Learn how different binning strategies transform the same numeric variable into categories with different meanings. Compare methods side-by-side to see how design choices shape interpretation.
Categories are constructed through design choices, and different choices create different meanings from the same data.
This tool teaches you five approaches to converting numeric variables into categorical groups. Each method makes different trade-offs between interpretability, balance, and fidelity to the underlying distribution.
| Method | Best For | Avoid When |
|---|---|---|
| Equal-width | Uniform distributions, simple reporting | Highly skewed data |
| Quantile | Ranking, percentile-based targets | Many tied values at boundaries |
| Manual | Known thresholds (e.g., NPS categories) | No domain knowledge available |
| Jenks | Geographic/spatial data, natural clusters | Need for equal-sized groups |
| K-means | Discovering patterns, exploratory analysis | Need reproducible business rules |
Each case study demonstrates a different distributional pattern commonly found in marketing data. Select one to explore how binning choices affect interpretation, or upload your own dataset.
Select a case study from the dropdown above to load data.
Provide a file with a header row. After upload, select which numeric variable to analyze.
Drag & Drop data file (.csv, .tsv, .txt, .xlsx)
First row should contain column headers.
Choose a numeric variable to transform into categories.
Enter cutpoint values separated by commas (e.g., "0, 10, 50, 100"). Values will be sorted automatically.
Load data and apply a schema to see the visualization.
Each dot represents one observation. The x-position shows the raw numeric value (this never changes). The y-position is random jitter—it spreads dots vertically so you can see individual points instead of them stacking on top of each other. The y-axis has no meaning; ignore it.
The color and vertical band show the bin assignment under the current schema. When you change the binning method or number of bins, watch how dots get reassigned to different categories—same data, different interpretation.
Compare how different binning methods partition the same data. Each panel updates automatically as you change settings. Click "Use This" to apply that schema to the Bin Summary below.
Different binning methods optimize for different goals. Equal-width creates uniform ranges (good for interpretability). Quantile creates equal-sized groups (good for comparisons). Jenks and K-means minimize within-group variance (good for clustering). Seeing them side-by-side reveals how the same data tells different stories depending on your binning philosophy.
Apply a schema to see bin statistics.
Generate deterministic code to reproduce this binning in your analysis environment.