MIS784 – Marketing Analytics – Trimester 3 2024 Assessment Task 2 – Transaction Analysis – Individual
DUE DATE: Friday, 10th January 2025, by 8:00pm (Melbourne time) PERCENTAGE OF FINAL GRADE: 30%
Submission: You will submit to unit site:
– one Word file, with your analysis queries, and
– one Word file, with your written report: Word count: 2000
words (+/- 10%)
Description
The assignment requires that you analyse a data set, interpret, and draw conclusions from your analysis, and then convey your conclusions in a written report. The assignment must be completed individually and must be submitted electronically in CloudDeakin by the due date. When submitting electronically, you must check that you have submitted the work correctly by following the instructions provided in CloudDeakin. Hard copies or assignments submitted via email will NOT be accepted.
The assignment uses datasets which can be downloaded from CloudDeakin. The assignment focuses on materials presented up to and including Week 7. Following is an introduction to this scenario and detailed guidelines.
Context/Scenario:
This trimester, although we have real data, the client organization cannot be identified for privacy reasons. In this unit, we will refer to the client organization as “MarketCo.” MarketCo is a globally recognized omnichannel retailer, renowned for its ability to sell products from worldwide to worldwide. Leveraging advanced technology, MarketCo ensures seamless operations and exceptional customer experiences across multiple sales channels.
MarketCo’s primary goal is to deliver high-quality, personalized shopping experiences, catering to a diverse global audience while generating revenue through various streams. The organization excels in providing a seamless, engaging, and timely shopping journey, whether customers are purchasing online, in-store, or through other integrated channels.
With a vast product portfolio spanning electronics, fashion, home goods, and groceries, MarketCo is adept at meeting the varied needs of its international customer base. The company’s robust infrastructure enables it to trigger and manage multiple channels efficiently, ensuring a consistent and
comprehensive shopping experience. Beyond its primary retail offerings, MarketCo also facilitates sales for other organizations, earning commissions that are recorded in contract tables, thereby diversifying its revenue streams.
Following a successful collaboration with MarketCo in the previous round, MarketCo has hired you again as the marketing data analyst to gain insights into customer behaviours, manage subscriptions, and boost online sales commissions in their Australian market. Please answer the following questions raised by your shareholders and provide a report to assist them in understanding your analysis and providing suggestions. The questions are accompanied by guidelines highlighted in blue. You are required to submit your analysis file, along with a report that explains the outcomes of your analysis and two recommendations. Given that your audience may not have training in marketing analytics, your report must present the results in plain, straightforward language.
After a recent update to its online platform, MarketCo is focused on improving the website’s performance by addressing issues that might be affecting user experience and engagement. They’ve noticed some pages have high bounce rates and low conversion rates, which could mean that users aren’t finding what they need, or the experience isn’t meeting their expectations. MarketCo is also curious about how special days, like Valentine’s Day and Mother’s Day, impact user behaviour, especially how traffic changes during these times. Additionally, they want to explore how different operating systems and browsers affect user experience so they can prioritize which mobile versions to improve.
1. Are there differences in the behaviour of customers who have subscribed to the Gold Membership compared to those who have not? Merge the relevant data files and compare the behaviours of Gold Members versus non-members. You can consider but not limited in the following ways: purchase frequency, total spending, average spending, and communication patterns (both inbound and outbound). There are no set answers so feel free to explore the data in your own way to highlight any significant differences in customer behaviours. Calculate Customer gamification behaviours based on the in -page performance.
2. What are the patterns of customer behaviour on MarketCo? Analyse the dataset to identify patterns among different customer groups, such as age, tenure, state and gender. For example, you can focus on how different age groups of customers differ in terms of communication frequency (both inbound and outbound) and technology preferences (operating systems and browser they use). You may also explore other ways to calculate and identify patterns based on your understanding of the dataset. Calculate Customer gamification behaviours based on the in -page performance.
3. Is there any important data absent from the current dataset that could improve the analysis? Consider whether there are any gaps in the current dataset that might hinder your
analysis. Suggest additional variables or data sources that could provide deeper insights. Explain how this new data could enhance your analysis and the overall findings. Calculate Customer gamification behaviours based on the in -page performance.
