Global superstore dataset

MKT 356 Take Home Midterm Exam Spring 2018 Instructions: 1. Open notes/book, but independent work is required. You may work with other students on this exam. However, each individual must turn in their own exam. Plagiarism will result in a mark of zero for the exam, and will be reported to the department. 2. Please support your claims with proper charts or exhibits – remember you are talking to a business audience. If you show a chart that is with too much information or not readable, no marks can be given. One or two charts are expected for each question, except for Part B q1 and q2 (For these two questions, you may just say “Done. See sheet2 in the file xxx.xlsx.”). 3. An appendix of how you construct each exhibit is required. An example of such appendix is on Canvas. Please put the appendix at the end of your word document. 4. Your work is due 5pm 3/20/2018 on Canvas. Please submit a word document (put the Appendix mentioned above at the end of the document – don’t put them in the text), and other supporting twb files and/or excel files. Please save your Tableau charts as images and paste them in the proper places in your word file. I will be grading your word file ONLY. 5. No re-do is allowed for the Midterm Exam. Using the global_superstore.xls data (the “Orders” sheet, unless instructed otherwise), answer the following questions. Part A [50 marks]: Compare profitability for the Africa, LATAM, and the US market. 1. Describe what this dataset is all about. (It is about How many consumers’ purchasing records, where, when, and in what product categories?) (5 marks) 2. Comment on the overall profit of these markets. Together with their sales, what would you say? (5 marks) 3. Now add product category information to the picture. Comment on profitability again. (5 marks) 4. Take the US market as an example, see whether your comments in question 3 would apply on a year to year basis or not. What do you say now? (5 marks) 5. Bring variable “Discount” to the picture. Do you see discount done differently in these markets, in each category? (5 marks) 6. Bring variable “Shipping Cost” to the picture. Please comment on how shipping cost may affect profitability in each category. (5 marks) 7. Now Open the data in Excel. In the “Orders” sheet, create a new column, and call it Returned. Use the vlookup function, and the OrderID as identifier, to bring in information for “whether the order is returned or not” from the “Returns” sheet. Then create a blank Tableau workbook and connect the data again. Compare two economic aspects of the returned orders to those that are not returned. Comment on the results. [Hint: you may consult hw3.] (15 marks) 8. Based on what you have observed so far, can you make some conjectures and recommendations for each market, in terms of how we may improve its profitability? Please support your claims with facts that you commented on previously. (5 marks) Part B [40 marks]: RFM is commonly used in marketing, retail and professional services industries to assess customer value. The general idea behind the analysis can be summarized as: Recency: People who have purchased recently from you are much more likely to respond to a new offer than someone who you haven’t sold to in a long time. Frequency: People who shop frequently at your store are more likely to respond to new offers than less frequent buyers. Monetary: People who spend more money at your store are more likely to show interest in new offers. They are defined as: Recency: How long ago did the customer make a purchase (usually measured by days)? Frequency: How many purchases has the customer made (during the last year or other time period)? Monetary Value: How much has the customer spent in total (in given time period)? 1. Please set 2014 data aside, and use 2011-2013 data (the end of year 2013 as cutoff point for recency), and compute the RFM for each customer. Your resulting database is one with 795 customers and four variables: Customer Name, Recency, Frequency, and Monetary. (5 marks) 2. For each of the R, F and M variable, create a code for each customer by first sorting the variable, and then assigning group membership 3 (the most recent/most frequent/spent the most), 2, or 1 to each customer. The three groups are equal size (in this case, 1/3, 1/3, and 1/3). Now your RFM database has 6 variables (with customer names, it would be 7 columns). And you can bring total profit for each customer in (the 8th column) to add additional information for the analysis below.