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Running k-means customer segmentation analysis...
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Analyze another fileSegments data into k distinct groups using K-Means clustering. Automatically determines the optimal number of clusters or lets you specify. Shows cluster profiles and assignments.
Use this when you want to find natural groupings in numeric data and know (or want to discover) how many groups exist.
If clusters aren't spherical, use DBSCAN. If you want customer segments with business labels, use RFM Segmentation.
Built for: Data scientist, marketing analyst, customer analyst
Typical data source: Numeric dataset with 2+ variables for segmentation
Customer or product data for segmentation
Minimum 30 rows · Best with 200-10000 rows
Segments customers into distinct behavioral groups using k-means clustering on RFM features. Automatically determines optimal cluster count via elbow method and silhouette analysis. Delivers cluster profiles, PCA visualization, and actionable customer segment labels.
Within-cluster sum of squares vs number of clusters
Average silhouette width by cluster count
2D PCA projection of customer clusters
Scaled feature means per cluster for RFM comparison
Cluster sizes and RFM statistics
Top customers by segment with RFM values
Plain-English interpretation — what the numbers mean, what's significant, and what to do next.
Need something simpler? Correlation — Just want to see relationships, not clusters
Need more power? Dbscan — Clusters are non-spherical or you don't know k
Similar: Rfm
See our FAQ for details on pricing, data privacy, and how the analysis works. Every report includes a Methodology section showing the statistical test, assumptions checked, and diagnostics run.
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