Math 533 - AMS
I will be going over data provided about our customers based off there location, income size, household size, years at current location, and the credit balance they carry with our company. Using statistical analysis we can learn more about our customers and hopefully use this information to provide better services to our customers in the future to keep them customers for life. In my analysis I will go over the following:
* Customer Location
* Customer Income
* Store Credit Balance
* Credit Balance Compared to Income Level
* Household Size Compared to Household Location
* Household Size Compared to Income Level
Where the customer lives, their income level and the balance they hold with the company are good indicators of customer loyalty. Extending credit to our customers is a good faith way of us showing the customer that we want their business and are willing to take time to pay off their purchases. Before I fully analyze the numbers I would assume that the greater the income level the more they are charging. They have the financial means to pay the monthly payments and can keep a high balance. Customer location is important because it can help tell us how often you frequent the store. Someone in an urban area might shop 4+ times a month, where someone in a rural area might only shop once a month. But the person shopping 4+ times a month might be spending less than the person living in the rural area who can only come once a month because of distance issues. Credit balance compared to income level shows us how much higher income customers are spending. Comparing the household size to the location can give us a feel for where the families are living compared to the single parent or single person households are. You can use this kind of information to market and advertise special sales as necessary. Finally household size compared to the income level will show us how much large families are spending and how often. This can also be useful to market big sales.
The above representation is a pie chart which shows are clients’ demographic locations. Of the 50 customers used in our sampling the greatest numbers of our clients are in the Urban region, with the rural region being the least frequented. Of the sample we concluded that:
* 44% live in an urban area
* 30% live in a suburban area
* 26% live in a rural area
This is in my opinion an expected outcome. The ability to come to our store is much greater for those living in suburban and urban areas because it is closer to their proximity. While residents in the rural communities do frequent our stores they do so on a less frequent basis. Further investigation might be able to show that rural residents could possibly shop less frequently, but spend more per trip than someone in the urban area. Customer Income
This simple bar graph shows the income level in the $1,000s for our customer data set. Simple analysis showed the following: Mean
The average income for our shoppers is right around $46,000, and because the standard deviation is less than the average household income most of our shoppers fall at or below the average income level.
Each customer used for our analysis has a line of credit with us.
After analyzing the data I concluded the following:
The average credit is well over $4,000. Each customer owes $1,000 or more. To further understand credit lines of our customers we should evaluate the credit owed to their yearly income.
Average Credit Balance Based off Income
Here we have what I had originally thought. After averaging the data into sets as shown below: Income Level
| Average Credit Balance
| Average Income
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