THE UNIVERSITY OF NEW SOUTH WALES
SCHOOL OF INFORMATION SYSTEMS AND TECHNOLOGY MANAGEMENT TERM 2 2020
INFS5720: BUSINESS ANALYTICS METHODS
FINAL EXAMINATION
1. Time Allowed: 24 Hours.
2. This is a Take-Home Exam, your responses must be your own original work. You must attempt this Take-Home Exam by yourself without any help from others. Thus, you have NOT worked, collaborated or colluded with any other persons in the formulation of your responses. The work that you are submitting for your Take-Home Exam is your OWN work.
3. Release date/time (via Moodle): Friday, 21 August 2020 9:00am (Australian Eastern Time Zone)
4. Submission date/time (Via Turnitin): Saturday, 22 August 2020 8:59am (Australian Eastern Time Zone)
5. Failure to upload the exam by the submission time will result in a penalty of 15% of the available marks per hour of lateness.
6. This Examination Paper has 6 pages, including the cover page.
7. Total number of Questions: 3 Questions. Question 1 and 2 have sub-parts.
8. Answer all 3 Questions.
9. Total marks available: 100 marks. This examination is worth 55% of the total marks for the course.
10. Questions are not of equal value. Marks available for question sub-parts are shown on this examination paper.
11. Some questions have word limits as indicated on the question. These word limits must be adhered to. Text in excess of the specified word limit(s) may not be considered in the marking process.
12. Candidates must submit a signed Declaration Form together with the Take- Home Exam answer document. Failure to submit the signed Declaration Form
may result in your Take-Home Exam answer sheet not being marked.
13. Answers to questions are to be written in the template provided. Please ensure that you provide the following details on your Take-Home Exam answer sheet:
• Student ID:
14. In accordance with the Declaration Form, this Take-Home Exam paper cannot
be copied, forwarded or shared.
15. Students are reminded of UNSW’s rules regarding Academic Integrity and Plagiarism. Plagiarism is a serious breach of ethics at UNSW and is not taken lightly. For details see Examples of plagiarism.
16. This Take-Home Exam is an open book/open web, further information is available “Here”.
• You are permitted to refer to your course notes, any materials provided by the course convenor or lecturer, books, journal articles, or tutorial materials.
• It is sufficient to use in-text citations that include the following information: the name of the author or authors; the year of publication; the page number (where the information/idea can be located on a particular page when directly quoted), For example, (McConville, 2011, p.188).
• You are required to cite your sources and attribute direct quotes appropriately when using external sources (other than your course materials).
• When citing Internet sources, please use the following format: website/page title and date.
• If you provide in-text citations, you MUST provide a Reference List. The Reference list will NOT BE counted towards your word limit.
17. Students are advised to read the Take-Home Exam paper thoroughly before commencing.
18. The Lecturer-in-Charge (LiC) / Exam Referee will be available online (via Moodle) after the Take-Home Exam paper is released for a period of two hours.
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QUESTION 1 30 MARKS
Based on the case study Predicting Customer Churn at QWE Inc. by Anton Ovchinnikov, answer the following questions:
A) Explain the key challenge faced by the VP of customer services. (Your answer should be less than 200 words. Only the first 200 words will be marked.)
(5 marks)
B) Evaluate FOUR (4) potential use cases of how predictive analytics could be used to benefit an organisation like QWE. (Your answer should be less than 400 words. Only the first 400 words will be marked.) (10 marks)
C) QWE executives are giving you the opportunity to pitch ONE (1) of your use cases from Question 1B. Recommend with justification which case you would pitch to the executives. (Your answer should be less than 400 words. Only the first 400 words will be marked.) (10 marks)
D) Identify and describe TWO (2) negative consequences of implementing the use case you recommended in Question 1C. (Your answer should be less than 200 words. Only the first 200 words will be marked.) (5 marks)
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QUESTION 2 50 MARKS
In SAS VA, you will find a dataset ‘UVA_QA_0806X’, this is the data behind the case study Predicting Customer Churn at QWE Inc. by Anton Ovchinnikov. A data dictionary is available in the appendix of this document.
