Clustering Analysis
Stop guessing who your customers are. We surface the natural groups hiding in your data — each with its own behavioral profile — so you can target, message, and design offers that actually fit the people you're selling to.
Dataset
2147 Records
Marketing campaign customer data
1. Analysis Name
Clustering Analysis
We find the customer segments that actually exist in your data — not the ones assumed in advance — so your targeting, messaging, and offer design are grounded in real behavior.
2. Problem Context
What you'll be able to decide
Once you see which segments exist and how they differ, you can make concrete calls: which group to prioritize, what message fits each audience, and where a single campaign is leaving revenue on the table by treating everyone the same.
3. Observed Data
Raw customer observations and descriptive statistics
The chart below plots the observed customer records on the selected axes. The table summarizes the feature distributions for the same underlying population.
Descriptive Statistics
4. Workflow
How the answer is built
Each visual corresponds to a step in the segmentation workflow, from selecting a usable number of clusters through validating separation and interpreting segment profiles.
Choose K
Evaluate inertia across candidate cluster counts to locate a practical elbow.
Check Separation
Compare relative cluster sizes and radar profiles to verify distinct shapes.
Interpret Segments
Translate statistical differences into segment narratives that can guide action.
Silhouette Score
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Inertia
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Within-cluster sum of squares
5. Conclusion
Recommended segmentation answer
Why this is the best answer
Cluster Profiles