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COMM1190 Assessment

创建日期：2024-05-23 15:21:58

所属分类：计算机computer science

标签： COMM1190

Assessment

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COMM1190 Assessment 2: Team Report

A written report
Maximum of 2000 words, excluding references, figures, tables, and appendix.
Via Turnitin on Moodle course site
Objective
In this team assignment, you will play the role of a team of junior consultants for the
firm Insight Bridge Consulting. You have been asked to continue your work on the data
analysis project for the e-commerce website, The Mobile Hub.
Recall that the Mobile Hub is a technology retailer that sells a range of consumer
electronic products, including smartphones, tablets and accessories. The site has
recently launched a new mobile app, but downloads and sales have been lower than
expected. Previously, The Mobile Hub hired Insight Bridge Consulting to help them
analyse a dataset of user behaviour and spending patterns in order to understand user
behaviour to optimise the app and improve sales.
The Mobile Hub has re-engaged Insight Bridge Consulting because the firm is trying
to be bought by E-Electronics Central. E-Electronics Central is a similar online retailer
to The Mobile Hub that sells related products such as audio equipment, cameras, and
gaming devices. E-Electronics also owns an online technology support provider called
Tech Support Central, which specialises in providing remote technical support and
troubleshooting services for a wide range of technology products. E-Electronics
Central wants to purchase The Mobile Hub for two reasons:
1. To expand its technology offerings into smartphones and tablets, which Tech
Support Central currently services; and
2. To gain access to The Mobile Hubs app, since E-Electronics Central currently
makes all sales through their online website.
However, E-Electronics Central is concerned about the Application Satisfaction of The
Mobile Hub.
As a result, The Mobile Hub wants an analysis that features descriptive and predictive
analytics techniques to generate actionable insights on how they can improve user’s
application satisfaction before entering negotiation with E-Electronics Central.
To help with the analysis, the company has provided Insight Bridge Consulting with
some clarification regarding the previous data: They clarified that “rural” in C_Region
refers to regional and that App_E-mailCommunication is about The Mobile Hub
contacting consumers and not the other way around. Moreover, The Mobile Hub has
also provided Insight Bridge Consulting with additional data:
1. C_Number_of_Orders – The number of orders been placed by the customer
using the app.
2. App_SatisfactionRating – A rating out of 100 in terms of satisfaction. The values
L, M, and H were determined by these ratings. Anyone with a rating above 75
was assigned an H by the mobile hubs IT team, anyone with a rating between
50 and 74 provided a rating of M, and anyone with a rating 49 or below was
given a rating of L. As App_SatisfactionRating has more information than the
categories L, M, and H, The Mobile Hub has dictated that the continuous
variable rating measure should be the focus of the app satisfaction analysis.
3. App_SharesWithFriends – Similar to App_SatisfcationRating, The Mobile Hub
also has data on the number of times a customer has shared content from the
app with a friend through a messenger service. They have replaced the
previous variable with numerical data on the number of shares.
4. App_Deleted – Whether or not the customer had previously deleted the app.
5. App_Deleted_Readded – Whether or not the customer had previously deleted
the app and re-added the app to their device.
6. App_ConsumerE-mailFirm – Whether or not the customer has e-mailed the
firm.
Guidance on Data Analysis:
Note: the dataset and the data dictionary will be provided to you separately.
• Critically and collaboratively reflect on the feedback that each team member has
received from their individual project and use them to develop your team project
where applicable.
• Use descriptive analytics to identify the key factors that may impact a user’s app
satisfaction. Descriptive Analytics refers to statistics and visualization techniques.
For example, a box plot and a bar chart are two different techniques.
• Use predictive analytics to diagnose and/or forecast factors that influence users’
app 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. You
should use the modelling techniques discussed in lectures and workshops (i.e.,
do not use modelling techniques beyond the scope of this course).

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