whatagraph-custom-dimensions

You can group marketing data fields by creating derived custom dimensions that apply condition-based bucketing to existing fields. This enables you to categorize campaign data into custom groups like Branded or Non-branded for unified reporting.

Normalizing inconsistent naming conventions is achieved by creating derived dimensions that alias existing marketing data fields. This maps varied source names into a unified format, ensuring consistent cross-channel reporting and accurate data categorization.

Yes, you can use AI-driven classification within custom dimensions to automatically categorize campaign names into specific intent buckets like Brand, Generic, or Display. This automates data labeling for complex marketing analytics reporting.

Yes, creating custom dimensions requires precise field ID resolution via source discovery tools. Accurate field IDs ensure metadata mapping, manual source tagging, and data transformations apply correctly across connected marketing channels and reports.

Use condition-based data bucketing to automatically group fields matching specific criteria like campaign name keywords. Use manual source tagging when you need to explicitly assign metadata labels to individual data fields that do not fit automated rules.

Custom dimensions may fail to transform data correctly across channels if there is inaccurate field ID resolution during metadata mapping. You must use source discovery tools to verify exact field IDs before applying condition-based bucketing or AI classification.