Let's see how to interpret the results after we have run our model. The results consist of 4 tabs: Cluster Summary, By Cluster, By Numeric Value, and Details Tabs.
As the model divided your data into clusters, a group of objects where objects in the same cluster are more similar to each other than to those in other clusters, it is essential to compare the average values of the variables across all clusters. That's why in the Cluster Summary Tab you can see the differences between the clusters through the graph. For example, in the picture above, you can see that customers in Cluster0 have the highest average value of Spending Score, unlike the customers in Cluster4.
Wouldn't it be interesting to explore each cluster by a numeric value or each numeric value by a cluster? That's why we have the By Cluster and By Numeric Value Tab - each variable and cluster are analyzed by their minimum and maximum, first and the third quartile, etc. The devil is in the details - details are important, so be conscientious and pay attention to the small things. Last but not least, on the Detail Tab, you can find a detailed table where you can see all relevant values which were used for the above results.
With the right dataset and a few clicks, you will get results that will considerably help you in your business - general segmentation helps you in creating marketing and business strategies for each detected group. It's all up to you now, collect your data and start modeling. 🙂