Week 1 Assignment Assessment Description The purpose of this case assignment is to apply analytics techniques to a marketing analytics problem, determine solutions for addressing organizational challenges, and communicate recommendations to organizational stakeholders. Problem Tesch Garden Equipment Inc., is a distributor of garden equipment machinery and tools. The company also sells soil, fertilizer, shrubs, and small trees. Their customer base primarily consists of small landscaping companies that sell to and service homeowners with small acreages, hobby farms, etc. The owner of Tesch is requesting that you provide an analysis of data that were gathered during the prior calendar year. The data relate to items purchased. While there is no missing data, the data are somewhat incomplete (e.g., product category information is provided but not the actual product itself). The dataset is included in the attached "Marketing Analytics Case Study Data" file. Descriptions of the data fields are as follows: 1. Record: Index field for each record (i.e., row) in the dataset 2. CustomerMonths: Number of months the customer has purchased products from Tesch 3. ProductCategory: Category of product purchased (per Product Category Key provided below and in the "Marketing Analytics Case Study Data" file. 4. UnitRevenue: Unit revenue (i.e., price) for item purchased 5. Quantity: Number of items purchased 6. Total Revenue: UnitRevenue * Quantity 7. GiftCardPurchase: 1 = Gift Card used for Purchase; 0=Gift Card not used for Purchase Product Category Key: A = Snow removal tools and equipment B = Specialty garden tools . C = Soil and fertilizer • D = Medium to large garden/lawn machinery E=Common shrubs and trees F = Small to medium garden/lawn machinery G = Specialty shrubs and trees Descriptive Analysis and k-Means Cluster Analysis Part 1 Based on the data in the "Marketing Analytics Case Study Data" file, use Tableau to visually gain insight into the following: 1. The Quantity of each Product Category sold 2. The Total Revenue by each Gift Card category 3. Histogram of Quantity, marked by Product Category 4. Total Revenue by Customer Months 5. Total Revenue of each Product Category sold Note that a different Tableau chart is needed for each item above (i.e., five unique charts are needed). For each required item above, take note of the results. The insights gained from this information will need to be summarized in a PowerPoint presentation that will be submitted as a part of this assignment. You are required to submit the completed Tableau *.twb file to your instructor. Part 2 Based on the data in the "Marketing Analytics Case Study Data" file, use RStudio to perform descriptive analyses on the "TotalRevenue" variable. 1. Note that classic statistical analyses (mean, median, standard deviation, etc.) need to be performed along with a histogram or any other relevant charts. 2. Determine if outliers exist for the "Total Revenue" variable. Justify your response. For each required item above, take note of the results. The insights gained from this information will need to be summarized in a PowerPoint presentation that will be submitted as a part of this assignment. You are required to submit the R commands to your instructor by pasting them into a Word document. Part 3 Based on the data in the "Marketing Analytics Case Study Data" file, use KNIME to perform a k-means cluster analysis to ascertain if any realistic segments can be derived from the data. In performing this analysis, take note of the following: 1. Use the following fields to cluster on: CustomerMonths, UnitRevenue, Quantity, and Total Revenue. Use a Column Filter node to filter out the rest of the fields prior to importing the data into the Normalizer Node. 2. Use a Normalizer Node prior to importing the data into the k-Means Node. Use a denormalizer node prior to exporting the data (with the cluster assignments) to the Excel Writer node. It is recommended that you perform a cluster analysis for three to seven clusters and select the optimal number of clusters based on the results. 3. At a minimum, provide the following screenshots from KNIME for the optimal number of clusters: (a) One or more Scatterplots from the Scatter Plot Node. Ensure Cluster is on the y axis; pick a field(s) for the x axis. (b) Mean Silhouette Coefficient Table from the Silhouette Coefficient Node. (c) Line Plot of the Mean Silhouette Coefficient Table (note that a Line Plot Node needs to be connected to the Mean Silhouette Coefficient output port of the Silhouette Coefficient Node). 4. Using KNIME, place the results of the cluster assignments into an Excel file and sort rows by Cluster. For each required item above, take note of the results. The insights gained from this information will need to be summarized in a PowerPoint presentation that will be submitted as a part of this assignment. You are required to submit the completed KNIME *.knwf file to your instructor. Specifically, export your KNIME model to a KNIME workflow file. To perform this task in KNIME, ensure that your KNIME model is active (i.e., displayed). Then, go to File -> Export KNIME Workflow. In the "Destination workflow file name (.knwf)" area, browse to a specific location on your computer. Click "Save" and then click "Finish." PowerPoint Presentation Create a 20-30-slide PowerPoint presentation that summarizes the setup and results of your linear optimization analysis. The analysis setup portion of the presentation should include the following: 1. Summary of the Tableau charts from Part 1. 2. Results of the statistical analyses from Part 2. 3. Summary of the cluster analysis from Part 3, including identification of specific segments. 4. Perform any other analyses as you see fit in order to gain greater insights into the results. Results of each analysis must be included in your presentation. The use of graphs, charts, and supporting data is required. You must interpret the results of each analysis and draw general conclusions from the results. You will make recommendations for the organization and address the organizational challenges that may be encountered based upon your recommendations. The PowerPoint presentation should be organized in the following way (incorporating the content-specific information described above accordingly). 1. Introduction and case background. 2. Objectives for each analysis. 3. Approach or method of analysis and justification for selecting the approach or method. 4. Results of each analysis. 5. Supporting graphs, charts, data, and spreadsheets for each analysis (minimum = 4, maximum = 8). 6. Interpretation of the results for each analysis. 7. Based on all analyses, what marketing-related insights can be gained based on the results of the data analyses? 8. Recommend a specific course of actions that should be taken regarding the findings of your analyses. Discuss challenges that could be encountered if the prescribed course of action(s) are taken. 9. Description of additional marketing analytics approaches that might be useful this organization. In the "Notes" section of each slide, include your talking points. This information should align to the results of your analyses and be supported in the accompanying Excel file. In addition to the PowerPoint file, submit the following: 1. Completed Tableau *.twb file from Part 1. 2. R commands from Part 2 pasted into a Microsoft Word file. 3. Completed KNIME workflow *.knwf file from Part 3. 4. Cluster assignments Excel file from Part 3.