EBSC6017 Data Mining for Marketers Unit Handbook 2026 | UCA

EBSC6017 TERM 1 2025-26

Unit Handbook Introduction

Course Diagram

For course diagram please check your yearbook.

Unit Information

Content

Data Mining focuses on the use of automation to uncover relationships within datasets that can be used to support improved decision processes.  When employed in marketing, data mining will use customer, potential customer, supplier and population data to reveal patterns in the data that have potential to improve the marketing process and outcomes. New customers may be acquired, existing customers retained and others abandoned, and markets explored. Various tools will be used for data mining including descriptive statistics including SPSS and R.

Syllabus

• Introduction to R and SPSS
• Optimisation of marketing budget allocation
• Principle, procedure, and practice of text mining
• CHAID based customer behaviour mining
• Market based analysis

Unit Learning Objectives

Assessment 

How and when will I be Assessed

This Unit is assessed through the completion of the Formative and Summative assessments.

Formative Assessment

Please Note: Formative assessment does not count towards your final Unit grade.

Summative Assessment

Please Note: The pass mark is 40%

If you do not submit for a summative assessment, a mark of 0 will be awarded, with a result of ‘Fail’. The maximum mark available for any unit reassessed by Resit will normally by 40 if the unit is credit bearing, or ‘pass’ where it is not.

Assessment Requirements

 Table A1 – Assessment Components

Table A2 – Categories for Assessment

Please make changes base on your unit content information:

Re-Assessment Elements

Some students may be required to take reassessment for the Unit, following a decision from a Board of Examiners. Do check the Programme Handbook and seek advice from your Personal Academic Tutor if this is the case for you. Support from the Unit Team will be available in preparation for the Re-assessments.

Should you be required to take reassessment, the nature of the reassessment will be:

The resit task is the same as the original task. If you have failed the assessment at the first attempt, please read and address the feedback given to you and amend your original work based on the feedback. If you did not submit at the first attempt, please follow the brief of the original task.

Indicative Reading

To develop your skills in finding, accessing and analysing business information, data and knowledge you are encouraged to explore all sources of information to drive and enhance your learning (books, academic and professional journals, online resources, etc.).  The books from ‘myReadingList’ web link is an indicative list of reading that you may find helpful in your studies; more specific readings may be utilized throughout the Unit.

Link to myReadingList:

https://uca.rl.talis.com/lists/53038AB7-A636-51F0-4C27-D7C9DC35A880.html

Summative Assessment Task

Background

British high street retailer B&M is adapting to the changing retail landscape, which has included the recent acquisition of Wilko stores. As part of their strategy, B&M is considering a redesign of their in-store fixtures to enhance the customer experience. To navigate these developments, B&M has engaged your team of marketing consultants to provide strategic insights and recommendations to further strengthen their business. In addition to your independent research, B&M has provided a sales dataset from Wilko (accessible on the MYUCA site) to support your analysis.

Objective

Working in groups of up to 5 students per group, your task is to generate a comprehensive business report, with a minimum of 1400 words, that leverages the marketing knowledge you have acquired over the past two years and the specific skills gained from this unit. You may consider the following key tasks:

Business Environment Review (800 words +/-10%)

Provide an analysis of the business environment in which B&M operates. Focus on the dynamics of the high street retailing industry in the UK, emphasising both its historical rise and current decline.

Assess the major factors contributing to the challenges faced by B&M within this context.

Utilise relevant market research and industry data to support your analysis.

Data Mining and Analysis (400 words +/-10%)

Examine the dataset provided by B&M. Employ data mining techniques to extract valuable insights.

Identify and report key findings from the dataset.

Formulate clear and actionable recommendations based on your data analysis.

Note: Data mining and visualisation techniques are highly desirable in this section. Use appropriate graphs, charts, and visuals to enhance the clarity of your findings.

Attach your code to the report as an appendix.

In-Store Music Playlist Recommendation (200 words +/-10%)**

Suggest a cheerful and engaging in-store music playlist specifically tailored for B&M’s New Year sales event.

Justify your song selections based on Spotify data scrapping.

Coursework team sign-up sheet:

https://docs.google.com/spreadsheets/d/1bMeO5pkyGNuO2vNgxCw7J8M8ZO7nfGNc_Z6UUfBkh_Q/edit?usp=sharing

**NB As of 26 Nov 2025, Spotify blocked its API for data scraping. For this part of the task, you will need to use the sample dataset provided by the tutor for analysis.

TIMETABLE

UCA Grading Descriptor

According to the UCA Regulations, each assessment component will be assessed against the assessment criteria as published in the unit descriptor for that unit and awarded a mark between 0 and 100 in accordance with the University’s published grading descriptors. All marks are provisional until formally confirmed by the Board of Examiners.

Use of Artificial Intelligence in Submitted Work

The University supports the ethical and responsible use of AI technologies in the learning experience, including tools such as ChatGPT, Dall-E, Midjourney, Sora or Firefly, as well as Microsoft CoPilot, Grammarly and Quillbot. These technologies can enhance your learning, help to explore or visualise new ideas and concepts; support your research; help with translation, assessment planning or proof your written copy.

Any use of AI must be carefully considered within the following criteria:

Work produced by AI cannot be presented as your own.
This is stated in the University for the Creative Arts Academic Misconduct regulation, Section 1.12 (h): The students may gain an unfair advantage by using AI applications (e.g. ChatGPT) with the intent of presenting the work created by the app as your own’.

• Any AI produced work included in your submission must be referenced.
  You will need to use the Harvard Reference Scheme and also state the prompt used to generate the relevant work in the bibliography reference.
• Use of AI must be approached critically.
  Generative AI has been found to often produce factually inaccurate materials, such as citations to research papers or articles that do not exist. It is therefore essential that you fact-check all work produced by AI. In critically analysing AI produced materials there are a range of further questions that you will need to consider such as data or algorithmic bias, along with issues of intellectual property that includes uncredited use of open-source data or ‘web scrapes’.
• There should be an appropriate use of AI in your final submission.
  It is important to consider how you are using AI in your final submission. AI can be utilised appropriately to achieve specific tasks or goals, such as visualising a product for which you have developed the concepts yourself, or as a tool to support with writing styles, grammar and spelling. Including a brief summary that explains how you have used AI, along with a reflection on its usefulness and the challenges you encountered, can provide valuable insight into your approach.
• If you are unclear about using Generative AI or if you ahve any further questions, please contact your Unit Lead who will be happy to help.

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