4. Write an analytical report for Market Co that will assist them in making better business decisions.
o Discuss customer behaviour patterns and identify opportunities for enhancing engagement and increasing sales based on insights gained from the previous analyses.
o Based on your findings, highlight potential marketing challenges and suggest future directions for Market Co.
o Offer practical, data-driven recommendations for improving customer engagement and sales, leveraging the marketing analytics strategies discussed earlier. If these strategies are not suitable, propose alternative solutions and explain why they would be effective.
o Discuss how Market Co could benefit from implementing the marketing analytics strategies covered in the previous sections or suggest new approaches if needed.
Data description
Market Co collects data from members interacting on its website. The provided data is contained in two data files named: Customer.csv and Contract.csv. These files include various types of information crucial for analysing customer behaviour and sales performance.
The Customer_AT2_T3.csv file includes information about the customers (members), such as their personal details and interaction history with Market Co. The Contract_AT2_T3.csv file contains information about the amount, the products, and the timing of each sale. The variable Cost_ID is linking customer and contract information.
Contained in the two files are two basic data types:
1. Customer_AT2_T3: This includes information about the customers (members), such as their personal details, preferences, and interaction history.
2. Contract_AT2_T3: This includes information about the amount, the products, and the timing of each sale.
Customer_AT2_T3.csv variables:
• Cost_ID: Unique identifier for each customer.
• Affluence_Level: Customer’s income bracket.
• Tenure: Duration of the customer’s membership, measured in months.
• Age: Age of the customer.
• Gender: Gender of the customer. N/A for not disclosing.
• State: State where the customer resides.
• Has_Valid_Email_Address: Indicates if the customer has a valid email address.
• Has_Valid_Mobile_Number: Indicates if the customer has a valid mobile number.
• Number_Of_Inbound_Communication_In_24_MTH: Number of communications received from the customer in the past 24 months.
• Number_Of_Inbound_Communication_In_60_MTH: Number of communications received from the customer in the past 60 months.
• Number_Of_Outbound_Communication_In_24_MTH: Number of communications sent to the customer in the past 24 months.
• Number_Of_Outbound_Communication_In_60_MTH: Number of communications sent to the customer in the past 60 months.
• Pathway_Arrears_In_24_MTH: Indicates if a payment is rejected in the past 24 months. • Pathway_Arrears_In_60_MTH: Indicates if a payment is rejected in the past 60 months.
• Inter_Mailing_Preferances_Update: Indicates the number of times the customer has updated their mailing preferences.
• Inter_Personal_Details_Update: Indicates the number of times the customer has updated their personal details.
• Operating_Systems: The operating system used by the customer.1 = Windows; 2 = macOS; 3 = Linux; 4 = Android.
• Browser_type: The browser used by the customer. 1 = Google Chrome; 2 = Mozilla Firefox; 3 = Apple Safari; 4= Microsoft Edge; 5= Opera.
Contract_AT2_T3.csv Variables:
• Cost_ID: Unique identifier linking to the customer.
• Contract_ID: Unique identifier for each contract.
• Is_Initial_Commitment: Indicates if this is the first payment from the customer. • Product: Type of product or service associated with the contract.
o Gold Membership: This is considered a monthly regular payment. All other products are one-off purchases.
o Sport Products: This includes a commission which is 15 AUD per sale. It also includes donations that members pay as extra to their membership in response to a financial ask (marketing campaign).
o Travel Commission: This is a commission from sales of any travel plan, including national and international tours. The collected commission is recorded in the Total Paid column. The commission is calculated through a complex equation, and members pay whenever they want, regardless of an ask.
o Cruise Commission: This is a commission from sales of any cruise ticket and is correlated to the total ticket cost.
o Hotel Referral Commission: This is a commission from sales of any accommodation and is correlated to the total sales amount.
• Total_Paid: Total amount paid for the contract.
• Fulfilment_Date: Date of receiving the first payment (start of the contract).
