven decision-making in a business context. The report will include elements such as a brief market audit, definition of marketing problems or objectives, and analysis of provided primary and secondary datasets to identify relevant insights. Using these datasets, students will be expected to specify a clear marketing objective, select appropriate data sources, and apply suitable analytical methods such as descriptive statistics and basic regression analysis to generate practical, data-driven insights. They will also examine segmentation and targeting strategies using the data and discuss how insights from marketing analytics can inform channel selection and campaign planning. Emphasis will be placed on students' ability to critically evaluate and justify their choices based on data-driven evidence. The resit for this module will be 100% by a 2,000-word individual report.
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Last updated: 30/04/2026
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