COMM1190 Assignment

Objective

This team assessment aims to test your ability to conceptualize and solve analytic problems, your skills in R programming, your knowledge of the ethical use of data, and your ability to provide business recommendations based on analytic results. You will form a data analytic team with your peers in this assignment. You are expected to analyze data using descriptive and predictive techniques. The learning content has been covered in the course up until the end of Week 7.

Description

In this team assessment, you must take the business scenario in the individual assessment further to generate actionable insights on improving sales and app satisfaction for ASAL. Recall that:

The online e-store Amazing Sports Australia Ltd (ASAL) sells a variety of branded and non-branded sports products, which have been broadly categorized into (i) Equipment, (ii) Apparel, and (iii) Footwear. The site has recently released a shopping mobile app but is concerned about whether it has effectively boosted sales and promoted its products. The management team seeks to understand customer spending patterns and behaviour with the ultimate objective of optimizing app use and enhancing sales.

ASAL has updated the data set and collected additional data on variables. An updated data dictionary has been shared with you in a separate file.

ASAL requires you to:

1. Form an analytic team to use descriptive and predictive analytic techniques to generate actionable insights on how to improve user sales and application satisfaction.
2. Reflect on the feedback from your project and take it further to help ASAL predict the factors that influence a) user sales and b) application satisfaction.
3. Suggest the ethical considerations related to the analysis and the use of data for enhancing sales and application satisfaction.
4. Submit your findings as a written report by 3 pm July 21st (Friday) via Turnitin on Moodle.

How to Download Data

Download the team leader’s personalized data, using the link below and change z9999999 to your zID and access it by copying into R Studio:

dt <- read.csv("https://raw.githubusercontent.com/dat
analytics/data_access_2_t2_2023/main/z3165140_z3165140-Assessment2Data.csv")

Note that each team will have a personalized data set. Hence, different results and recommendations may emerge across teams even when using the same analytic technique.

Guidance on Data Analysis

1. Critically and collaboratively reflect on each team member’s feedback from their project and use them to develop your team project where applicable.
2. Use descriptive analytic to identify the key factors that may impact a user’s sales and application satisfaction. Descriptive Analytics refers to statistics and visualization techniques. For example, a box plot and a bar chart are different techniques.
3. Use predictive analytic to forecast the factors that influence future user sales and application satisfaction. Predictive Analytics refers to linear regression, logistic regression, and decision tree modelling techniques. For example, linear regression and logistic regression are two different modeling techniques. It would be best if you used the modelling techniques discussed in lectures and workshops (i.e., do not use modelling techniques beyond the scope of this course).
4. For each modelling technique (e.g., linear regression, decision tree, etc.) you use, consider trying out several models using different independent variables to predict the outcome variable and present the “best” model in your report. To select a model to be the “best” out of your candidate models, you can assess it based on the model’s goodness of fit and its performance in predicting the outcome variable. You should use methods and criteria learned from this course to test the goodness of fit and its performance (i.e., do not use methods and criteria beyond the scope of this course).
5. Develop coherent logic from your business issue identification to your variables and modeling techniques selection and your recommendations to ASAL.
6. Explicitly state any key assumptions that impact your data analysis.

Requirements

1. Business Issue Identification (10%)
   • State business issues that your report seeks to address. Examples of business issues:
     • What are the key factors associated with sales? How do these factors influence voluntary sales?
     • What are the key factors associated with application satisfaction?
   
   How do these factors influence users’ satisfaction with the application?
2. Data Analysis (40%)
   • Use appropriate descriptive analytics techniques and/or a relevant industry context about sales and application satisfaction to identify key variables for predictive analysis.
   • Use predictive analytics modelling techniques to forecast how certain variables impact sales and application satisfaction.
   • Justify the selection of variables and analytics techniques.
   • Interpret analytics results.
3. Business Recommendations (20%)
   • Provide recommendations based on analytics results.
   • Support recommendations based on established industry practices and/or academic references.
4. Ethical Consideration and Suggestions (10%)
   • Identify ethical issues about data collection, data analysis, and data communication.
   • Provide suggestions to avoid and/or mitigate the issues.
   • Supplementary reading:
     • Consult Danish Design Centre’s Digital Ethics Compass (https://ddc.dk/tools/toolkit-the-digital-ethics-compass/) to understand the nuances of data ethics in the context of digital products
5. Project Management (10%)
   • Follow USNW Guide to Group Work (must read) to participate in the team project.
   • Develop and record a project management plan by specifying key milestones and each team member’s responsibilities.
   • Nominate a team project lead to facilitate the collaboration.
   • Reflect on team project management, for example, the issues impeding effective collaboration; how you would do differently for improvement if you had the time again (150 words maximum).
   
   Note that if any issue emerges from the collaboration and requires the teaching team’s support, a team should report the issues to the teaching team as early as possible by involving all team members.
6. Communication and Organization of Report (10%)
   • Demonstrate proficiency in writing in English.
   • Develop a logical structure to organize the sections of your report.
   • Develop an executive summary using jargon-free language.
   • Uses figures and/or tables to convey qualitative and quantitative information effectively and accurately.
   • Use academic referencing in Harvard style. Refer to UNSW guideline: https://www.student.unsw.edu.au/harvard-referencing
   • Attach the codes of your R programming (not a screenshot) in the Appendix of your report.

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