• Fulfilment_Ended_Date: Date the last payment was received (end of the contract for Gold Membership). Null means this is still an active membership.
The dataset you will be working with in this assignment is compiled from real interactions on Market Co’s website, offering authentic data and insights directly relevant to the operations of a modern news agency. It is specifically curated by the MIS784 team at Deakin Business School to be used for educational purposes in the Marketing Analytics unit.
Assignment instructions
The assignment consists of two parts.
Part 1: Data Analysis
Your data analysis must be performed on the provided data files: Customer_AT2_T3.csv and Contract_AT2_T3.csv.
When conducting the analysis, you need to apply techniques from marketing analytics. The analysis section you submit should be clearly labelled and grouped around each question. Poorly presented, unorganised analysis or excessive output will be penalised.
Part 2: Report
Having analysed the data, you are required to provide a formal analytical report. Given that your audience may not have training in marketing analytics, your report must present the results in plain, straightforward language. The audience will only be familiar with broad, generally understood terms
(e.g., Average, Correlation, Causality). They will need you to explain more technical terms, such as Web analytics, HTML, Cookies, Segmentation etc.
In Section 1 of the report, provide a brief interpretation of your findings from the data analysis (e.g., the patterns and results you find from Q1 and Q2). In Section 2 of the report, provide recommendations that could help Market Co enhance customer behaviour understanding and long-term performance by showing your thoughts gained from Q3 and Q4. Your report and recommendations should be based on the analysis conducted in this assignment and any additional relevant analysis that enhances the impact of your recommendations. Ensure that all recommendations are directly informed by your data analysis. Avoid including any commentary not supported by your data analysis. Highest marks will be awarded to students who draft distinct recommendations, and whose recommendations take into account a broad range of data-supported considerations.
When exploring data, we often produce more results than we eventually use in the final report, but by investigating the data from different angles, we can develop a much deeper understanding of the data. This will be valuable when drafting your written report.
You are allowed approximately 2,000 words (1,800 to 2,200 words) for your report. Remember you should use font size 11 and leave margins of 2.54 cm.
Carefully consider the following points when writing your report:
• Your report is to be written as a stand-alone document.
• Keep the English simple and the explanations clear. Avoid the use of technical statistical jargon. Your task is to convert your analysis into plain, simple, easy to understand language.
• Marks will be deducted for the inclusion of irrelevant material, poor presentation, poor organisation, poor formatting, and reports that exceed the word limit.
When you have completed drafting your report, it is a useful exercise to leave it for a day, and then return to it and re-read it as if you knew nothing about the analysis. Does it flow easily? Does it make sense? Can someone without prior knowledge follow your written conclusions? Often when re reading, you become aware that you can edit the report to make it more direct and clearer.
Learning Outcomes
This task allows you to demonstrate your achievement towards the Unit Learning Outcomes (ULOs) which have been aligned to the Deakin Graduate Learning Outcomes (GLOs). Deakin GLOs describe the knowledge and capabilities graduates acquire and can demonstrate on completion of their course. This assessment task is an important tool in determining your achievement of the ULOs. If you do not demonstrate achievement of the ULOs you will not be successful in this unit. You are advised to
familiarise yourself with these ULOs and GLOs as they will inform you on what you are expected to demonstrate for successful completion of this unit.
The learning outcomes that are aligned to this assessment task are:
|
Unit Learning Outcomes (ULO) |
Graduate Learning Outcomes (GLO) |
|
ULO1: Explain marketing analytics concepts and methodologies. |
GLO1: Discipline-specific knowledge and capabilities |
|
ULO2: Analyse real-world marketing problems and propose appropriate marketing analytic solutions. |
GLO1: Discipline-specific knowledge and capabilities GLO5: Problem solving |
|
ULO3: Deploy marketing analytic solutions using a contemporary analysis tool. |
GLO1: Discipline-specific knowledge and capabilities GLO3: Digital literacy |
|
ULO4: Prepare written reports that effectively communicate your solution to marketing problems. |
GLO2: Communication |
Submission
You must submit your assignment in the Assignment Dropbox in the unit Cloud Deakin site on or before the due date.
