Which technique should be used to segment customers based on spending behavior in a dataset with a million rows?

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Segmenting customers by spending behavior is about turning a continuous spending amount into discrete groups that you can compare and analyze. Binning does exactly this by partitioning spending values into defined intervals (for example, low, medium, high spenders) and placing each customer into the appropriate bin. This makes it easy to summarize and compare segments, identify patterns, and tailor strategies to each group. In a dataset with a million rows, binning is also efficient and helps manage skew or extreme values by grouping them into sensible spending ranges. Standardization scales a feature for modeling rather than creating spend-based segments, while concatenation and appending are about combining data, not forming segments.