---
title: "Cluster analysis"
url: "https://helpdesk.telmarhelixa.com/en/article/cluster-analysis"
language: "en"
last_updated: "2026-10-27T15:23:47.313Z"
---
# Cluster analysis
Partitions respondents into mutually exclusive segments that are internally homogeneous and externally heterogeneous.
## **Inputs and outputs**
- **Survey inputs:** Likert scales (1 = definitely agree to 5 = definitely disagree), volumetrics and binary questions.
- **Quality metrics:** Determination (1 to 100%), Calinski-Harabasz Index, Silhouette Score and Inertia.
- **Cluster label renaming:** click the default cluster headers ("Cluster 1", "Cluster 2") to assign custom persona titles (for example "Value Shoppers", "Tech Enthusiasts") for presentations.
## **Metrics for cluster quality**
| Metric | Purpose and interpretation |
| --- | --- |
| Determination | Finding the optimal number of clusters that best represents the data structure. Shown on a 1 to 100% scale;
**higher is better**
. If Determination scores are similar, the data may be too similar to create more clusters; more varied data produces more cluster group recommendations. |
| CH-Index (Calinski-Harabasz) | Measures separation between clusters and compactness within clusters. A
**higher**
CH-Index means better-defined, more distinct clusters, and helps identify the optimal number of clusters. |
| Silhouette Score | Quantifies how similar data points are to their assigned cluster compared with other clusters. Used to determine the optimal number of clusters and evaluate separation. |
| Inertia (within-cluster sum of squares) | Measures compactness.
**Lower**
inertia means points are more tightly grouped around their cluster centroids. |
## **Saving and viewing Cluster Analysis results**
You can save multiple cluster analysis results to review them at a later date within the same report.
- After running an analysis, click the "Save Cluster Analysis" button. Provide a name and an optional description, then click Save.
- To retrieve past runs, click "View cluster analysis" to open a modal listing your saved iterations, and click Select to load it onto the canvas.
## **Use cases and benefits**
- **Identifying differentiated audience segments:** split a group such as a product category into mutually exclusive groups of like-minded people, based on responses to lifestyle and attitudinal statements.
- **Understanding buying behavior:** find groups of buyers and analyse each group separately on favorite stores, brand loyalty, price willingness and purchase frequency.
- **Identifying new product opportunities:** cluster brands and products to find competitive sets. Brands in the same cluster compete more with each other, which exposes gaps in current offerings.
- **Attitudinal profiling:** categorize people by attitudes, beliefs and preferences (psychographics) rather than only demographics or behavior, to build more personalized campaigns.
- **Media industry segmentation:** segment audiences to create targeted content or advertisements, maximizing engagement and ROI.