CASE STUDY ACCOUNTING 790 METADATA The data set for your analysis includes variables related to Global Bike's most recent sales data. You should be familiar with most of the variables, but here are some hints on this data set. ProdCat CatDescr OrderItem Division UnitOfMeasure Years Product category Product category description Line item in the sales order. First line is “10”, second is "20", and so on. "AS" = Accessories, "BI" = Bicycles "ST"=stücke in German translates to piece or each in English 2010 through 2022 PROBLEM STATEMENT Nina Kane and the global sales team at Global Bike are concerned about new competitors in the market. A strategic planning meeting is scheduled for the first of the month. Your job is to explore the data and provide insights on GB's current customer base and sales data. You need to complete the following analyses for the sales team. Be sure to include screen shots to support each of your answers and analyses. Submit your report as one Word document in Canvas. Title the file "your last name final case". For example, my document would be named "JonesN Case Study". Use full sentences and professional formatting. Visualizations are preferred over tables. Address your analysis to Nina Kane, President of Sales at Global Bike. Examine and clean and transform the data as needed. • Include your name in the title of all visualizations. Save all visualizations in your Story in SAC. Save your story in your 790 folder as LastNameFirstInitial Case Study. (My SAC file would be called "JonesN Case Study".) Create a visualization to answer each of the following questions for Nina and her team. While you may use more than one chart to answer the question, all of the variables need to be included in the charts. For example, charts for the first question would include revenues, regions, and product category. Write a brief interprestation of the visual analysis. Explain any filters, ranks, calculations, and/or aggregations. 1. Do sales revenues vary by region and product category? 2. Are some customers and divisions more profitable than others? (Use gross margin ratio.) 3. Are there any patterns of the number of orders by customer? Do the pattern(s) vary over time? As you are analyzing the data, pay attention to data that may be of importance to Nina and her team. Identify two (2) interesting and distinctly different relationships amongst three or more variables of the data set and discuss each briefly. You might hypothesize as to why the relationships are what they are. For example, "It makes sense that X is positively related to Y and Z because they are ...." Do NOT repeat any of the analyses from questions above or use the same variable for your two observations. 1. Create an appropriate visualization to illustrate the relationships you discovered. Use at least three (3) variables for each relationship. Do not use the same three variables for both of your visualizations. In some cases, I might recommend a tree map, Marimekko, and/or bubble chart to handle the larger quantity of variables. Alternatively, you might use trellising or some other means of handling multiple variables. Filter, sort and/or rank data if needed and explain what you filter if you do. You might even show the settings in a screen shot. Be sure to identify the aggregation of your measures for each chart, perhaps in the title. a. Explain to Nina why each of these visualizations is important to her team. Nina Kane, her US sales team, and her colleagues in Germany believe that Global Bike has four (4) groups of customers with distinct characteristics other than geography. She also knows that customers have changed over the past few years and so any analysis that you provide on the groups should be focused on the newest data available. Create a cluster analysis for Nina and her colleagues. What can you tell Nina about the 4 customer groups? (Use the following as a guide to answer this question.) 1. Define how you clustered the transactions for Nina. What measures and dimensions did you use? Did you use any filters? If so, what were they? 2. Is there a group that stands out? Why? Take a screenshot of the cluster analysis and explain why you chose that cluster and any characteristics that define that cluster. The sales team is also interested in sales trends overall. Create a forecast of sales for the next 36 months. 1. Is the forecast reliable? Why or why not? MORE HINTS You will need to be sure you have at least one date dimension. • Avoid using column or bar charts for every answer. GRADING PARAMETERS • Did you follow instructions? • Did you choose the right model(s) for the question asked? • • Did you get the correct answers to specific questions? Were you able to accurately explain the results of your model(s)? Do your explanations show an understanding of the data in the data set? Did you choose the best visualizations for the data represented? . Did you choose the correct variables aggregated at the appropriate level for the question at hand? Are your visualizations properly formatted, (i.e.; titles and legends), and easily read and interpreted? • Well organized and professionally written report. I will likely look at your files in SAC. Make sure that I have access. Do your visualizations in SAC match what you submitted in the write up? (You may have more visualizations in SAC than in your write up, but you must at least have the ones that you used to support your answers to this case study.)