SEMESTER 2 2023/24
COURSEWORK BRIEF:
Module Code: MANG6260 Assessment: Individual Coursework Weighting: 70%
Module Title: Marketing Data Science
Module Leader: Iain Brown
Submission Due Date: @ 16:00 31/05/24 Word Count: 2500
Electronic via Blackboard Turnitin ONLY
Method of Submission:
(Please ensure that your name does not appear on any part of your work)
Any submitted after 16:00 on the deadline date will be subject to the standard University late penalties (see below),
unless an extension has been granted, in writing by the Senior Tutor, in advance of the deadline.
University Working Days Late: Mark:
1 (final agreed mark) * 0.9
2 (final agreed mark) * 0.8
3 (final agreed mark) * 0.7
4 (final agreed mark) * 0.6
5 (final agreed mark) * 0.5
More than 5 0
This assessment relates to the following module learning outcomes:
A. Knowledge and A1. The different types of marketing analytics activities involved advanced analytical techniques in
Understanding contemporary organisations;
A2. The complexities of collecting, integrating, processing and managing text and network data from a wide
range of internal and external sources;
A3. How various advanced analytical techniques can be used to uncover the potential of various types of data
to gain actionable insights and support marketing decisions.
B. Subject Specific B1. Select and apply suitable methods to collect data, and then integrate, prepare and manage these data;
Intellectual and B2. Critically analyse, interpret, organise and use visual tools to present quantitative data;
B3. Evaluate and apply advanced analytical techniques to solve Marketing Analytics problems, and then
Research Skills
reflect upon the selected approach;
B4. Derive actionable insights through the results of analyses and communicate them to a non-technical
audience.
C. Transferable and C1. Communicate ideas and arguments fluently and effectively in a variety of written formats;
Generic Skills C2. Communicate ideas and arguments orally and through formal presentations;
C3. Work effectively in a team and recognise problems associated with team working;
C4. Manage yourself, time and resources effectively;
C5. Use computing and IT resources effectively;
C6. Demonstrate confidence in your own ability to learn new concepts.
3
Coursework Brief:
Assignment (individual): Part B (70%): Advanced data analysis using SAS Enterprise Miner
This is an individual part of your assignment using any the datasets provided in part A (i.e. the group assignment). This
assignment requires you to analyse the data using advanced statistical techniques such as tree based algorithms (CART
/ Random Forests / Gradient Boosting), regression and neural networks.
Please review relevant literature before you begin, and give some thoughts to your research questions and decide your
relevant independent and dependent variables or when appropriate moderators and mediators.
Specifically, your tasks include:
1. Examine the dataset and carry out preliminary data analysis to ensure that the data fulfil statistical
assumptions prior to actual data analysis.
2. Identify latent factors and assess their reliability, which would help you determine latent constructs and their
relationship. This may involve the use of exploratory factor analysis and principal component analysis.
3. Develop a theoretically grounded conceptual model using your knowledge of marketing as a starting point for
theoretical reasons, and provide justification for model, such as hypothesized relationships in your conceptual
model.
4. Use relevant statistical techniques to check measurement model, and provide measure validation for the
hypothesized constructs and overall model.
5. Compute and estimate relationships of your model(s). This includes providing full explanation of SAS outputs
and carrying model evaluation when it is appropriate and be supported by theoretical reasoning (if
applicable).
SEMESTER 2 2023/24
Please prepare a technical report (2,500 words) and SAS code scripts / Enterprise Miner diagrams (include good
practice of using comment (/*Comment*/).
Submission details:
Firstly, you must electronically submit your written report to Blackboard (Turnitin) so that it can be checked for
plagiarism. Note that Turnitin will only accept MS Word or PDF documents.
Secondly, you need to submit all your files (including the data, SAS code scripts / Enterprise Miner diagrams and your
report) via dropoff. Open your browser and go to https://dropoff.soton.ac.uk/. Log in. Click the "Drop-off" button.
Check that your details are correct and click the "Next" button as you do not have a request code. On the next page,
check again that the email address in your "from" details is correct, leave the default options checked, and click on the
"+" button to specify where you would like to send the file to. Then enter the following details:
• Name: Brown, I
• Email: [email protected]
Make sure these are correct! Fill in your name, student ID and Turnitin submission code in the "Short note to the
Recipients" box (along with any optional instructions about what to do with your file if needed). Have a final check
everything is included. Click the "Drop off Files" button. Your file should then be uploaded to the system ready to be
picked up. You will get a confirmation later on when the file has been picked up.
