Overview
Assignment Title
Business Decision-Making Using Machine Learning: Analysis of a Real-World Business
Problem
Word Count: 2,000–3,000 words
Submission Format: MS Word
Referencing Style: APA Submission
All sources must be cited using APA.
reputable business sources) is required.
Assignment Purpose
Modern organisations increasingly use Machine Learning (ML) to support strategic, tactical, and
operational decision-making. This assignment aims to develop students' understanding of how ML
techniques can improve business decisions through data-driven insights.
Students will identify business problems, examine how machine learning can support decision
making, select appropriate ML techniques, evaluate business implications, and provide managerial
recommendations.
Assignment Scenario
1
Assume you are a Business Analytics Consultant hired by an organisation seeking to improve
decision-making through machine learning.
Select ONE of the following business decision-making scenarios:
Option 1: Customer Retention Decision
A company wants to identify customers likely to leave and determine retention strategies.
Option 2: Loan Approval Decision
A bank wants to improve credit approval decisions while reducing loan default risk.
Option 3: Inventory Management Decision
A retail company wants to forecast product demand and optimise inventory levels.
Option 4: Marketing Campaign Decision
An e-commerce company wants to identify customers most likely to respond to promotions.
Option 5: Fraud Investigation Decision
A financial institution wants to identify suspicious transactions requiring investigation.
Option 6: Employee Retention Decision
A company wants to identify employees at risk of resignation and design retention programs.
Option 7: Supply Chain Decision
A manufacturing company wants to predict disruptions and optimise supplier selection.
Assignment Requirements
Section 1: Business Problem and Decision Context
Task
Describe the selected organisation and explain the business decision that needs improvement.
Include:
2
1.1 Organisation Background
1.2 Business Problem
Explain:
1.3 Decision-Making Context
Identify:
Example
Organisation: Telecommunications Company
Problem: High customer churn rates.
Required Decision: Which customers should receive retention offers?
Decision Type: Tactical and operational decision.
Section 2: Role of Machine Learning in Business Decision-Making (4 Marks)
Task
Explain how machine learning supports business decisions.
3
Include:
2.1 Machine Learning Overview
Define:
2.2 Types of Machine Learning
Explain:
Supervised Learning
Examples:
Unsupervised Learning
Examples:
Reinforcement Learning
Examples:
2.3 Selected ML Approach
Justify why your chosen approach is suitable.
Section 3: Data Requirements
4
Task
Identify the data required for decision-making.
Section 4: Machine Learning Model Selection and Application
Task
Select one or two ML algorithms suitable for the decision problem.
Include:
4.1 Algorithm Selection
Examples:
Business Decision
Churn Prediction
Credit Approval
Sales Forecasting
Fraud Detection
Customer Segmentation
Pricing Decision
4.2 How the Algorithm Works
Explain in simple terms.
4.3 Decision Support
Explain how managers use model output for decision-making.
ML Technique
Decision Tree
Logistic Regression
Linear Regression
Random Forest
K-Means Clustering
Reinforcement Learning
Section 5: Business Impact, Ethical Issues and Risks
Task
Evaluate organisational implications.
5.1 Benefits
5
Discuss at least five benefits.
Examples:
5.2 Risks and Challenges
Examples:
5.3 Ethical Considerations
Discuss:
Use examples from GCC or international organisations.
6
Section 6: Recommendations and Conclusion
Recommendations
Provide at least five practical recommendations.
Examples:
1. Establish data governance policies.
2. Improve data quality management.
3. Train employees in data analytics.
4. Regularly monitor model performance.
5. Implement ethical AI guidelines.
Conclusion
Summarise:
Table of Contents
Executive Summary (150–200 words)
1. Business Problem and Decision Context
2. Machine Learning in Business Decision-Making
3. Data Requirements and Preparation
4. Machine Learning Model Selection and Application
5. Business Impact, Ethical Issues and Risks
6. Recommendations and Conclusion
References
Appendices (if applicable)
Illustrative Example
Topic:
Using Machine Learning for Customer Churn Decision-Making in the Telecommunications
Industry
Business Problem
A telecom company loses approximately 15% of customers annually. Customer acquisition costs
are increasing, making retention a strategic priority.
Decision Required
Which customers are most likely to leave within the next three months, and what retention actions
should be taken?
ML Technique
Decision Tree Classification
Data Variables
Expected Model Output
Customer
Customer A
Churn Probability
85%
9
Customer
Customer B
Customer C
Decision-Making Application
Managers can:
Business Benefits
Ethical Issues
Recommendation
Churn Probability
22%
78%
Deploy the churn prediction model and review model performance quarterly to maintain prediction
accuracy and business relevance.
10