Learning outcomes
On successful completion of this component you will have demonstrated competence in the
following areas:
1. Clearly understand the types of analytics used in marketing and the impact of data-driven
decision making.
2. Effectively access and process various types of marketing data.
3. Appropriately summarise and visualise marketing data.
4. Critically interpret and appraise regression, classification, and clustering models for
marketing analytics.
5. Effectively use software tools to obtain and present analytical results from data science
models.
Coursework task
This assessment is an individually assessed assignment. You are going to analyse the customer churn
problem of telephone service companies. Customer churn is the phenomenon where customers of a
business no longer purchase or interact with the business. Therefore, a high churn means that a higher
number of customers no longer want to purchase goods and services from the business. Telephone
service companies often have customer service branches which attempt to win back defecting clients,
because recovered long-term customers can be worth much more to a company than newly recruited
clients.
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The data related to this assessment can be downloaded from Moodle. You need to write a report to
discuss how do you complete the tasks and go into sufficient depth to demonstrate knowledge and
critical understanding of the relevant processes involved. 100% of available marks are through the
completion of the written report, with clear and separate marking criteria for each required report
section. Notably, a distinct and significant report section on discussing and critiquing the analysis and
implementation processes you carried out for your data solution is required.
Your report must conform to the below structure and include the required content as described, with
information on specific marking criteria for each section available in the accompanying assessment
criterion reference. You must supply a written report containing four distinct sections that provide
a full and reflective account of the processes undertaken.
Section I: Data Loading, Pre-Processing, and Summary (15%)
As a first step, you need to download the data from Moodle. There are two datasets: Dataset_01.csv
and Dataset_02.csv are the data you are going to use to train and test your prediction model for
customer churn. The variables in both datasets are briefly explained as follows:
Dataset_01.csv & Dataset_02.csv
Variable Description
User_ID Customer ID
User_Gender Female or male
Is_Senior Whether the customer is a senior citizen (Yes, No)
Has_Partner Whether the customer has a partner or not (Yes, No)
Has_Children Whether the customer has children or not (Yes, No)
Usage_Length Number of months the customer has stayed with the company
Has_Phone_Service Whether the customer has a phone service or not (Yes, No)
Multiple_Lines Whether the customer has multiple lines or not (Yes, No, No phone service)
Intnet_Provider Customer’s Internet service provider (DSL, Fiber optic, No)
Has_Security_Service Whether the customer has online security or not (Yes, No, No internet service)
Has_Online_Backup Whether the customer has online backup or not (Yes, No, No internet service)
Has_Device_Protection Whether the customer has device protection or not (Yes, No, No internet service)
Has_Tech_Support Whether the customer has tech support or not (Yes, No, No internet service)
Has_Steam_TV Whether the customer has streaming TV or not (Yes, No, No internet service)
Has_Steam_Movies Whether the customer has streaming movies or not (Yes, No, No internet service)
Contract_Type The contract term of the customer (Month-to-month, One year, Two year)
Has_Paperless_Billing Whether the customer has paperless billing or not (Yes, No)
Payment_Method The customer’s payment method (Electronic check, Mailed check, Bank transfer
(automatic), Credit card (automatic))
Monthly_Fee The amount charged to the customer monthly
Total_Fee The total amount charged to the customer
Attrition Whether the customer churned or not (Yes or No)
You need to upload Dataset_01.csv and Dataset_02.csv onto Microsoft Azure Machine Learning (ML)
Studio and merge them into Data.csv file. You need to provide a screenshot of this step (5%).
Have you realised a feature column in the data is useless for our analysis? Tell us why you think it is
useless and use Microsoft Azure to remove this feature from the dataset (provide screenshots of this
step) (5%).
How many numeric features exist in our dataset and what are they? Try to provide a description table
for these numeric features which contain the following values (5%):
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Feature name Mean Median Min Max Standard deviation Number of unique values
… … … … … … …
… … … … … … …
Section II: Preliminary Analysis (15%)
Try to verify the following statements by using Microsoft Excel PivotTable:
1. Females users with children tend to have lower monthly fee than the females without children
(3%).
2. New users (i.e. with less than 12 months usage length) are more likely to churn than the old
users (3%).
3. Senior users are less likely to use the online service when compared with other users (3%).
4. Senior users tend to have lower monthly fee than other users (3%).
5. Users who pay the fee via credit card have a higher average monthly fee (3%).
You should give clear data analysis evidence to support your answer. For example:
Statement: Users with partners tend to have a higher monthly fee.
