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ASSESSMENT 2 GUIDE
COMM1190
Data, Insights and Decisions
Term 2, 2024

Assessment Details
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Due Date Weighting Format Length/Duration Submission

Turnitin
Turnitin is an originality checking and plagiarism prevention tool that enables checking of submitted written work for
improper citation or misappropriated content. Each Turnitin assignment is checked against other students' work, the
Internet and key resources selected by your Course Coordinator.
If you are instructed to submit your assessment via Turnitin, you will find the link to the Turnitin submission in your
Moodle course site. You can submit your assessment well before the deadline and use the Similarity Report to
improve your academic writing skills before submitting your final version.
You can find out more information in the Turnitin information site for students.
Late Submissions
The parameters for late submissions are outlined in the UNSW Assessment Implementation Procedure. For this
course, if you submit your assessments after the due date, you will incur penalties for late submission unless you
have Special Consideration (see below). Late submission is 5% per day (including weekends), calculated from the
marks allocated to that assessment (not your grade). Assessments will not be accepted more than 5 days late.
Extensions
You are expected to manage your time to meet assessment due dates. If you do require an extension to your
assessment, please make a request as early as possible before the due date via the special consideration portal on
myUNSW (My Student profile > Special Consideration). You can find more information on Special Consideration and
the application process below. Lecturers and tutors do not have the ability to grant extensions.
Special Consideration
Special consideration is the process for assessing the impact of short-term events beyond your control (exceptional
circumstances), on your performance in a specific assessment task.
What are circumstances beyond my control?
These are exceptional circumstances or situations that may:
Prevent you from completing a course requirement,
Keep you from attending an assessment,
Stop you from submitting an assessment,
Significantly affect your assessment performance.

Available here is a list of circumstances that may be beyond your control. This is only a list of examples, and your
exact circumstances may not be listed.
You can find more detail and the application form on the Special Consideration site, or in the UNSW Special
Consideration Application and Assessment Information for Students.

UNSW Business School

Use of AI
For this assessment, you may use AI-based software however please take note of the attribution requirements
described below:
Coding: You may freely use generative AI to generate R code for your analysis, without attribution. You do not need to
report this.
Written report: Use of generative AI in any way to produce the written (prose) portions of the report must be
completely documented by providing full transcripts of the input and output from generative AI with your submission.
Examples of use that must be documented (this is not an exhaustive list): editing your first draft, generating text for
the report, translation from another language into English.
Any output of generative AI software that is used within your written report must be attributed with full referencing. If
the outputs of generative AI software form part of your submission and is not appropriately attributed, your marker will
determine whether the omission is significant. If so, you may be asked to explain your understanding of your
submission. If you are unable to satisfactorily demonstrate your understanding of your submission you may be
referred to UNSW Conduct & Integrity Office for investigation for academic misconduct and possible penalties.

AI-related resources and support:
Ethical and Responsible Use of Artificial Intelligence at UNSW
Referencing and acknowledging the use of artificial intelligence tools
Guide to Using Microsoft Copilot with Commercial Data Protection for UNSW Students

Assessment 2: Customer churn project
Stage 1 C Individual Report Stage 2 C Group Report

Week 7: 5:00pm Wednesday 10 July 2024

Week 9: 5:00pm Wednesday 24 July 2024
Individual report (template provided)

Group report

2 pages

~ 4 pages

Via Turnitin and attached as appendices with
Stage 2 submission

Via Turnitin

Description of assessment tasks
This is a group assessment with reporting being done in two stages. Students will be assigned to groups in Week 5
when this documentation is released. While reporting in done in two stages students are encouraged to commence
their collaboration within their group early in the process before the submission of the Stage 1 individual reports.

Stage 1: Complete the 2-page individual task using a data set specific to your Assessment 2 project group.
This first-stage submission contains key inputs into the group work that will result in the single group
report produced in Stage 2. Students who do not submit a complete, legitimate attempt of this
assessment will not be awarded marks for Stage 2.

