Task One-Business analytics concepts [30 marks] Critically evaluate five peer-reviewed journal articles that demonstrate the diverse and significant business applications of big data clustering techniques. Your review should meet the following criteria:

Student Assignment Brief

This document is intended for Coventry University Group students for their own use in completing their assessed work for this module. It must not be passed to third parties or posted on any website. If you require this document in an alternative format, please contact your Module Leader.

Contents:

• Assignment Information
• Assignment Task
• Marking and Feedback
• Assessed Module Learning Outcomes
• Assignment Support and Academic Integrity
• Assessment Marking Criteria

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 Report

Assignment Due: 11/12/2025, 18:00 UK time

Assignment Credit: 10 credits

Word Count (or equivalent): 2000 +10%

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

Task One-Business analytics concepts [30 marks]

Critically evaluate five peer-reviewed journal articles that demonstrate the diverse and significant business applications of big data clustering techniques. Your review should meet the following criteria:

• Each article must be correctly cited and sourced from a reputable peer-reviewed journal.
• The five articles should represent five different industries (e.g. healthcare, retail, finance, manufacturing, logistics, etc.).
• Each article must also focus on a distinct business function.
• Your evaluation should discuss the clustering method(s) used, the type of data analysed, the business problem addressed, and the impact or insight generated.
• Include a comparative discussion on the strengths and limitations of clustering in each context.
• You are expected to apply critical thinking, incorporate relevant literature, and demonstrate how clustering supports evidence-based decision-making in different business settings.

Task Two- Marketing Analytics [35 marks]

You MUST use dataset 1, if your Student ID Card Number ends with an odd number (numbers are 1,3,5,7,9) and you MUST use dataset 2, if your Student ID Card Number ends with an even number (numbers are 0,2,4,6,8).

Scenario: Pricing Decision for a Consumer Electronics Company

By analysing historical sales data, a consumer electronics company has identified that the sales volume of personal audio devices (such as wireless earbuds and headphones) has fluctuated based on changes in product pricing over recent years. The sales figures are summarised in the table below.

The company is preparing to launch a new premium wireless noise-cancelling headphone, and an appropriate price point must be determined by evaluating market demand patterns and profitability trends.

The demand for the new product is price-sensitive — as the selling price increases, demand tends to decrease in a predictable manner.

The company’s objective is to maximise total profit from the launch of this new device. The unit production and distribution cost for each headphone set is £45, so the profit per unit is calculated as:

(selling price per unit – £45)

Using the historical pricing and demand data, determine the optimal price that will maximise total profit from the new product launch. 

Table 1a: Dataset 1

Price (£)	Demand (Units)
55	418
57	395
61	373
62	360
63	345
64	302
66	270
67	255
68	247
69	240
71	233
72	222
73	216
74	214
76	210
77	205

Table 1b: Dataset 2

Price (£)	Demand (Units)
60	405
62	382
65	360
66	340
67	325
68	305
70	278
71	265
72	252
73	240
75	228
76	220
77	215
78	210
80	208
81	200

a. Use Excel to determine the estimated demand quadratic equation function and describe why quadratic form is preferred.

b. Use Excel solver to find the optimal price which maximizes the company’s profit.

c. Determine the optimal demand.

d. Compute the optimal profit.

e. Interpret the results.

 f. If the unit cost is £35 and then £62 determine the optimal price and interpret the results appropriately.

 g. Suppose that the supply cost is £62, and the company has decided to set the price less than £70, then what will be the optimal price? Interpret the results.

Task Three- Forecasting [35 marks]

You MUST use dataset 1, if your Student ID Card Number ends with an odd number (numbers are 1,3,5,7,9) and you MUST use dataset 2, if your Student ID Card Number ends with an even number (numbers are 0,2,4,6,8).

Suppose you have collected quarterly sales revenue data from 2017 to 2023 for the consumer electronics company, focusing on its audio product line. This data reflects seasonal variations, product release cycles, and changes in market demand.

The company is planning to launch a new premium wireless noise-cancelling headphone in 2024 and requires a quarterly sales forecast to guide inventory planning, marketing budgets, and distribution logistics.

Using the historical data presented in Table 2, your task is to analyse past sales trends and develop a forecast for the expected sales in each quarter of 2024. This forecast will support strategic decision-making and ensure resources are aligned with expected demand throughout the year.

Table 2a: Dataset 1

Year	Q	Sales (Units)	Four Moving average	Baseline/CMA	Seasonality	Deseasonality	Trend	Forecast	Error	MAD	MSE	MAPE
2017	Q1	1400
2017	Q2	1100
2017	Q3	950
2017	Q4	1350
2018	Q1	1500
2018	Q2	1200
2018	Q3	1050
2018	Q4	1450
2019	Q1	1620
2019	Q2	1270
2019	Q3	1130
2019	Q4	1570
2020	Q1	1720
2020	Q2	1350
2020	Q3	1200
2020	Q4	1680
2021	Q1	1850
2021	Q2	1420
2021	Q3	1250
2021	Q4	1800
2022	Q1	1980
2022	Q2	1500
2022	Q3	1320
2022	Q4	1920
2023	Q1	2100
2023	Q2	1580
2023	Q3	1400
2023	Q4	2050

