rajathkotyal/Customer_Market_Clustering_Kmeans

A K-Means Clustering algorithm is built around a shopping mall dataset such that the customers are Seperated/Clustered into different catagories.

Customer Segmentation using K-Means Clustering

Customer Data is taken from a Shopping Mall. which contains :

1. Customer Age
2. Customer Annual Salary
3. Spending score (1-100, 100 meaning the person is an avid shopper)

A K-Means Clustering algorithm is built around the dataset such that the customers are Seperated/Clustered into different catagories.

[ This dataset was used as it is simple & easy to understand for the viewers, please feel free to use datasets with more number of features ]

---

Analysis of the above graph :

Clusters in order :

1. Red - These are people with low income but yet high spends in shopping. - Should be presented with more discounted products.
2. Blue - Customers with average income and average spends - Its volatile and much cant be done to this segment.
3. Green - Customers with high income, high spends - Must be presented with premium products.
4. Cyan - High income, low spends - Make them understand the brand/s more and push in offers accordingly.
5. pink - Low income, low spends - Should be presented with discounted products.

This way Clustering can be utilized inorder to do Targeted Marketing efficiently .