Your submission will comprise of two files:
1. A Microsoft Word document containing your analysis queries, and
2. A Microsoft Word document containing your report (Part 2).
When uploading your assignment, your submission files should be named:
Word file 1: MIS784_AT2_YOURStudentID_Query.doc (or .docx), and
Word file 2: MIS784_AT2_YOURStudentID_Report.doc (or .docx).
Submitting a hard copy of this assignment is not required. You must keep a backup copy of every assignment you submit until the marked assignment has been returned to you. In the unlikely event that one of your assignments is misplaced you will need to submit your backup copy.
Any work you submit may be checked by electronic or other means for the purposes of detecting collusion and/or plagiarism and for authenticating work.
When you submit an assignment through your Cloud Deakin unit site, you will receive an email to your Deakin email address confirming that it has been submitted. You should check that you can see your assignment in the Submissions view of the Assignment Dropbox folder after upload and check for, and keep, the email receipt for the submission.
Marking and feedback
The marking rubric indicates the assessment criteria for this task. It is available in the CloudDeakin unit site in the Assessment folder, under Assessment Resources. Criteria act as a boundary around the task and help specify what assessors are looking for in your submission. The criteria are drawn from the ULOs and align with the GLOs. You should familiarise yourself with the assessment criteria before completing and submitting this task.
Students who submit their work by the due date will receive their marks and feedback on Cloud Deakin 15 working days after the submission date.
Extensions
Extensions can only be granted for exceptional and/or unavoidable circumstances outside of your control. Requests for extensions must be made by 12 noon on the submission date using the online Extension Request form under the Assessment tab on the unit Cloud Deakin site. All requests for extensions should be supported by appropriate evidence (e.g., a medical certificate in the case of ill health).
Applications for extensions after 12 noon on the submission date require University level special consideration and these applications must be submitted via Student Connect in your Deakin Sync site.
Late submission penalties
If you submit an assessment task after the due date without an approved extension or special consideration, 5% will be deducted from the available marks for each day after the due date up to seven days*. Work submitted more than seven days after the due date will not be marked and will receive 0% for the task. The Unit Chair may refuse to accept a late submission where it is unreasonable or impracticable to assess the task after the due date. *’Day’ means calendar day for electronic submissions and working day for paper submissions.
An example of how the calculation of the late penalty based on an assignment being due on a Thursday at 8:00pm is as follows:
• 1 day late: submitted after Thursday 11:59 pm and before Friday 11:59 pm– 5% penalty.
• 2 days late: submitted after Friday 11:59 pm and before Saturday 11:59pm – 10% penalty.
• 3 days late: submitted after Saturday 11:59 pm and before Sunday 11:59pm – 15% penalty.
• 4 days late: submitted after Sunday 11:59 pm and before Monday 11:59 pm – 20% penalty.
• 5 days late: submitted after Monday 11:59 pm and before Tuesday 11:59 pm – 25% penalty.
• 6 days late: submitted after Tuesday 11:59 pm and before Wednesday 11:59 pm – 30% penalty.
• 7 days late: submitted after Wednesday 11:59 pm and before Thursday 11:59 pm – 35% penalty.
The Dropbox closes the Thursday after 11:59 pm AEST/AEDT time.
Support
The Division of Student Life provides a range of Study Support resources and services, available throughout the academic year, including Writing Mentor and Maths Mentor online drop ins and the Smart Thinking 24 hour writing feedback service at this link. If you would prefer some more in depth and tailored support, make an appointment online with a Language and Learning Adviser.
Referencing and Academic Integrity
Deakin takes academic integrity very seriously. It is important that you (and if a group task, your group) complete your own work in every assessment task Any material used in this assignment that is not your original work must be acknowledged as such and appropriately referenced. You can find information about referencing (and avoiding breaching academic integrity) and other study support resources at the following website:
Your rights and responsibilities as a student
As a student you have both rights and responsibilities. Please refer to the document Your rights and responsibilities as a student in the Unit Guide & Information section in the Content area in the Cloud Deakin unit site.
