QBUS3600 Group Project - Big W
Due dates: Monday 23 October 2023 and others (details provided below)
Value: 40%
Length: maximum of 25 pages excluding appendices
Notes to Students
1. The assignment MUST be submitted, by the team leader, electronically to Turnitin
through QBUS3600 Canvas site. Please do NOT submit a zipped file.
2. The main assignment document is due on Monday 23 October and others like
meeting agendas/minutes are at various dates as indicated on Canvas. The late
penalty for the assignment is 5% of the assigned mark per day, starting after 5:00pm
on the due date. The closing date is the 7th day after the due date which is the last
date on which an assessment will be accepted for marking, normally one week after
the announced due date.
3. The data sets for this assignment can be downloaded from Canvas. The dataset is
highly confidential, and you have responsibility to keep it secure and for it to be used
only for your QBUS3600 coursework.
4. Presentation of the assignment is part of the assignment. Marks are assigned for
clarity of writing and presentation.
5. Think about the best and most structured way to present your work, summarise the
procedures implemented, support your results/findings and prove the originality of
your work.
6. Numbers with decimals should be reported to the second decimal point.
2023S2 QBUS3600 Group Assignment - Big W 1
Background and Task
During the individual assignment, you identified several potential insights into identifying
the top behaviours and attributes that are likely to be useful for predicting total store
sales. Now, your task as a group is to synthesise your potential insights and construct a
model which can perform this prediction task.
You will need to build a model with whatever machine learning approaches you feel
appropriate. You should evaluate your model/s on a range of metrics, however, the RMSE
(defined below) will be used to evaluate the performance of your final model on the test
data. You should follow an industry recognised approach to Data Science problems (e.g.
CRISP-DM) and include a justification for your selected model. You will be required to show
the methods you used to prioritise your potential insights and defend the models and results
with supporting evidence. You will also be required to submit your retention predictions on
the test data.
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Important:
1) Please use the pre-splitted training and test set that has already been provided.
Your evaluation metrics on the test set are important.
2) Please consider which variables are not available at the time of predictors, and
exclude those as predictor variables (because in real life, your model won’t have
them available when making predictions!). You can read more about data leakage
here: https://www.kaggle.com/code/alexisbcook/data-leakage
The Woolworths Group Team will be available for a Q&A session with the class (date and
time TBA). This session will run for 60 minutes; groups will have the opportunity questions of
the management team. Please use this session to ask questions following your engagement
with the problem and data through individual assignment 1. This Q&A session will be
recorded and shared with students who are unable to attend, however each group is
required to have at least one member in attendance.
The final task for the group will be to design a potential project for the Woolworths team to
execute, to take advantage of the key behaviours and attributes that can help Woolworths
increase store sales for their Big W chain. This project could include (but not limited to)
opening stores in new areas, pushing specific investments or promotions at certain times, or
focusing on e-commerce in specific areas. Each project must be supported by an estimated
improvement in overall revenue with supporting data and assumptions.
Please limit the number of recommended projects to 1-2. Also note that it is ideal for
groups to recommend deployment of their model, however groups can also leverage model
insights for recommendations, as long as the recommendations are closely linked to the
insights and not overly general in nature (e.g. general app redevelopment or event).
2023S2 QBUS3600 Group Assignment - Big W 2
The output of your group assignment will consist of a written report (of no more than 25
pages) and a presentation of no more than 15 minutes. In this task, you can assume that the
audience will be the Woolworths Group team. The top 1-2 best performing teams will be
selected and will have the opportunity to present in-person to the Woolworths Group team.
Your report and presentation should include (but not limited to):
• Summary of potential insights the group took from the individual task.
• Summary of analysis, modelling, model selection and validation.
• Detailed outline of your strategic project:
− Description of your recommendation:
+ What are you recommending to do/change?
+ Who are you recommending it for (targeted at a group or for everyone)?
+ How would you implement your recommendation?
− Rationale for your recommendation (your data-driven hypothesis).
− Projected impact of the project with supporting evidence and assumptions.
− Estimated costs (based on what you know) of the project with supporting
evidence and assumptions.
Deliverables
Report (65%)
Write a report, limited to 25 pages, to describe, explain, and justify your analyses, models
and findings to Woolworths Group executives. Make sure your report is concise and
objective.
Separately, if relevant provide sharable models and associated code.
Oral Presentation (20%)
Design and deliver an oral presentation of 15 minutes in length to summarise your work for
an executive or managerial audience. All group members must speak for equal amounts of
time.
Remember, your business audience will not be experts in business analytics. As such, your
presentation should translate the analysis, modelling and findings to business impact.
Meeting Minutes (15% - Required)
Your group is required to submit meeting agendas and minutes for at least THREE group
meetings. Your group should use the attached templates for preparing agendas and meeting
2023S2 QBUS3600 Group Assignment - Big W 3
minutes. The meeting minutes shall document what have been discussed, action items, and
reflection on the completed tasks from individual members etc.
Peer Assessments (0% - Required)
You must submit a peer assessment at the conclusion of the assignment. Details of how to
complete the peer assessment will be provided at a later date. In the case a student fails to
submit a peer assessment report, a 5-mark penalty will be deducted from the awarded
group marks.
Your marks will be adjusted based on the peer reviews by your team members.
Due Dates
Report: 17:00 Friday 28 October 2022
Meeting Minutes:
1st Meeting Minutes: 17:00 Friday 1 September 2023
2nd Meeting Minutes: 17:00 Friday 22 September 2023
3rd Meeting Minutes: 17:00 Monday 23 October 2023
Oral Presentation: Week 12 and/or 13 (TBA)
Disputes
If there is a dispute within a group, please bring them to the unit-coordinator as soon as
possible, with evidence, so that they may be resolved quickly and equitably.
Uncooperative/uncontactable group members will be removed from groups and will
complete their assignment individually. Members who do not contribute equally may have
their marks adjusted based on ad hoc reports to the coordinator during the assignment and
the final peer assessment.
Marks and Feedback
1. Each group will be awarded a group mark per the marking criteria. In some cases,
individual marks may be applied if there is dispute in a group and the quality or
quantity of contributions made by individuals are significantly different, in which
cases the unit coordinator will review the meeting minutes and peer assessment
reports.
2. Feedback will be provided on the marked submission or through the Review system.
Marking and Key Rules:
2023S2 QBUS3600 Group Assignment - Big W 4
Your deliverables will be marked against the following criteria and rules:
• The research problem aim/s, question/s and/or hypotheses clearly articulated.
• Selection, critical evaluation and use of relevant scholarly and practitioner literature
that substantiates the research problem, integrates previous research and identifies
theory/theories to be used in the scholarly study
• Choice and justification of research methods
• Clearly draw conclusions based on analysis
• Proper training of machine learning pipelines
• Statements are clear, concise and accurate, with correct spelling, free of grammar
errors and correct use of punctuation
• Use of presentation technique is appropriate
• Group presentation is well performed in terms of confidence, connection with
audience, passion, timing and logic
• The reports are well structured and sentences are well connected
• Demonstrates consideration for the audience
• Clear, concise and commented Python code, if any
2023S2 QBUS3600 Group Assignment - Big W 5