Subject to the External Examiner’s approval and may be subject to change
LUBS5403M
This question paper consists of 4 printed pages, each is identified by LUBSM5403
© UNIVERSITY OF LEEDS (Semester Jan-May, 2020/2021)
Assessed Coursework LUBS5403M Marketing Analytics 100% Assignment
Background:
Crafty Chocolates (pseudonyms for confidentiality) is a premium chocolate manufacturer. As a marketing analytics specialist, you are approached by them for a marketing consultancy. You are asked to develop a marketing report, providing them with useful recommendations for increasing their marketing performance. They have conducted some primary data collection and secured some secondary data for you. The following datasets are provided:
1_demongraphics.csv
A survey data about the demographics of existing and potential consumers.
2_chocolate_rating.csv
A survey data from a customer panel about ratings on major chocolate brands and their chocolate products, along with key attributes about the chocolate products.
3_ sustainable_consumption.csv
A choice data from a sample of existing and potential consumers about their demographics and whether they have ever purchased sustainable labelled products.
4_purchase_history.csv
Historical data from consumers making orders online directly from the company’s website, about the chocolate purchase history up to the end of June 2020 of the cohort of 500 consumers.
5_conjoint.csv
A choice-based conjoint data from the same respondents in “1_demongraphics.csv” about their choice on newly developed chocolate products.
6_groceries.dat
A transaction history data from a major supermarket partner about consumers shopping basket
7_advertising.csv
Historical data from the company about previous advertising spending on different platforms and monthly sales
8_clickstream.csv
An online experiment data about two versions of ads displayed on a video-based social media platform (e.g. TikTok). Ads version A focuses on the subjective hedonic benefit of the sustainable- labelled chocolates; ads version B focuses on the objective quality of the sustainable-labelled chocolates.
Variable details in each dataset are provided in the related readme file.
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Task:
You are asked to conduct marketing analytics by applying appropriate analysis tools (using R) to the provided datasets and to develop a marketing consultancy report based on your analysis results. You can use part or all of those data files. In addition to analysing the provided datasets, you are required to provide suggestions on future marketing analysis plan.
Guidance for your task:
Your report should cover the following contents:
Introduction (5%):
Briefly introduce the report, including the business context and background.
Main body (75%):
This is based on data analysis and should address the THREE out of the following four main marketing themes (equally weighted):
Managing customer heterogeneity,
Managing customer dynamics,
Managing sustainable competitive advantage, and
Managing resource trade-offs.
For each theme, you can follow the following structure:
Problem define and model specification: identify the useful analysis tools and the datasets;
and describe your model specification.
Results: present the results using appropriate tables and figures; and interpret the results.
Discussion: summarize key findings and implications; reflect critically on the validity of your
proposed model.
Future works (20%):
This is the part you make suggestions on future marketing analysis plan. Suggestions should include what other data could be collected and how, what models could be used to analyse the data, and what insights could be obtained.
Assignments should be a maximum of 3,000 words in length. R codes should be provided in separate R files (.R) or in Appendix.
All coursework assignments that contribute to the assessment of a module are subject to a word limit, as specified in the online module handbook in the relevant module area of the MINERVA. The word limit is an extremely important aspect of good academic practice, and must be adhered to. Unless stated specifically otherwise in the relevant module handbook, the word count includes EVERYTHING (i.e. all text in the main body of the assignment including summaries, subtitles, contents pages, tables, supportive material whether in footnotes or in-text references) except the main title, reference list and/or bibliography and any appendices. It is not acceptable to present matters of substance, which should be included in the main body of the text, in the appendices (“appendix abuse”). It is not acceptable to attempt to hide words in graphs and diagrams; only text which is strictly necessary should be included in graphs and diagrams.
You are required to adhere to the word limit specified and state an accurate word count on the cover page of your assignment brief. Your declared word count must be accurate, and should not mislead. Making a fraudulent statement concerning the work submitted for assessment could be considered academic malpractice and investigated as such. If the amount of work submitted is higher than that
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specified by the word limit or that declared on your word count, this may be reflected in the mark awarded and noted through individual feedback given to you.
The deadline date for this assignment is 12:00:00 noon on Wednesday 24 March 2021.
An electronic copy of the assignment must be submitted to the Assignment Submission area within the module resource on the Blackboard MINERVA website no later than 12:00:00 noon prompt on the deadline date.
Faxed, emailed or hard copies of the assignment will not be accepted.
Failure to meet this initial deadline will result in a reduction of marks, details of which can be found at the following place:
https://lubswww.leeds.ac.uk/TSG/coursework/
SUBMISSION
Please ensure that you leave sufficient time to complete the online submission process, as upload times can vary. Accessing the submission link before the deadline does NOT constitute completion of submission. You MUST click the ‘CONFIRM’ button before 12:00:00 noon for your assignment to be classed as submitted on time, if not you will need to submit to the Late Area and your assignment will be marked as late. It is your responsibility to ensure you upload the correct file to the MINERVA, and that it has uploaded successfully.
It is important that any file submitted follows the conventions stated below: FILE NAME
The name of the file that you upload must be your student ID only.
ASSIGNMENT TITLE
During the submission process the system will ask you to enter the title of your submission. This should also be your student ID only.
FRONT COVER
The first page of your assignment should always be the Assessed Coursework Coversheet (individual), which is available to download from the following location: https://students.business.leeds.ac.uk/forms-guidance-and-coversheets/
STUDENT NAME
You should NOT include your name anywhere on your assignment
END
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Appendix: LUBS5403M Marketing Analytics Assignment Marking Criteria
Poor
Adequate
Good
Excellent
Strength of argumentation (e.g. clear motivation for decisions, consistency of argumentation)
Depth of analysis (e.g. appropriate use of data and analysis tools to make strategic decisions)
Critical reflection (e.g. ability to critically reflect on information)
Communication(e.g. professional, engaging report, well structured, good use of tables and figures)
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