DAD 220 Project 2

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School

Southern New Hampshire University *

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220

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Computer Science

Date

Feb 20, 2024

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Sarah Byerly DAD 220 Professor Venckus 10/09/2023 1. Begin by writing SQL commands to capture usable data (which you've preloaded into Codio) for your analysis. sudo chmod +x change_perm.sh sudo ./change_perm.sh mysql show databases; use QuantigrationUpdates; show tables; SELECT COUNT(*) FROM Collaborators; SELECT COUNT(*) FROM Orders; SELECT COUNT(*) FROM RMA; These are the commands I used to start. I changed the ownership of the workspace. By using these commands, I verified that my database and corresponding tables had the information needed for this project. 2. Specifically, the product manager wants you to analyze the following:  Analyze the number of returns by state and describe your findings in your report. SELECT Collaborators.State AS State, Count(*) AS Return_Number FROM Orders INNER JOIN RMA ON Orders.OrderID = RMA.OrderID INNER JOIN Collaborators ON Collaborators.CollaboratorID = Orders.CollaboratorID GROUP BY State ORDER BY Return_Number DESC LIMIT 15; SELECT Collaborators.State AS State, Count(*) AS Return_Number FROM Orders

Sarah Byerly DAD 220 Professor Venckus 10/09/2023 INNER JOIN RMA ON Orders.OrderID = RMA.OrderID INNER JOIN Collaborators ON Collaborators.CollaboratorID = Orders.CollaboratorID GROUP BY State ORDER BY Return_Number ASC LIMIT 15; Description of findings: This analysis shows the returns by state. The findings show an interesting pattern. After arranging the state's highest first, we can see that Massachusetts has the highest returns. We also see that Oregon, Arkansas, Alabama, and West Virginia are also in the top 5. Massachusetts has 972 returns while Arkansas has 844. This trend shows that these states may have contributing factors for the higher return rate. This could be related to product quality, customer preference, or distribution logistics. When looking at the lowest return states we find that South Carolina has the fewest returns with 702 followed by New Jersey, Colorado, Georgia, and Nebraska. Customer satisfaction, an efficient return process, or better product sustainability could be factors that are contributing to these lower numbers. Further analysis is needed to determine the underlying cause of these trends. A further investigation can help find the root cause of these trends and could give insight into possible new strategies that would improve customer satisfaction.  Analyze the percentage of returns by product type and describe your findings in your report. SELECT SKU AS Product_SKU, Description AS Product_Description, COUNT(*) AS Total, (COUNT(*) / (SELECT COUNT(*) FROM Orders INNER JOIN RMA ON Orders.OrderID = RMA.OrderID) * 100 ) AS Percentages_of_Returns FROM Orders

Sarah Byerly DAD 220 Professor Venckus 10/09/2023 INNER JOIN RMA ON Orders.OrderID = RMA.OrderID WHERE UPPER(status) = 'COMPLETE' GROUP BY Product_SKU ORDER BY Percentages_of_Returns DESC LIMIT 15; Description of Findings: The data shows interesting findings for the percentage of returns. Basic Switch 10/100/1000 BaseT 48 port (BAS-48-1 C) has the highest percentage of returns at 19.7120%. This high percentage could be due to performance, functionality, or usability. Secondly, Enterprise Switch 40GigE SFP+ 48 port (ENT-48-40F) was the second highest percentage at 14.5610%. This might be caused by the same issues as the previous product. Thirdly, Enterprise Switch 10GigE SFP+ 48 port (ENT-48-10F) has a 10.2406%. This could need further investigation to determine if the issues are like the previous products. Fourthly, Enterprise Switch 10GigE SFP+ 24 Port (ENT- 24-10F) could also have similar issues due to the 10.1262% which is remarkably close to the previous item's percentage. With the consistently high return percentages it might be worthwhile to investigate the possibility that there is a broader issue going on with Enterprise Switch products since they have 3 of the top 5 highest return percentages. These findings suggest there could be design flaws, customer dissatisfaction, or functionality gaps related to these products. Further investigation could lead to the discovery of the reasons for the high return percentage. This could further help improve the products and in turn improve customer satisfaction and retention. To further understand the customers, a review of the customer reviews and feedback would give a better understanding. SELECT SKU AS Product_SKU, Description AS Product_Description, COUNT(*) AS Total, (COUNT(*) / (SELECT COUNT(*) FROM Orders INNER JOIN RMA ON Orders.OrderID = RMA.OrderID) * 100 ) AS Percentages_of_Returns