This individual task will be separately assessed together with the Stage 2 group report, and
associated marks will be available together with Stage 2 marks. Because of the nature of the
relationship between the Stage 1 and 2 tasks, you will not receive your Stage 1 marks before
submitting Stage 2.

Stage 2: As a group, use the results from Stage 1 to produce a report for the Head of Management Services.
You will use R to explore a dataset that includes the pilot data together with extra observations and
variables (see attached Appendix A Data Dictionary). The pilot data are common to all students, but
the extra observations will vary across students according to their SID as they did in Assessment 1. A
group-specific data set will be determined by nominating the SID of one of the group members to
generate the single data set used by all group members in both stages of the project. Details for
obtaining the personalized group-specific data set will be provided on Moodle

Your Stage 2 group mark will be common to all students in your group who have submitted a
complete, legitimate attempt of Stage 1.

Note: The course content from Weeks 4, 5, and 7 will be of particular relevance to completing this Assessment.

UNSW Business School

Context of assessment tasks
The Head of Management Services of Freshland, a large grocery store chain in Australia, has made use of your
updated report (from Assessment 1) to deliver a presentation to the Senior Executive Group.
Access this presentation via your Moodle course site.
Based on this initial analysis and recommendations, approval has been given for further analysis of customer loyalty
and churn using an expanded data set. The core task will involve a comparison of predictive models and subsequent
recommendations on how to use and improve these to inform future retention policies.

The analysis in the presentation to the Senior Executive Group was based on the initial pilot data set which was used
by the intern to produce the initial report and was part of the data provided to you with Assessment 1. These data
have now been extended, with extra variables being added. These extra variables are:

ltmem =1 if 3
mamt1 Average monthly expenditure ($) in first 6 months of previous year (2023)
mamt2 Average monthly expenditure ($) in second 6 months of previous year (2023)
fr1 Frequency of monthly transactions in first 6 months of previous year; 1 (low) 2 (medium), 3 (high)
fr2 Frequency of monthly transactions in second 6 months of previous year; 1 (low) 2 (medium), 3 (high)
rind XYZ risk index in the form of a predicted probability of customer churn

You and your team have been tasked with investigating alternative algorithms for predicting customer churn. Given
the structure of data that has been made available, you have been advised to define churning to be when a customer
has previously had non-zero transactions for at least 6 months but then has zero transactions in the next six-month
period. The outcome of interest is the binary variable churn. Given the available data, an observation for a customer
will have = 1 if 1 > 0?&?2 = 0? and ? = 0 if 1 > 0?&?2 > 0.

The Head of Management Services has given you authority to use your expert judgment to make the necessary
modelling choices but has outlined an overarching research plan for you and your group to follow:

Currently, Management Services has a basic regression model (details below) that can be used to predict
future customer expenditure for members of the rewards program. It has been suggested that this could be
used to generate a risk index where those with predicted expenditures that are low relative to actual
expenditures being deemed as high risk of no longer shopping at the store.
However, there were suggestions that the existing model could be improved as a predictor of expenditures
and your group has been asked to evaluate a range of model extensions.
The current focus is on predicting churn. Based on the performance of the alternative models in predicting
expenditures, choose one and analyse whether it also performs well in predicting churn.
An analytics firm, XYZ, that uses proprietary predictive methodology has offered a trial of their products by
providing a predictor of churn. Your evaluation of predictive performance should include a comparison of this
predictor with that generated by your chosen regression-based predictor.
Based on this analysis, make recommendations on using such algorithms in initiatives targeting customers at
risk of churning with the aim of retaining them as loyal customers.
o Notice that any recommendation to employ the predictors of the analytics firm would involve
additional cost compared to a method produced in-house by Management Services.
o In addition, any decision to employ the predictors of XYZ will not include documentation of the
methodology used to generate the predictions.
o It might also be that you conclude that neither predictor is adequate and that it would be appropriate
to explore alternative predictors or approaches. You are not expected to explore such alternatives.