Custom dimensions are user-defined attributes created to categorize or segment data beyond standard fields. They allow businesses to analyze data using logic specific to their needs.

Examples include:

• Grouping countries into custom regions

• Classifying customers into segments

• Labeling products by lifecycle stage

• Creating marketing channel groupings

Custom dimensions are commonly created in BI tools, semantic layers, or data models using conditional logic.

From a business perspective, custom dimensions make analytics more relevant. Standard dimensions rarely match how businesses think about their operations.

However, custom dimensions require governance. If each team defines segments differently, results become inconsistent. Mature organizations centralize custom dimensions in data models or semantic layers.

Custom dimensions are especially useful in:

• Cohort analysis

• Funnel reporting

• Attribution modeling

• Product analytics

They allow teams to align analytics with real-world business logic.