As for your technical report, you need to explain the analyses conducted and show your ability to select, explain,
critically discuss, and evaluate the approaches taken and models chosen as well as the results obtained. This report will
complement SAS input (code scripts / diagrams) and SAS output files. The SAS code scripts / diagrams will not be
counted in the overall assignment wordcount. In brief, your report will include:
(a) The steps taken in the data analysis process
(b) Details of SAS procedures/nodes used
(c) Choice of analytical methods and implications
(d) Explanation for categorisation and manipulation of the data
(e) Basics of data management
(f) Theoretical and practical arguments for model specification
(g) Specification of the final model based on the data
(h) Model evaluation
(i) An appendix containing replicable SAS code and diagrams
It has to be comprehensive and thorough. Please use 12-point fonts for your report containing your SAS
codes/diagrams and relevant data files.
NB: If limitations exist with your approach, that is fine, if those limitations are recognised, they are reasonable and
their implications considered thoughtfully.
Nature of Assessment: This is a SUMMATIVE ASSESSMENT. See ‘Weighting’ section above for the percentage that this
assignment counts towards your final module mark.
Word Limit: +/-10% either side of the word count (see above) is deemed to be acceptable. Any text that exceeds an
additional 10% will not attract any marks. The relevant word count includes items such as cover page, executive
summary, title page, table of contents, tables, figures, in-text citations and section headings, if used. The relevant word
count excludes your list of references and any appendices at the end of your coursework submission.
You should always include the word count (from Microsoft Word, not Turnitin), at the end of your coursework
submission, before your list of references.
Title/Cover Page: You must include a title/ cover page that includes: your Student ID, Module Code, Assignment Title,
Word Count. This assignment will be marked anonymously, please ensure that your name does not appear on any part
of your assignment.
References: You should use the Harvard style to reference your assignment. The library provide guidance on how to
reference in the Harvard style and this is available from: http://library.soton.ac.uk/sash/referencing
Submission Deadline: Please note that the submission deadline for Southampton Business School is 16.00 for ALL
assessments.
SEMESTER 2 2023/24
Turnitin Submission: The assignment MUST be submitted electronically via Turnitin, which is accessed via the
individual module on Blackboard. Further guidance on submitting assignments is available on the Blackboard support
pages.
It is important that you allow enough time prior to the submission deadline to ensure your submission is processed
on time as all late submissions are subject to a late penalty. We would recommend you allow 30 minutes to upload
your work and check the submission has been processed and is correct. Please make sure you submit to the correct
assignment link.
Email submission receipts are not currently supported with Turnitin Feedback Studio LTI integrations, however
following a submission, students are presented with a banner within their assignment dashboard that provides a link
to download a submission receipt. You can also access your assignment dashboard at any time to download a copy of
the submission receipt using the receipt icon. It is vital that you make a note of your Submission ID (Digital Receipt
Number). This is a unique receipt number for your submission, and is proof of successful submission. You may be
required to provide this number at a later date. We recommend that you take a screenshot of this page, or note the
number down on a piece of paper.
The last submission prior to the deadline will be treated as the final submission and will be the copy that is
assessed by the marker.
It is your responsibility to ensure that the version received by the deadline is the final version, resubmissions after
the deadline will not be accepted in any circumstances.
Important: If you have any problems during the submission process you should contact ServiceLine immediately by
email at [email protected] or by phone on +44 (0)23 8059 5656.
Late Penalties: Further information on penalties for work submitted after the deadline can be found here.
Special Considerations: If you believe that illness or other circumstances have adversely affected your academic
performance, information regarding the regulations governing Special Considerations can be accessed via the
Governance and Policies landing pages: Regulations Governing Special Considerations (including Deadline
Extension Requests) for all Taught Programmes and Taught Assessed Components of Research Degrees
2023-24 | University of Southampton
Extension Requests: : Extension requests along with supporting evidence should be submitted to the Student Office
as soon as possible before the submission date. Information regarding the regulations governing extension requests
can be accessed via the Governance and Policies landing pages: Regulations Governing Special Considerations
(including Deadline Extension Requests) for all Taught Programmes and Taught Assessed Components of
Research Degrees 2023-24 | University of Southampton
Academic Integrity Policy: Please note that you can access Academic Integrity Guidance for Students via the Quality
Handbook: http://www.southampton.ac.uk/quality/assessment/academic_integrity.page?. Please note any
suspected cases of Academic Integrity will be notified to the Academic Integrity Officer for investigation.
Feedback: Southampton Business School is committed to providing feedback within 4 weeks (University working
days). Once the marks are released and you have received your feedback, you can meet with your Module Leader /
Module Lecturer / Personal Academic Tutor to discuss the feedback within 4 weeks from the release of marks date.
Any additional arrangements for feedback are listed in the Module Profile.
Student Support: Study skills and language support for Southampton Business School students is available at:
http://www.sbsaob.soton.ac.uk/study-skills-and-language-support/.
