In a sales data set contains three groups of data
1. Customer
2. Product
3. Customer Purchase
4. Sales Location (optional)
And the data analysis should contains the following results
1. Customer wise revenue.
This will give a clear revenue map about the Who is making high revenue and who is making the less revenue
2. Strategy to boost the sales (Forecasting the way to improve the sales)
Identifying the low level sales area and find the marketing solution to boos the sales (i.e. a discount coupon for low level sales item)
If a company is a small one and it has only a few customers then one can easily understand who is making high revenue and who is making low revenue.
On the other hand, if a company has millions of customers, then it is not possible to identify customer by customer. Now we can view the customer as a few customer group or segments or clusters. Generally the following clusters are using
1. Age wise customer segmentation
2. By Occupation
3. By Customer’s Monthly or Yearly Income
4. By Sex
The segmentation or clustering is valuable when the segment wise focus on single group of product. A segment is purchasing the entire product range, where by using this segmentation we cannot find the low level selling product. Then the data analyst should find the next type of segmentation or grouping or clustering to relate the sales.
1. Customer
2. Product
3. Customer Purchase
4. Sales Location (optional)
And the data analysis should contains the following results
1. Customer wise revenue.
This will give a clear revenue map about the Who is making high revenue and who is making the less revenue
2. Strategy to boost the sales (Forecasting the way to improve the sales)
Identifying the low level sales area and find the marketing solution to boos the sales (i.e. a discount coupon for low level sales item)
If a company is a small one and it has only a few customers then one can easily understand who is making high revenue and who is making low revenue.
On the other hand, if a company has millions of customers, then it is not possible to identify customer by customer. Now we can view the customer as a few customer group or segments or clusters. Generally the following clusters are using
1. Age wise customer segmentation
2. By Occupation
3. By Customer’s Monthly or Yearly Income
4. By Sex
The segmentation or clustering is valuable when the segment wise focus on single group of product. A segment is purchasing the entire product range, where by using this segmentation we cannot find the low level selling product. Then the data analyst should find the next type of segmentation or grouping or clustering to relate the sales.
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