A data analyst team needs to segment customers based on customer spending behavior. Given one million rows of data like the information in the following sales order table: Which of the following…

Standardization (e.g., z-score normalization) transforms data to have a common scale but does not directly segment or categorize data into groups like spending tiers.

Concatenation combines strings or fields, which is not relevant for segmenting numerical customer spending data.

Binning (or bucketing) is the process of grouping a range of continuous data into a smaller number of "bins" or intervals. For customer spending behavior, this means creating categories like "low spender," "medium spender," and "high spender" based on defined spending ranges, which is ideal for segmentation.

Appending combines rows of similarly structured datasets, which is for data consolidation, not for segmenting existing data within a single table.