Clients clustering: How would you procede for adding other than rfm variables to kmeans?

Summary

K-means clustering gains new insights by integrating additional variables that better predict customer behavior.

K-means clustering: what is happening

There is a growing interest in enhancing K-means clustering by adding variables beyond traditional RFM (recency, frequency, monetary) metrics. A recent discussion on Reddit suggests variables such as product returns, payment methods, and web behavior data. This raises questions about the best approach: should these variables be integrated into the same K-means clustering or analyzed separately?

K-means clustering: why this matters

For BI professionals, understanding how various variables can affect data clustering is crucial. The trend toward more advanced customer segmentation requires professionals to explore innovative methods to better understand customer groups. Competitors may be using different analytical techniques, so it is important to invest now in approaches that maximize customer data value. Combining different data types can lead to more accurate insights and a competitive advantage.

K-means clustering: concrete takeaway

BI professionals should consider incorporating diverse variables into their clustering strategies. Test different approaches, such as combining variables or performing separate K-means analyses by cluster, to discover what yields the best results for customer segmentation.