Find the Natural Segments in Your Data Automatically

How it works

The analysis standardizes your selected columns so each counts equally, then fits k-means clustering for every candidate number of clusters from 2 to 8. Each candidate is scored by average silhouette width — how much closer rows sit to their own cluster than to the nearest other one — and the best score picks k. Each discovered segment is auto-named by its most distinctive features, profiled in your original units, and drawn on a 2-D map.

Use it when you suspect your rows fall into distinct types — customers, products, sessions, stores — and you want the data to tell you how many types and what defines each one.

Not for categorical-only data, time-series pattern grouping, or when you need clusters of arbitrary shape (k-means assumes compact, roughly round groups).

What data do you need?

Any table where each row is an entity described by numeric columns. For example, customer behavior metrics:

Minimum 30 rows · Best with 100-10,000 rows and 2-12 numeric columns

What's in the report?

Standard-library analysis: discover the natural groups hiding in your data. K-means clustering on standardized features with the number of clusters chosen by the data itself (average silhouette width over k=2..8) — segment sizes, per-segment profiles in original units, auto-named segments, and a 2-D map of the groups. Works on any dataset: map 2 or more numeric columns.