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Sarah Byerly DAD 220 Professor Venckus 10/09/2023 FROM Orders INNER JOIN RMA ON Orders.OrderID = RMA.OrderID WHERE UPPER(status) = 'COMPLETE' GROUP BY Product_SKU ORDER BY Percentages_of_Returns ASC LIMIT 15; Description of Findings: Basic Switch 10/100/1000 BaseT 24 port (BAS-24-1 C) has the lowest return percentage of 0.0745%. This seems to be the best product based on return rate. This could mean that customers are satisfied with this product. Secondly, the Enterprise Switch 40GigE SFP+ 24 port (ENT-24- 40F) also has a relatively low return with 4.9965%. This also suggests that the customers are satisfied with this product's performance and functionality. Thirdly, the Advanced Switch 10GigE Copper 24 port (ADV-24-10C) has a return of 9.8360%. Customers are still satisfied with this product even though the return percentage is almost double the previous product. Fourthly, Advanced Switch 10 GigE Copper/Fiber 44 port copper (ADV-48-10F) has a return of 9.9452%. Again, it is not the lowest, but customers are still somewhat satisfied with is product. The findings here suggest that the customers generally like these products with a small number being returned. This could be due to the product's quality, performance, design, and overall customer satisfaction. To better understand these findings, examination of customer feedback and reviews will give insight into why the customers are not returning these products. It will be most influential in helping with further development of top returned products and return rate reductions. Additional Analysis: SELECT (SELECT COUNT(*) FROM Orders) AS Number_Of_Orders, COUNT(*) AS Number_Of_Returns,

Sarah Byerly DAD 220 Professor Venckus 10/09/2023 COUNT(*)/(SELECT COUNT(*) FROM Orders) * 100 AS Percentage_Of_Orders_Returned FROM Collaborators INNER JOIN Orders ON Collaborators.CollaboratorID = Orders.CollaboratorID INNER JOIN RMA ON Orders.OrderID = RMA.OrderID WHERE UPPER(RMA.Status) = 'COMPLETE'; SELECT Reason, COUNT(*) FROM RMA GROUP BY Reason; Description of Findings: The total number of orders is 37,998. The total orders returned is 33,653. This means that 88.5652% of orders were returned. This extremely high return rate is of great concern and warrants further investigation into product quality, customer satisfaction and accuracy of orders. Looking at the reasons for the returns also gives some insight into why the return rate is so high. There are 13,116 orders returned due to their being an incorrect product or not meeting customer expectations. There were 12,609 returns due to defective products. Lastly there were 11,841 returns due to other reasons. After reviewing these findings, it is crucial to further investigate the causes of these returns to determine if there is an issue with shipping, a defect in the product or other issues not listed here. To reduce the number of returns and improve customer satisfaction the company can improve order accuracy, quality control measures, communication with customers, and description of products. By conducting regular analysis of returns the company can identify and correct issues within the business. 3. In your report, clearly summarize your analysis of the data for stakeholders . Include screenshots of the results of each query. When summarizing results, you may want to consider the following questions:

Sarah Byerly DAD 220 Professor Venckus 10/09/2023 o How does the data provide the product manager with usable information? o What are the potential flaws in the data that has been presented? o Are there any limitations on your conclusions, or any other ways of looking at it that you haven't considered? Clearly communicate your findings to stakeholders. RMA Analysis Report Analysis by State: The first thing I did was analyze the number of returns by the state that the RMAs were received. Once the states were in a descending order, I easily identified variations in the rate of returns across different states. Massachusetts, West Virginia, Oregon, Arkansas, and Alabama reported some of the highest rates of returns. This indicates areas of concern in quality, distribution, and customer preferences. On the other hand, Georgia, Colorado, New Jersey, Nebraska, and South Carolina reported some of the lowest returns. This could indicate that the return process is more effective in these areas, causing customer satisfaction to be higher in these regions. The strategies that the states with the lower rates should be investigated and implemented in the states with the significantly higher return rates. Analysis of Returns by Product Type: My analysis looked at the percentage of returned products by their types. This provided information on the products that had high rates of returns. These high numbers could be caused by design issues, customer dissatisfaction or gaps in functionality. For example, the Basic Switch 10/100/1000 BaseT 48 port (BAS-48-1 C) was the highest returned product with 19.7120%. This indicates there are issues with this item's performance, functionality, or usability. Similar observations were seen with other products on the Enterprise Switch product line. These findings warrant further investigation and strategies needed for improvement. Additional Analysis: I looked deeper to gain a better understanding of what was going on with returns. I figured out the total number of orders, returns and what percentage of orders were returned. This discovery was alarming with 88.5652% of all orders were returned. This is a major concern and requires prompt investigation into why this is happening. I also investigated the reasons these items were being returned and found that product defects and incorrect products were the two main reasons

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Sarah Byerly DAD 220 Professor Venckus 10/09/2023 for returns. By addressing these issues through order accuracy and better-quality control measures we can reduce the number of returns significantly and improve customer satisfaction. Information for the Product Manager: The analysis gives the product manager insight into ways to optimize operations while also increasing customer satisfaction. Understanding the patterns of the returns by state and product type will allow the company to tailor strategies for improvement in quality and address the issues with distribution and order accuracy. This analysis also brings to light the need to investigate the causes of the returns and the need for modifications to the quality control and order accuracy. Possible Limitations or Flaws: This analysis offers valuable insights, but potential limitations need to be considered. This data may only give a glimpse of the issues that cause returns. This is due to the reviews and feedback not being incorporated. External factors such as market trends and competing products could influence the rate of returns. This analysis does not account for customer preferences, which could be a contributing factor for returns. This data does not indicate a specified time for the information. Having a defined time is vital to better understand the trends of the returns and sales. Another thing is there is no indication of the impact the returns have on the inventory or working capital. There is zero insight from customers for the high returns.

DAD 220 Project 2

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