Example answer: Yes, as the below table shows, users who have partner have higher average monthly
fee compared to users without partners.
Section III: Data Visualisation (10%)
You need to use either Microsoft Excel or Azure to visualise Data.csv. You need to put the result graphs
in your report.
1. Use the pie chart to indicate the composition of customer contract types (i.e. showing the
percentage of users who choose certain type contract) (2%).
2. Use the histogram graph to display the distribution of customer usage length (3%)
3. Use the PivotTable + histogram graph to indicate the churn rate of users with different usage
length. It should look like the below figure (5%).
NOTE: all of the above visualisations have been covered in the lectures. Before creating one, think
what it visualises and what variables/features you will need to create it.
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Section IV: Customer Churn Prediction (60%)
You need to use Microsoft Azure to develop (i.e., train and test) two data-driven models for predicting
the customer churn. The first model is the logistic regression and the second model is the neural
network model. The target variable is Churn and the evaluation metric is accuracy. You need to
answer the following questions:
1. Could you describe and explain both models (i.e., methodology) in a short paragraph (5% for
each model)?
2. Could you describe and explain the key stages of the process of how do you carry out both
models (5% for each model)?
Could you discuss in detail and summarise the marketing insights provided by both models (40%)?
For example, which variables/features are important in prediction? What types of customers are
more likely to churn? What do you think might be the reasons behind the findings? Your analysis
needs to be reasonable, and you could include some marketing theories or evidence (in other
related cases from referenced sources) to justify your statements along with the empirical findings
from the provided data.
Assessment criterion reference
This criterion reference is comply with the University of Glasgow’s Guide to the Code of Assessment
(for details, please find the link: https://www.gla.ac.uk/media/media_124293_en.pdf) and the
course result will be calculated following Schedule A.
Learning outcome Section Question
1. Clearly understand the types of
analytics used in marketing and
the impact of data-driven decision
making.
2. Effectively access and process
various types of marketing data.
3. Appropriately summarise and
visualise marketing data.
4. Critically interpret and appraise
regression, classification, and
clustering models for marketing
analytics.
5. Effectively use software tools to
obtain and present analytical
results from data science models.
Section I: Data Loading,
Pre-Processing, and
Summary (15%)
• Question 1 (5%)
• Question 2 (5%)
• Question 3 (5%)
Section II: Preliminary
Analysis (15%)
• Question 1 (3%)
• Question 2 (3%)
• Question 3 (3%)
• Question 4 (3%)
• Question 5 (3%)
Section III: Data
Visualisation (10%)
• Question 1 (2%)
• Question 2 (3%)
• Question 3 (5%)
Section IV: Customer
Churn Prediction (60%)
• Question 1 (10%)
• Question 2 (10%)
• Question 3 (40%)
Grade Gloss Grade point Descriptors for attainment of intended learning outcomes
A Excellent 18-22 You have clearly described the data and models to an excellent
standard, carried our substantial and key steps with extensive
explanation, and provided the plots as required to a great
standard. Your discussion and presentations are detailed, indepth, and offer a critique of the steps undertaken. A significant
amount of the discussion is related to the key information
obtained from the processes. You have demonstrated an
excellent understanding of how to use data science models and
excellent critical thinking and analysis for marketing analytics.
B Very Good 15-17 You have clearly described the data and models to a good
standard, carried our substantial and key steps with extensive
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explanation, and provided the plots to a good standard. Your
discussion and presentations are detailed and offer a critique of
the steps undertaken. A significant amount of the discussion is
related to the key information obtained from the processes. You
have demonstrated a good understanding of how to use data
science models and good critical thinking and analysis for
marketing analytics.
C Good 12-14 You have provided a detailed description of the data or models,
carried out some data pre-processing steps, and provided several
plots and screen shot evidences as required. Your discussion and
presentations are brief with little critique.
D
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Satisfactory 9-11 You have provided a basic description of the data or models,
carried out some data pre-processing steps, and provided one of
the plots. Your discussion and presentations are brief with little
critique.
E Weak 6-8 You have not provided convincing evidence for attainment in
F Poor 3-5 respect of specific intended learning outcomes of the course.
