Charts & Visualizations

Heat Map

A two-dimensional visualization using color intensity to represent values.

Definition

Heat maps display data in a matrix format where individual values are represented by colors. The intensity or hue of each cell indicates the magnitude of the corresponding value, making it easy to spot patterns and variations across two dimensions.

Examples

Website traffic by hour (columns) and day (rows)

Chart Visualization

This example includes an interactive chart visualization with 2 data points.

Chart type: heatmap

Usage

Best Used For

  • Visualizing patterns in large datasets
  • Showing correlation matrices
  • Identifying clusters and outliers
  • Comparing categories across two dimensions

Data Requirements

[Object]

When to use this chart

Cohort retention

Data shape: Month-0 to month-6 repeat rate by acquisition cohort

Color intensity for rate; include numeric labels on key cells for accessibility

Source: Wilke (2019); marketing analytics cohort reporting standards (Mixpanel, Amplitude docs)

Limitations

Important Considerations

  • ⚠Color perception can vary between viewers
  • ⚠Limited to two primary dimensions
  • ⚠May require careful color scale selection
  • ⚠Can be difficult to read exact values

Best Used For

  • Visualizing patterns in large datasets
  • Showing correlation matrices
  • Identifying clusters and outliers
  • Comparing categories across two dimensions

Supplemental Resources

Frequently asked questions

Common questions about Heat Map, answered.

What is a heat map?
A heat map represents data as a grid of cells where color encodes the magnitude of a value. The two axes are usually categories or time buckets (day of week × hour, channel × month), and each cell's color shows the metric for that combination — darker or warmer meaning higher. It turns a dense table of numbers into an instantly scannable picture of where values are high and low.
When should I use a heat map?
Use one when you have a value measured across two dimensions and want to spot patterns, concentrations, or anomalies at a glance — peak activity hours, conversion by segment and device, correlation matrices. Heat maps shine when there are too many cells to read as numbers but the overall pattern matters. Avoid them when readers need exact values for every cell, since color is approximate; add labels in that case.
How do I choose colors for a heat map?
Match the scale to the data. For values from low to high, use a sequential scale (light → dark of one hue). For data that diverges around a meaningful midpoint (e.g. above/below target, positive/negative), use a diverging scale with a neutral center and two contrasting hues. Avoid rainbow scales — they create false boundaries and aren't perceptually uniform — and check the palette is colorblind-safe.
Heat map vs treemap — what's the difference?
A heat map arranges cells on a fixed grid defined by two axes and uses color for magnitude, so it's about a value across two dimensions. A treemap nests rectangles sized by value to show hierarchy and part-to-whole. Use a heat map for a matrix of one metric over two dimensions; use a treemap for hierarchical composition where size shows share of a total.
Should I add numbers to heat map cells?
Add them when precise values matter and the grid is small enough that labels fit without clutter — color then gives the at-a-glance pattern and the numbers give exactness. Omit them on large grids where labels would be unreadable, and rely on color plus a tooltip on hover. A clear legend mapping color to value is essential either way.

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Data Visualization

Mentioned in

Articles from the AdSights blog that discuss Heat Map.