Contents:
The work you submit for this assignment must be your own independent work, or in the case of a group assignment your own groups’ work. More information is available in the ‘Assignment Task’ section of this assignment brief.
Assignment Information
Module Name: Business Analytics and Intelligence
Module Code: 7045SSL
Assignment Title: Individual presentation
Assignment Due: 30/10/2025, 18:00 UK time
Assignment Credit: 5 credits
Word Count (or equivalent): 5 to 10 minutes
Assignment Type: Standard
Percentage Grade (Applied Core Assessment). You will be provided with an overall grade between 0% and 100%. To pass the assignment you must achieve a grade of 40% or above.
Assignment Task
Background Information
The Clothing Retailing in the UK is one of the most dynamic and competitive sectors within the broader retail market. It includes a diverse range of businesses, from high-street brands and fast fashion retailers to luxury labels and e-commerce platforms. The industry contributes significantly to the UK economy in terms of employment, innovation, and consumer spending. The UK clothing retail market has faced challenges, including a decline at a -2.3% market size in 2024.
IBISWorld. (2025). Clothing retailing in the UK (Report No. G47.710). IBISWorld. Retrieved May 29, 2025, from https://my.ibisworld.com/uk/en/industry/G47.710/about
Assessment Requirement
Video Presentation (5 to 10 minutes)
As a Business Analyst, it is expected that you examine the Clothing Retailing in the UK and make recommendations about the future of the sector.
This assignment requires you to produce a 5-to-10-minute video presentation on business analytics within UK’s Clothing Retailing. Not only will this assessment test your presentation skills, but it will also assess how well you are able to gather data, analyse and interpret the output with supporting relevant literature.
As a business analyst, you are required to prepare a 10-minute video presentation that provides insights into the following tasks.
Note: Your slide submission must include an accessible link to a video recording that can be viewed by all members of the module teaching team.
1. Data Compilation
Using sources such as FAME or IBISWorld, compile a dataset to support the analyses required in the following tasks. Your dataset must meet the following criteria:
a. Include at least five companies from the specified industry.
b. Contain yearly data from 2015 to 2024.
c. Include 5–10 key business metrics.
d. Among the selected metrics, ensure at least three are “managed metrics”, i.e., those that managers can directly influence (e.g., liquidity ratio, number of employees). Metrics such as profit or ROCE, which are outcomes rather than directly managed, should be excluded from this subset.
e. Include a slide in your presentation that provides a link to the shared Excel workbook containing the compiled dataset. Ensure the workbook includes at least one sheet that consolidates data from all companies.
2. Data Visualisation for Descriptive/Exploratory Analysis
a. Present at least four relevant charts:
- At least two should enable inter-company comparison.
- At least two should show trends over time.
b. Explain your analysis and findings, highlighting key insights from the visualisations, especially in relation to the business issues discussed.
c. Provide a critical evaluation of the types of charts and visualisations best suited for effective business dashboards. Use real-world examples of impactful visual-based decision-making to support your evaluation.
3. Summary Statistics for Descriptive Analysis
a. Present key summary statistics with careful formatting (e.g., currency, decimals, and commas). Always include the count (N) for each statistic.
b. Critically evaluate the added value of the median and standard deviation compared to the mean, using your calculated statistics to illustrate your discussion.
4. Predictive Analytics
a. Explore key relationships between selected business metrics through correlation analysis.
b. Build a regression model to forecast one of the output metrics (e.g., Profit), using the managed metrics identified in Section 1.d as independent variables. Clearly identify your dependent variable.
c. Provide a critical evaluation of your predictive model, focusing on its reliability and the extent to which it can inform managerial decision-making.
5. Recommendations and Conclusions
Based on both your descriptive and predictive analyses, and supported by relevant business literature:
Identify two key decisions that managers in this industry should focus on.
Provide critical recommendations for each, directly informed by your analysis and aligned with the business issues discussed.
Use of AI Tools
This assignment has been classed as Amber. AI use is allowed to assist in the development of an assessment in line with the student guidance.
Use of AI should be evidenced in the references at the end of your assignment.
Where permitted, any assistance/content generated by AI is not your own work and must be acknowledged within your work (see submission instructions below). Failure to do so is academic misconduct.
Submission Instructions:
The assessment must be submitted electronically on Turnitin submission links by 18:00 UK time on 30/10/2025. No paper copies are required and submission by emails will not be accepted in any situation.
You can access the submission link through the Assessment tab on your 7045SSL Aula page. Your coursework will be given a zero mark if you do not submit a copy through Turnitin. Please take care to ensure that you have fully submitted your work.
Please ensure that you have submitted your work using the correct file format, unreadable files will receive a mark of zero. For written assignments this should usually be Microsoft Word and not PDF, unless otherwise advised by the module leader.
All work submitted after the submission deadline without a valid and approved reason (see below) will be given a mark of zero.
