Auto-tagging is a powerful way to discover new conversations and continually enrich your data.

With auto-tagging enabled, Frame will automatically apply the tag to conversations which match its trained model.

Candidates for Auto-Tagging

Frame's success team can assist you in training auto-tagging models. In general, here are some characteristics of tags that are ready for auto-tagging:

What to Look For	What to Avoid
The tag comprises records which have language patterns directly observable in the messages themselves.	- The tag represents a higher-level intent or product category that cannot be inferred from the message text at all. - The tag represents the inbound channel (e.g., chat/email), not the contents of the message. - The tag represents an aspect of the speaker's identity (e.g., "VIP"), not the contents of the message.
The tag is specific enough to be associated with message text that is in some way similar across records.	The tag is a "catch-all" for any kind of activity, such as a tag called "Support".