Table 2b: Dataset 2 

Year	Q	Sales (Units)	Four Moving average	Baseline/CMA	Seasonality	Deseasonality	Trend	Forecast	Error	MAD	MSE	MAPE
2017	Q1	1200
2017	Q2	950
2017	Q3	870
2017	Q4	1180
2018	Q1	1280
2018	Q2	1020
2018	Q3	940
2018	Q4	1250
2019	Q1	1360
2019	Q2	1080
2019	Q3	990
2019	Q4	1340
2020	Q1	1450
2020	Q2	1150
2020	Q3	1050
2020	Q4	1420
2021	Q1	1530
2021	Q2	1210
2021	Q3	1110
2021	Q4	1500
2022	Q1	1600
2022	Q2	1270
2022	Q3	1170
2022	Q4	1580
2023	Q1	1680
2023	Q2	1340
2023	Q3	1230
2023	Q4	1670

a. Plot the data and describe the main features of the series.

b. Compute the four-period moving average and enter your values in the appropriate columns. Calculate the Centered Moving Average (CMA)/Baseline. Interpret it.

c. Calculate the Trend and interpret the trend.

d. Determine the Seasonality (St) and interpret it properly.

e. Forecast the revenue for 8th year (i.e. 2024).

f. Calculate the Error, mean absolute percentage error (MAPE), Mean Square Error (MSE) and Mean Absolute Deviation (MAD).

g. Write a brief report to explain and evaluate and make comments on the error variables, the forecasted and actual sales.

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 11/12/2025. 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.

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.

3. Critically analyse and interpret the outputs of data mining models and forecasting results for end-users.

4. Solve managerial problems and make systematic decisions by applying business data analysis techniques.

5. Justify the appropriate application of business analytics to various international business contexts by selecting appropriate 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: Azadeh Pourmalek

Module Leader Email: ad9666@coventry.ac.uk

Assignment Category: Written

Attempt Type: Standard

Component Code: Cw2

Assessment Marking Criteria

	Theory, concepts and models	Analysis, evaluation and application	Critique, conclusions and recommendations
Exceptional First 80 to 100%	Exceptional use of seminar examples, demonstrating comprehensive insight and sophisticated application to context Exceptionally accurate and nuanced comparison between unsupervised clustering and supervised machine learning, showcasing deep conceptual understanding Exceptionally well-chosen curve and model, with insightful justification of inputs and methodological decisions Exceptionally clear, precise, and logically structured explanation of all steps, reflecting critical thinking and advanced analytical skill	Exceptional analysis of Solver outputs, demonstrating advanced understanding and critical interpretation Exceptionally thorough and insightful evaluation of model outputs, highlighting implications and accuracy Exceptionally detailed and accurate steps in calculating forecasted seasonality, reflecting deep methodological understanding Exceptionally insightful evaluation of peaks and lows, showing clear recognition of patterns and their significance Exceptional application to real-world context, demonstrating relevance, depth, and practical awareness	Exceptionally high-quality and well-researched examples underpinning the case studies, demonstrating critical depth and academic rigor Exceptionally clear and well-defined evaluation criteria for assessing realism, applied with precision and insight Exceptionally well-critiqued and contextually sound recommendations tailored to complex business situations Exceptionally strong presentation of analysis and recommendations, demonstrating clarity, coherence, and impact Exceptionally clear, well-structured, and logically sequenced presentation, enhancing understanding and engagement Exceptionally relevant and well-integrated visualizations, effectively supporting key points and insights Exceptionally insightful and critically engaged discussion, demonstrating depth, relevance, and synthesis of ideas
First 70 to 79%	Excellent use of seminar examples, demonstrating strong understanding and relevance Excellent and mostly accurate comparison of unsupervised clustering with supervised machine learning Excellent choice of curve and well-explained model inputs, showing solid justification Excellent clarity and focus in explanation of steps, with good logical flow Excellent analysis of Solver outputs, demonstrating confident interpretation Excellent evaluation and interpretation of model outputs, with thoughtful observations Excellent clarity in outlining steps to calculate forecasted seasonality Excellent evaluation of peaks and lows, highlighting key trends Excellent application to real-world context, with clear practical connections		Excellent examples of research supporting case studies, demonstrating good depth and relevance Excellent clarity in criteria for evaluating realism, appropriately applied Excellent critique of recommendations, showing strong contextual awareness Excellent presentation of analysis and recommendations, with coherent structure Excellent structure and logical flow in presentation, aiding audience understanding Excellent use of clear and relevant visualizations, effectively supporting the content Excellent discussion that is both relevant and insightful, demonstrating strong engagement with the topic
Upper Second 60 to 69%	Very good use of seminar examples, demonstrating relevant but limited insight Very good and generally accurate comparison of unsupervised clustering with supervised machine learning Very good and appropriate choice of curve, with adequately explained model inputs Very good explanation of steps, though with some room for improved clarity or depth	Very good analysis of Solver outputs, showing reasonable understanding Very good but somewhat limited evaluation and interpretation of model outputs Very good outline of steps for calculating forecasted seasonality, with minor gaps Very good evaluation of peaks and lows, though may lack depth or nuance Very good application to real-world context, with relevant but not fully developed insights	Very good examples of research supporting case studies, with some critical engagement Very good clarity in criteria for evaluating realism, though could be better applied Very good recommendations for business situations, with generally sound rationale Very good presentation of analysis and recommendations, though could be more polished Very good structure and logical flow in presentation, aiding comprehension Very good use of mostly relevant visualizations, supporting the content adequately Very good and relevant discussion, though may lack deeper insight or synthesis