G Very Poor 1-2
H 0
Preparing your coursework
Document creation
1. Please use this file naming convention: CourseCode_StudentID, e.g., MGT5372_7299019.
2. The file type must be in PDF.
3. Include your student ID in your document, ideally in the header on each page with the
course code, e.g., MGT5372_7299019.
4. The maximum file size limit on Moodle is 200MB
Formatting
You won’t be penalised if you don’t follow this good practice on formatting, but it will help your
markers.
• Use a Sans Serif font in black, e.g., Arial, Avant Garde, Calibri, Helvetica and Geneva.
• Use font size 12.
• Use 1.5 or double line spacing.
• Align your text to the left margin.
• Add page numbers.
Referencing and bibliography
You should reference your sources appropriately and list these in a bibliography. The bibliography is
excluded from your word limit. You should use the ‘Harvard’ referencing system, as detailed below
for written coursework.
In the text, use the following referencing conventions:
• Smith (1999) argues that…. or
• It has been argued that……. (Smith, 1999).
• If you use a direct quote, use quotation marks and cite the page number as well as the
author and date, i.e. (Smith, 1999, p. 4).
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Postgraduate students should be aware that on most programmes an average above D in taught courses is
required for progress to the dissertation at Masters level. Students should consult the appropriate degree
regulations and course handbooks for the grades they require to progress to specific awards. (Source:
https://www.gla.ac.uk/media/media_124293_en.pdf)
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• If you have two items by the same author in the same year, refer to one as ‘a’ and the other
as ‘b’, i.e. Smith (1999a) and Smith (1999b).
For more information, please refer to the University Library webpage.
Student conduct
Plagiarism
You must adhere to the University’s rules regarding plagiarism which are based on the premise
that ‘all work submitted by students for assessment is accepted on the understanding that it is the
student’s own effort’. More specifically, you must avoid plagiarism in the following forms:
• Copying from sources without ‘formal and proper acknowledgement’
• Inappropriate collaboration – working with other students to produce individual coursework
or copying work produced by another student
• Submitting work which you have obtained from another source, e.g. an essay mill
• Self-plagiarism – basing coursework on work that has already been submitted for
assessment purposes.
For advice and more information, please consult:
• LEADS web pages
• University Plagiarism Statement
Turnitin
Note that your coursework will be processed through Turnitin for similarity checking. You can
submit a draft of your coursework to Turnitin before submitting your final copy. You will find
information about using Turnitin in the Student Information Point Moodle.
Submitting your coursework
You must submit in accordance with the stated time and date on page 1. See below for
information if you are unable to do so.
Finalising your document
Please follow the steps listed below:
1. Check your spelling and grammar using the inbuilt tool on your device. You will not be
penalised for grammatical and spelling errors but we recommend that you take the
opportunity to correct them.
2. Check your file name (see above).
3. Check that you have used an accepted file type (see above).
4. Do not include your name in the file name or the document to support anonymous marking.
Uploading your document to Moodle
1. You will upload your document to the designated section of the Moodle course, which will
be clearly signposted.
2. Try to upload your document at least 30 minutes before the deadline (page 1) in case you
encounter any technical issues. You will be able to resubmit the document as often as you
like until the submission deadline.
3. Complete the Declaration of Originality (see below).
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Declaration of originality
When you upload your coursework on Moodle, you will be required to select a checkbox to confirm
that you agree with the University’s Declaration of Originality which applies to all academic work, as
follows.
I confirm that this assignment is my own work and I have:
• Read and understood the guidance on plagiarism provided on the Student Information Point
Moodle course including the University of Glasgow Statement on Plagiarism.
• Clearly referenced, in both the text and the bibliography or references, all sources used in
the work.
• Fully referenced (including page numbers) and used inverted commas for all text quoted
from books, journals, web etc.
• Provided the sources for all tables, figures, data etc. that are not my own work.
• Not made use of the work of any other student(s) past or present without
acknowledgement. This includes any of my own work, that has previously, or concurrently,
been submitted for assessment, either at this or any other institution, including school.
• Not sought or used the services of any professional agencies to produce this work.
• In addition, I understand that any false claim in respect of this work will result in disciplinary
action in accordance with University regulations.
Extensions and non-submission with good cause
Please refer to the Student Information Point Moodle for relevant information.
Late submission penalties
In the absence of good cause, late submission penalties will be applied as explained in Student
Information Point Moodle.