The University wants you to do your best. However, we know that sometimes events happen which mean that you can’t submit your coursework by the deadline – these events should be beyond your control and not easy to predict. If this happens, you can apply for an extension to your deadline for up to two weeks, or if you need longer, you can apply for a deferral, which takes you to the next assessment period (for example, to the resit period following the main Assessment Boards). You must apply before the deadline. You will find information about the process and what is or is not considered to be an event beyond your control at https://share.coventry.ac.uk/students/Registry/Pages/Deferrals-and-Extension.aspx
Students MUST keep a copy and/or an electronic file of their assignment.
Checks will be made on your work using anti-plagiarism software and approved plagiarism checking websites.
You MUST ensure two things:
- The slides (PPT file) should include an accessible link to the recorded 10-minute-long video on its first page. Please ask others to test if they can access it before submission and if the video is working and playing correctly. Do not submit the video file itself to Turnitin.
- Go via the Aula page for this module to Turnitin and submit the slides file only (the video should only be a working link inside it).
Marking and Feedback
How will my assignment be marked?
Your assignment will be marked by the module team.
How will I receive my grades and feedback?
Provisional marks will be released once internally moderated.
Feedback will be provided by the module team alongside grades release.
Your provisional marks and feedback should be available within 2 weeks (10 working days).
What will I be marked against?
Details of the marking criteria for this task can be found at the bottom of this assignment brief.
Assessed Module Learning Outcomes
The Learning Outcomes for this module align to the marking criteria which can be found at the end of this brief. Ensure you understand the marking criteria to ensure successful achievement of the assessment task. The following module learning outcomes are assessed in this task:
1. Define and evaluate key concepts of business analytics.
2. Critically apply business analytics skills for decision making.
4. Solve managerial problems and make systematic decisions by applying business data analysis techniques.
Assignment Support and Academic Integrity
If you have any questions about this assignment please see the Student Guidance on Coursework for more information.
Spelling, Punctuation, and Grammar:
You are expected to use effective, accurate, and appropriate language within this assessment task.
Academic Integrity:
The work you submit must be your own, or in the case of groupwork, that of your group. All sources of information need to be acknowledged and attributed; therefore, you must provide references for all sources of information and acknowledge any tools used in the production of your work. We use detection software and make routine checks for evidence of academic misconduct.
It is your responsibility to keep a record of how your thinking has developed as you progress through to submission. Appropriate evidence could include: version controlled documents, developmental sketchbooks, or journals. This evidence can be called upon if we suspect academic misconduct.
If using Artificial Intelligence (AI) tools in the development of your assignment, you must reference which tools you have used and for what purposes you have used them. This information must be acknowledged in your final submission.
Definitions of academic misconduct, including plagiarism, self-plagiarism, and collusion can be found on the Student Portal. All cases of suspected academic misconduct are referred for investigation, the outcomes of which can have profound consequences to your studies. For more information on academic integrity please visit the Academic and Research Integrity section of the Student Portal.
Support for Students with Disabilities or Additional Needs:
If you have a disability, long-term health condition, specific learning difference, mental health diagnosis or symptoms and have discussed your support needs with health and wellbeing you may be able to access support that will help with your studies.
If you feel you may benefit from additional support, but have not disclosed a disability to the University, or have disclosed but are yet to discuss your support needs it is important to let us know so we can provide the right support for your circumstances. Visit the Student Portal to find out more.
Unable to Submit on Time?
The University wants you to do your best. However, we know that sometimes events happen which mean that you cannot submit your assessment by the deadline or sit a scheduled exam. If you think this might be the case, guidance on understanding what counts as an extenuating circumstance, and how to apply is available on the Student Portal.
Administration of Assessment
Module Leader Name:
Module Leader Email:
Assignment Category: Artefact
Attempt Type: Standard
Component Code: Prs
Assessment Marking Criteria
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Theory, concepts and models
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Analysis, evaluation and application
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Critique, conclusions and recommendations
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Exceptional First
80 to 100%
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- Exceptional understanding of data and context, demonstrating insight and depth
- Exceptional selection and justification of variables, all clearly relevant and purposeful
- Exceptional documentation of data sourcing, with transparency and reliability
- Exceptional accuracy and relevance in charts, effectively communicating key findings.
- Exceptional use of summary statistics, detailed, well-formatted, and clearly explained.
- Exceptional understanding of chart relevance, with precise and purposeful choices
- Exceptional dashboard design, highly effective, user-friendly, and visually coherent
- Exceptional evaluation of median and standard deviation versus mean, well-supported and thorough.
- Exceptional clarity and insight in identifying key relationships, reflecting advanced analytical thinking.
- Exceptional development of business cases for derived variables, compelling and well-aligned with context.
- Exceptional analysis of predictive reliability, thoroughly supported and insightful.
- Exceptional clarity, structure, and logical flow throughout the presentation.
- Exceptional use of visualizations, highly relevant, integrated, and impactful.
- Exceptional discussion, critically engaged, relevant, and deeply insightful.
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First
70 to 79%
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- Excellent understanding of data and context, with only minor gaps.
- Excellent selection and justification of variables, with most clearly relevant.
- Excellent data sourcing, mostly thorough and appropriately documented.
- Excellent accuracy and relevance in charts, effectively illustrating key insights.