Prior to conducting any analysis on the dataset, please add a filter on the whole dataset using the ‘ID’ variable, and make the lower bound the first two digits of your student number. Then, set the upper bound to ‘62XY’, where XY represents the last two digits of your student number. See diagram as reference:
For example, if your student number is ‘1234567’, the first two digits is ‘12’, making the lower bound ‘12’ and the last two digits is ‘67’, thus making the upper bound ‘6267’, as shown below:
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Using this filtered dataset answer the following questions, including screenshots of the model outputs as appropriate:
A) Create two models in SAS VA that can be used to effectively predict customer churn. One model for customers who have been with QWE for no more than 10 months (i.e., 0 – 10 months inclusive) and another model for customers who have been with QWE for greater than 10 months. Each model can include a maximum of 4 independent variables; note that these variables may differ between the two models. Justify the choice for the inclusion of those independent variables. (Your answer should be less than 200 words. Only the first 200 words will be marked.) (10 marks)
B) Compare and evaluate the two models built in Question 2A. Justify which model is more effective for predicting churn for the specified customer age? (Your answer should be less than 300 words. Only the first 300 words will be marked.)
(15 marks)
C) From two models built, explain the key findings and using these findings recommend insights, policies and strategies that can help QWE minimize customer churn. (Your answer should be less than 500 words. Only the first 500 words will be marked.) (25 marks)
QUESTION 3 20 MARKS
Based on your experience, identify organisational benefits and challenges from using an AML tool such as DataRobot compared with SAS VA to make decisions. The critical reflection should include lessons learned from your experiences using the tools, with a focus on the value and challenges of working with data to provide insights. (Your answer should be less than 400 words. Only the first 400 words will be marked.)
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APPENDIX: DATA DICTIONARY
VARIABLE NAME
DEFINITION
A unique identifier for every customer on record. Note that the order of the data loaded in SAS VA will not match the sample 10 found in the case, the sample data depicted in the case if there for illustrative purposes only.
ID
Customer Age
(in months)
The age in months which an individual has been a customer with QWE.
Churn
(1 = Yes, 0 = No)
Flag to indicate whether a customer has churned, a ‘1’ indicates the customer has
churned and ‘0’ indicates the customer is still with QWE.
CHI Score Month
Current month’s (December) Customer Happiness Index (CHI), a higher index score corresponds to a greater level of customer happiness.
0
The difference between the current month’s (December) CHI and the previous month (November). A negative value means the number decreased from November to December, while a positive number means increasing.
CHI Score 0-1
Support Cases
The number of support cases for that customer in the current month (December).
Month 0
Support Cases 0-1
The difference between the number of current month’s (December) support cases and the previous month (November). A negative value means the number decreased from November to December, while a positive number means increasing.
The average support priority assigned to the current month’s (December) support cases, a higher value corresponds to a greater priority.
SP Month 0
The difference in the average support priority assigned to the current month’s (December) support cases and the previous month (November). A negative value means the number decreased from November to December, while a positive number means increasing.
SP 0-1
The difference in the number of logins made by the customer in the current month (December) compared to the previous month (November). A negative value means the number decreased from November to December, while a positive number means increasing.
Logins 0-1
The difference in the number of blog articles written in the current month compared to the previous month. A negative value means the number decreased from November to December, while a positive number means increasing.
Blog Articles 0-1
The difference in the number of views in the current month compared to the previous month. A negative value means the number decreased from November to December, while a positive number means increasing.
Views 0-1
The difference in the number of days since the last login in the current month compared to the previous month. A negative value means the number decreased from November to December, while a positive number means increasing.
Days Since Last
Login 0-1
— END OF EXAMINATION PAPER —
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