Leverage different perspectives of your RFM segments to optimize your decision making.

These visualizations represent the amount of customers present in each of your segments: we name them treemaps. They represent a data hierarchy between nested branches represented as rectangles. The wider a rectangle is, the more important it is relative to the others.

This treemap is a view of the "Recency x Frequency" segments occurring in your dataset, independently from their monetary value.

This treemap is a view of the "Recency x Frequency" segments occurring in your dataset, depending on their respective monetary value. Thus, the first blocks represent the amount of customers having a specific monetary value: their relative surfaces let you see which segments are the most represented in your segmentation.

Each of these treemaps is a view, for a specific monetary value, of the "Recency x Frequency" segments occurring in your dataset.

The first one will let you see easily which monetary value segments are the most represented in your dataset, but it will be hard to compare the "Recency x Frequency" segments present in each of these monetary value segments.
The second one will let you easily compare the "Recency x Frequency" segments present in each of these monetary value segments, but it won't be possible to have any insight about your monetary value segments relative importance.