- Excellent use of summary statistics, clear, well-formatted, and informative
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- Excellent understanding of chart relevance, with mostly appropriate and purposeful choices.
- Excellent dashboard design, mostly effective and well-structured.
- Excellent evaluation of median and standard deviation versus mean, clearly supported and well explained.
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- Excellent clarity and insight in identifying key relationships, showing strong analytical skills.
- Excellent articulation of business cases for derived variables, clearly aligned with context.
- Excellent analysis of predictive reliability, thorough and well-supported.
- Excellent clarity, structure, and logical progression in the presentation.
- Excellent use of visualizations, clearly relevant and well-integrated.
- Excellent discussion, relevant, insightful, and well-connected to findings.
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Upper Second
60 to 69%
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- Very good understanding of data and context, though with some noticeable gaps.
- Very good selection of variables, generally relevant and reasonably justified.
- Very good data sourcing, adequate but may lack depth or consistency.
- Very good accuracy and relevance in charts, though some may be simplistic or lack precision.
- Very good use of summary statistics, though further detail or formatting could enhance clarity.
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- Very good understanding of chart relevance, with generally appropriate visual choices.
- Very good dashboard design, mostly effective and functional.
- Very good evaluation of median and standard deviation versus mean, supported but may lack depth.
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- Very good clarity and insight in identifying key relationships, though not consistently sustained.
- Very good articulation of business cases for derived variables, though rationale may need strengthening.
- Very good analysis of predictive reliability, supported with appropriate evidence.
- Very good structure and clarity in presentation, generally logical and well-organized.
- Very good use of visualizations, mostly relevant and clearly presented.
- Very good discussion, relevant and informative, though may lack critical depth.
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Lower Second
50 to 59%
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- Good but basic understanding of data and context, with noticeable gaps in interpretation.
- Good selection of variables, though some are irrelevant or weakly justified.
- Good attempt at data sourcing, though limited in scope or clarity.
- Good use of charts, somewhat accurate and relevant, but lacking precision or impact.
- Good use of summary statistics, though basic and lacking detail or formatting.
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- Good understanding of chart relevance, though application may be inconsistent.
- Good but somewhat effective dashboard design, with limitations in clarity or usability.
- Good inclusion of evaluation of median and standard deviation vs. mean but lacking depth.
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- Good clarity in identifying key relationships, though insights are limited or general.
- Good presentation of business cases for derived variables, though basic and underdeveloped.
- Good analysis of predictive reliability somewhat supported but lacking deeper evaluation.
- Good structure and clarity in presentation, though occasionally disjointed or unclear.
- Good use of visualizations, somewhat relevant but lacking strong integration.
- Good discussion, somewhat relevant but limited in insight or critical reflection.
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Third
40 to 49%
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Outcomes Met but:
- Satisfactory but minimal understanding of data and its context, with key conceptual gaps.
- Satisfactory selection of variables, though many are irrelevant or poorly justified.
- Satisfactory data sourcing, though limited, unclear, or lacking justification.
- Satisfactory use of charts, though accuracy and relevance are minimal.
- Satisfactory application of summary statistics, though basic and inconsistently presented.
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Outcomes Met but:
- Satisfactory understanding of chart relevance, though application is weak or inconsistent.
- Satisfactory but minimally effective dashboard design, with issues in layout or usability.
- Satisfactory but unclear evaluation of median and standard deviation vs. mean, with incomplete analysis.
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Outcomes Met but:
- Satisfactory clarity in identifying key relationships, though insights are minimal.
- Satisfactory presentation of business cases for derived variables, but with weak logic or relevance.
- Satisfactory analysis of predictive reliability, though largely unsupported or superficial.
- Satisfactory presentation structure, though clarity and coherence are limited.
- Satisfactory use of visualizations, though minimally relevant or poorly integrated.
- Satisfactory discussion, though relevance and depth are lacking.
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Fail
30 to 39%
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Outcomes Not Met:
- Limited knowledge.
- Mostly irrelevant variables.
- Poor data sourcing.
- Inaccurate and irrelevant charts.
- Limited summary statistics.
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Outcomes Not Met:
- Limited understanding of chart relevance.
- Ineffective dashboard design.
Mostly missing evaluation of median and standard deviation vs. mean.
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Outcomes Not Met:
- Limited identification of key relationships.
- Limited business cases for derived variables.
- Limited and mostly unsupported analysis of predictive reliability.
- Lacks clarity and structure.
- Unclear and irrelevant visualizations.
- Mostly irrelevant discussion.
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Fail
0 to 29%
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Outcomes Not Met:
- Minimal knowledge.
- Irrelevant variables.
- Almost non-existent data sourcing.
- Highly inaccurate and irrelevant charts.
- Minimal or no summary statistics.
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Outcomes Not Met:
- Minimal understanding of chart relevance.
- Highly ineffective dashboard design.
- Missing evaluation of median and standard deviation vs. mean.
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Outcomes Not Met:
- Minimal identification of key relationships.
- Minimal or no business cases for derived variables.
- Minimal or unsupported analysis of predictive reliability.
- Unclear and unstructured presentation.
- Irrelevant visualizations
- Irrelevant discussion.
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