What does data segmentation refer to?

Data segmentation refers to the practice of dividing a dataset into smaller groups based on specific criteria, allowing for more targeted analysis and insights. This process enables analysts to identify patterns, trends, and behaviors within distinct groups, which can be crucial for making informed business decisions. By segmenting data, organizations can leverage more granular insights tailored to particular consumer demographics, geographic locations, or other relevant characteristics. This enhances the ability to understand varied customer needs and improves the effectiveness of strategies based on those insights.

In contrast, merging datasets involves combining two or more datasets into a single one, which does not involve dividing them. Eliminating outlier data points pertains to the removal of data that deviates significantly from the rest of the dataset, which is a different focus entirely. Aggregating data typically refers to summarizing or consolidating it to provide a broader view, rather than breaking it down into more detailed components. Each of these processes serves distinct purposes in data analysis, but segmentation specifically emphasizes the importance of understanding individual groups within a larger dataset.