Project 7-2 Analyzing Databases
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Feb 20, 2024
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7-1 Project 2
DAD 220
Gene Schexnayder
2/13/23
1.
Opened up the Database with the data we need to perform our queries.
Commands: mysql
Then: use QuantigrationRMA;
2.
Massachusetts has the most returns with 988 returns. South Carolina has
the least returns with only 715 returns. There does not seem to be a trend on returns but there is an average of 851 returns across the states. States with a high return rate are states that their fleet operates more frequently or because those states have more customers. Trends could also be from factors such as the regions weather causing wear and tear.
A.
C
ommands: Select Customers.State AS State, Count(*) AS Frequency
-> From Orders Inner Join RMA On Orders.OrderID = RMA.OrderID
-> Inner Join Customers On Orders.CustomerID = Customers.CustomerID
-> Group BY State
-> Order By Frequency DESC;
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We can also see the top 10 states for frequent returns.
Commands: Select Customers.State AS State, Count(*) AS Frequency From Orders Inner Join RMA On Orders.OrderID = RMA.OrderID Inner Join Customers On Orders.CustomerID = Customers.CustomerID Group BY State Order By Frequency DESC
-> Limit 10;
B.
The most returned product throughout all of the states was
ENT-24-10F, which is about 3 % of all total returns. The overall average percentage of returned products was 2.1%. Product ENT-24-10F return percentage can be lowered to 2% if we were to find the reasons for these returns. Fleet maintenance scheduling can also affect the percentage of returns. Fleet maintenance should be modified to service products and ensure durability. The data here will only give you a starting place to figure out which products are being returned most and where. In order to determine why there is a higher number of returns in some states than others and how to limit the amount of products returned, we would need to dig deeper. The data was limited and I don’t have the reasons for the return or the percentage of the reason for the returns for each product. With that data I could narrow down the areas that need improving.
Commands: Select SKU As Product, (Count(*) * 100 / (Select Count(*) From Orders Inner Join RMA On Orders.OrderID = RMA.OrderID)) AS ReturnPercentage -> From Orders Inner Join RMA On Orders.OrderID = RMA.OrderID Inner Join Customers On Orders.CustomerID = Customers.CustomerID
-> Group By State Order By ReturnPercentage DESC;
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How does the data provide the product manager with usable information?
The data provides the product manager with information by showing which states have the highest returns and which products are returned the most. The manager can use this data to try and make changes in those areas to eliminate the high number of returns.
What are the potential flaws in the data that has been presented?
If I don’t have all of the data or incorrect data then my results would be flawed.
Are there any limitations on your conclusions, or any angles you haven’t considered? I didn’t analyze percentage of returns by item per state. A different product is causing the high returns in states like Massachusetts other than the highest returned product. T
hrough analysis of the data I found that Massachusetts, Arkansas, and Oregon have the highest number of returns. Each state returns about 2.25% of its orders. The item Basic Switch 10/100/1000 BaseT 48 port is the most returned having over 22% of its orders returned.