IB9640
Marketing Analytics
Term Two 2019-2020 WARWICK BUSINESS SCHOOL
80% Individual Assignment
2500 words:
This is a strict limit not a guideline: any piece submitted with more words than the limit will result in the excess not being marked
Question 1: Marketing Analytics for Marketing Analytics (30%)
As the module leader (and thus the “product manager”) of IB9640, my objectives are to ensure that my students achieve the module’s learning objectives while having an enjoyable learning experience.
Please explain in detail how I could implement
1. descriptive analytics
2. diagnostic analytics
3. predictive analytics
4. prescriptive analytics
to make four different marketing decisions regarding next year’s module, each decision relating to a different marketing mix element (product, price, place, promotion, people, processes, physical evidence).
Out of the four options, please make a justified recommendation which would be the most valuable and which would be the least valuable analytics project for me to implement.
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Question 2: Evaluating an Analytics Project (20%)
A wholesaler of paint that serves 20,000 customers wants to reduce customer churn. Therefore, Phil, the company’s data scientist, aims to analyze why customers churned in the past. Phil compiles a dataset of all 3,000 customers that churned once in the past five years. Phil then runs a linear regression analysis on these 3,000 customers with customer churn as the dependent variable and the following independent variables:
• Revenue with the customer in the year before the churn event (collected from the wholesaler’s databases).
• Profit with the customer in the year before the churn event. Seeing that profit on the level of individual customers is not available in the databases, Phil calculates profit from revenue using the wholesaler’s average return on sales of 30%, (i.e., profit = 30% * revenue).
• Number of times the salesperson visited the customer in the year of the churn event.
Phil finds a significant positive relationship between the number of visits and the churn variable. He tells the wholesaler’s CEO: “My predictive analytics model shows that visiting customers often drives them away. Therefore, we need to tell our salespeople to restrict their customer visits. This will improve our customer retention.”
Please critique Phil’s approach and explain in detail what he should have done differently.
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Question 3: Customer Satisfaction Analytics (50%)
An airline would like to improve its customer satisfaction. To this end, the airline has collected answers of 1,000 customers on the following ten survey questions:
Variable
Survey question
Scale
V1
I am very satisfied with this airline.
1 (“Fully disagree”) to 7 (“Fully agree”)
V2
The onboard personnel smiled a lot.
1 (“Fully disagree”) to 7 (“Fully agree”)
V3
I liked the selection of food.
1 (“Fully disagree”) to 7 (“Fully agree”)
V4
The onboard personnel were very competent.
1 (“Fully disagree”) to 7 (“Fully agree”)
V5
I am very satisfied with the onboard personnel.
1 (“Fully disagree”) to 7 (“Fully agree”)
V6
I liked the selection of drinks.
1 (“Fully disagree”) to 7 (“Fully agree”)
V7
The onboard personnel were very friendly.
1 (“Fully disagree”) to 7 (“Fully agree”)
V8
I am very satisfied with the catering.
1 (“Fully disagree”) to 7 (“Fully agree”)
V9
The onboard personnel were fast.
1 (“Fully disagree”) to 7 (“Fully agree”)
V10
The flight was on time.
1 (“Fully disagree”) to 7 (“Fully agree”)
What should the airline do to improve customer satisfaction (V1)? Please analyze the dataset (IB9640 Individual Assignment – Dataset Question3.xlsx) and produce a written management summary of your recommendations, followed by a methodological appendix that clearly explains, justifies, and details all steps of your analysis including R codes used.
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General Notes
• You are NOT allowed to work in groups, but you have to write your answers
individually.
• The upper word limit is 2,500 words. This is a fixed limit. Material over the word limit will not be read or marked. Please adhere to WBS word count policy.
• The work submitted must be original and all works and sources used within the assignment must be correctly cited and referenced. Failure to reference correctly will be subject to University and WBS rules on poor scholarship and/or plagiarism. You must adopt the Harvard referencing style.
• Formatting should be in line with WBS policy (A4, 2.54 cm margins, 11pt Arial, 1.5 lines spacing.
• Your submission should include a title page with the following (and ONLY the following) information:
o The module code and module name (i.e., IB9640 Marketing Analytics);
o Your student ID number. Do not include your name anywhere in your work;
o A statement regarding the originality of the work and acknowledgment of the University’s rules on plagiarism to wit: “All the work contained within this assignment is my own unaided and original effort and conforms to the University’s guidelines on plagiarism.”
• Illustrations (diagrams, tables, graphs, etc.) are not only permitted but encouraged, however, make sure that they are clear and self-explanatory.
• Appendices must be used with care, must be appropriate, and must be referred to within the body of your work. Although excluded from the word limit, they cannot be used to circumvent the word limit.
• You should submit your assignment by the date and time stated within the module outline and/or as specified in the my.wbs notices for the module. Late submissions are subject to penalties.
• Consult my.wbs for the WBS postgraduate marking criteria.
• Any questions concerning this assignment should be directed to Dr. Johannes Habel
via e-mail (johannes.habel@wbs.ac.uk).
GOOD LUCK!
Dr. Johannes Habel IB9640 Marketing Analytics
SUBMISSION DEADLINE: 12:00 (noon, UK time) Thursday 9th April 2020
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Word Count Policy and Formatting (found in your Masters Student Handbook) https://my.wbs.ac.uk/-/academic/37360/resources/in/381545,786874/item/786880/
The submission deadline is precise and uploading of the document must be completed before 12.00 (UK time) on the submission date. Any document submitted even seconds later than 12.00 precisely will be penalised for late submission in line with WBS policy. Please consult your student handbook on my.wbs for more detailed information.
The online assignment submission system will only accept documents in portable documents format (PDF) files. Please note that we will not accept PDF files of scanned documents. You should create your assignment in your chosen package (for example, Word), then convert it straight to PDF before uploading. Please place your student ID number, NOT YOUR NAME, on the front of your submission as all submissions are marked anonymously.
All the scripts should also have the following paragraph included on the front page:
PLEASE ENSURE YOU KEEP A SECURITY COPY OF YOUR ASSESSMENT
Please ensure that any work submitted by you for assessment has been correctly referenced as WBS expects all students to demonstrate the highest standards of academic integrity at all times and treats all cases of poor academic practice and suspected plagiarism very seriously. You can find information on these matters on my.wbs, in your student handbook and on the University’s library web pages here.
The University’s Regulation 11 clarifies that ‘…’cheating’ means an attempt to benefit oneself or another by deceit or fraud. This includes reproducing one’s own work…’ It is important to note that it is not permissible to re-use work which has already been submitted by you for credit either at WBS or at another institution (unless you have been explicitly told that you can do so). This is considered self-plagiarism and could result in significant mark reductions.
Upon submission of assignments, students will be asked to agree to one of the following declarations:
Individual work submissions:
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By agreeing to these declarations (when the message pops up on submission) you are acknowledging that you have understood the rules about plagiarism and self-plagiarism and have taken all possible steps to ensure that your work complies with the requirements of WBS and the University.
You should only indicate your agreement with the relevant statement, once you have satisfied yourself that you have fully understood its implications. If you are in any doubt, you must consult with the Module Organiser or Named Internal Examiner of the relevant module, because, once you have indicated your agreement, it will not be possible to later claim that you were unaware of these requirements in the event that your work is subsequently found to be problematic in respect to suspected plagiarism or self- plagiarism.
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