Business Case:
Walmart (www.walmart.com) is one of the leading retailers in the world with no dedicated loyalty program (ignore the hybrid program). Although many retailing giants are leveraging the data on their customers, Walmart remains in the dark. Meanwhile, Walmart faces fierce competition amid Covid-19 pandemic from the online retailers like Amazon and Alibaba who completely rely on data-driven decision making.
Assume that you are a newly hired Business Analyst by Walmart; and you are planning to implement a new customer royalty card program to improve Walmart’s data-driven business decision making. The example of such the customer royalty card dataset looks as follow with 5 basic features (see below table):
1) Customer ID: an unique sequential number to identify a customer based on the card issued date.
2) Gender: male or female, claimed by the customer.
3) Age: based on a required government issued ID: date of the birth.
4) Annual income: based on the customer claimed annual income in thousand dollars (000s); e.g., 15 means $15,000.
5) Spending score: based on the previous accumulated purchase value and normalized from 1 to 100.
Your Tasks:
Please analyze the above Walmart business case with the sample customer royalty card dataset, and then write a business report like your individual assignment one (A1) for your Walmart CEO to address the following 5 business analytics questions with equal grading weight (20% x 5 = 100%):
1. What is data strategy which Walmart should adapt? What are data privacy issues which this company should be concerned about?
2. Given the above sample customer royalty card dataset, please apply the CRISP-DM process to do a data analyst on this data to tackle this company's business problems (please explain each of the 6
steps in detail related to the Walmart retailing business). And then, please specify 8 steps which you used for your individual assignment two (A2) to do Data Visualization using Tableau on this dataset.
3. How can you use K-Means Clustering technique to do the Walmart customer profiling based on this sample dataset and what kinds of insights you are planning to find? Please recall your team project experiences and the professor’s lectures & live demos in class and try to explain as much detail as you can for this Walmart business case (no actual data analytics is required!).
4. How can you use at least 2 Classification techniques such as Linear Regression, Logistics Regression, Support Vector Machine, Naive Bayes, Decision Tree, & Time Series Forecast, learnt from this course to do data analytics on the sample dataset and what kinds of trends or pattens you are going to find? Please recall your team project experiences and the professor’s lectures & live demos in class and try to explain as much detail as you can for this Walmart business case (no actual data analytics is required!).
5. Please Conclude and make a final recommendation of Action Plan for Walmart CEO to take based on the above business analytics work, aimed to improving Walmart’s data-driven decision making for better business performance and gaining competitive advantages in the global retailing marketplace.
Exam Remarks:
• In-class exam over MS Teams with your device camera on (you will be recorded for the academic integrity checking purpose).
• Due at the end of your regular class time (1 hour 50 minutes).
• Length: Maximum 5 pages with double spaces for your paper (no Minimum limit).
• APA style paper.
• The above 5 questions have equal weight (20%) and independent except Q5, and you can answer them in your preferred order, but you must mark your answers as Q1 to Q4.
• No actual data analytics work using any tools is required (you are NOT supposed to use Excel, Tableau, or Python during this exam)!
• Write your own paper using your own languages to answer the above questions to avoid high similarity (must be < 10%) to other sources (Do not copy and paste the format).
• Reference is only allowed to your individual assignment papers (A1 & A2), team project report, the professor’s lecture notes, and your textbooks (no Internet access except your final submission please!).
• Prepare for submission time, late submission will lead to a penalty.
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