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
EBSC6017 Data Mining for Marketers EBSC6017 TERM 1 2025-26 LEVEL Level 6 CREDIT VALUE 15 Credits STUDY HOURS 36 Scheduled Teaching Hours 120 Guided Independent Study Hours
PROGRAMME(S) BA / BSc (Hons) Advertising BA / BSc (Hons) Advertising with Professional Practice Year
BA / BSc (Hons) Arts and Cultural Management
BA / BSc (Hons) Arts and Cultural Management with Professional Practice Year
BA / BSc (Hons) Business and Management
BA / BSc (Hons) Business and Management with Professional Practice Year
BA / BSc (Hons) Digital Marketing and Social Media
BA / BSc (Hons) Digital Marketing and Social Media with Professional Practice Year
BA / BSc (Hons) Events and Festivals Management
BA / BSc (Hons) Events and Festivals Management with Professional Practice Year
BA / BSc (Hons) Event and Promotions Management
BA / BSc (Hons) Event and Promotions Management with Professional Practice Year
BA / BSc (Hons) Fashion Business and Management
BA / BSc (Hons) Fashion Business and Management with Professional Practice Year
BA / BSc (Hons) Music Business and Management
BA / BSc (Hons) Music Business and Management with Professional Practice Year
BA / BSc (Hons) Digital Media and Magazine Publishing
BA / BSc (Hons) Digital Media and Magazine Publishing with Professional Practice Year
BA (Hons) Fashion Branding and Communication
BA (Hons) Fashion Branding and Communication with Professional Practice Year
BA (Hons) Creative Industries Management in Fashion (Top-up)
BA (Hons) Business Innovation and Management (Top-up)
Unit Start Date w.c 29 Sep 2025 Unit End Date w.c 12 Jan 2026 Tutors Shawn Li Submission Date for Assessment
4pm 23 Jan 2026 External Accrediting Body None Unit Handbook Introduction Unit Number & Title
EBSC6017 Data mining for marketers Project Start Date w.c 29 Sep 2025 Project End Date 23 Jan 2026 Tutors Shawn Li Submission Date for Formative Assessment
w.c 18 Nov 2025 Submission Date for the Final Work for Summative Assessment
4pm 23 Jan 2026 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 LO1
Demonstrate a systematic understanding of key aspects of data mining for business decisions. Engaging with practice LO2 Deploy accurately established techniques and algorithms of analysis within SPSS, Excel, and R. Research & analysis LO3 Communicate data mining results to both specialist and non-specialist audiences. Realisation & communication Need Help with EBSC6017 Data Mining for Marketers Assignment?
Order Non Plagiarized Assignment 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.
Data analysis Individual data analytics task (this work will be carried out in class Analyse a series of business scenarios as a marketing analyst
This consists of a case study or structured quiz, you are expected to give the first attempt independently (40 minutes).
After your attempt, discuss your difficulties and solutions with your teammates.
Consolidate your findings as a group. Mark the work.
The result DOES NOT AFFECT your final mark
Datasets will be released in Week 4
Due Date: Week 4/5
No Submission Required
Format: Problem-solving and data analysis with real-life dataset (more details to announce in week 1)
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.
Written assignment 1,400 Words Business Report (see page 8 assessment briefing for details) (See ‘Unit Project’ section for details)
Coursework Due Date: 4pm 23 Jan 2026
Format: MS Word or PDF
Max. 5 Students Are Allowed in One Group
1,400 Words ± 10%
Assessment Requirements Table A1 – Assessment Components Assessment Component
List all separate components.
Weighting (%)
Typical Indicative Assessment tasks
Where the component comprises more than one assessment task
Assessment Type For each component double click in the box to see options. The options equate to the assessment types in table A2 Business report (summative) 100% Group business consultancy report Summative assessment Data analysis & problem solving (formative) 0% Analysis of 5 individual real-life datasets Formative assessment Table A2 – Categories For Assessment Please make changes base on your unit content information:
ASSESSMENT TYPE % OF ASSESSMENT CATEGORY Written exam Written Set exercise (under exam conditions but not testing practical skills) Written Written assignment, including essay Coursework Report Coursework Dissertation Coursework Portfolio 100% Coursework Project output (other than dissertation) Coursework Set exercise (not under exam conditions, e.g. critiques) Coursework Oral assessment and presentation Practical Practical skills assessment (including production of an artefact) Practical Set exercise testing practical skills Practical 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 WEEK Topic Main content to cover Week 1 (W/C 30 Sep)
Prelude The purpose of the head start week is for students to acquire an initial understanding of the nature and scope of the subject area of the theory and practice of data mining and an overview of the key concepts.
Agenda: Welcome and Introductions
Course Objectives and Syllabus Walkthrough
Key Concepts in Data Mining
Overview of tools used in data mining
Task:
Study the coursework briefing
Form study groups
Week 2 (W/C 7 Oct)
Mining preparation 1 This week will introduce the R studio and basic programming concepts and terminology. Basic numerical operators and R objects will be discussed.
Agenda: Introduction to RStudio
Basic Programming Concepts
Basic Numerical Operators in R
Working with R Objects
Interactive Q&A and Wrap-up
Task:
Subscribe to the YouTube Channels:
Data School – Offers a plethora of video tutorials on R and data science topics.
StatQuest with Josh Starmer – Excellent for understanding statistical concepts which can be implemented in R.
Week 3 (W/C 14 Oct)
Mining preparation 2 This week will develop your understanding of the packages in R, especially the data mining Swiss Knife – Tidyverse
Agenda: Introduction to Packages in R
Introduction to Tidyverse
Data Manipulation using dplyr
Week 4 (W/C 21 Oct)
Mining preparation 3 Continue to explore the power of Tidyverse; use the tool to conduct basic data frame summary and data analysis. Formative assessment.
Agenda: Review of Tidyverse Components
Data Frame Summary with Tidyverse
Practical Data Analysis Exercise
Week 5 (W/C 28 Oct)
Data visualisation 1 We will explore the ggplot package and different types of charts and data.
Agenda: Introduction to Data Visualisation
Basics of ggplot2
Types of Charts and Their Applications (pt1)
Week 6 (W/C 4 Nov)
Data visualisation 2 We will explore the ggplot package and different types of charts and data.
Agenda: Types of Charts and Their Applications (pt2)
Interactive Q&A and Wrap-up
Week 7 (W/C 18 Nov )
Formative assessment
Agenda: Data mining exercise
Result & analysis
Week 8 (W/C 25 Nov )
Market basket analysis 1 Introduce the algorithm for association / market basket analysis
Agenda: Introduction to Market Basket Analysis
Association Rule Fundamentals
Apriori Algorithm
Practical Application & Case Study
Week 9 (W/C 2 Dec )
Market basket analysis 2 Explore various forms for datasets for MBA. Formative assessment.
Agenda: Advanced topics in MBA
Practical Application & Case Study
Week 10 (W/C 9 Dec )
Excel data mining Use Excel to conduct data mining projects
Agenda: Why Excel for Data Mining
Excel Build-in Mining Tools
Case Study: Business Optimisation with Excel Data Mining
Interactive Q&A and Wrap-up
Week 11 (W/C 6 Jan )
Music data mining Use Spotify data to explore key insights from big music data
Agenda: Data scrapping – things to consider
Music data – structure
Music data – analysis
Week 12 (W/C 13 Jan)
Preparation for assessment based on course content in weeks 1-11. Agenda: Key Knowledge Review
Coursework Workshop
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.