Re-organize your tickets, build instant reports, and export it — all in a risk-free environment
We’ve learned from customer success teams that tagging is critical for turning heaps of conversations into prioritized action across the company. But it’s also hard. In a previous post, we went over some of the ways things go wrong.
Today we’re announcing the first step of our solution: a tag management layer that takes the pressure off of designing a masterpiece tag taxonomy, tagging conversations perfectly as they happen, or anticipating future needs far in advance. What if you could…
- rename, merge, and split tags with a few clicks
- organize tags into groups that are understood by search and visualization
- bulk edit tagged conversations based on search or AI suggestions — or even enable full auto-tagging on a tag-by-tag basis
- … all in a safe sandbox where you can’t accidentally corrupt your source data
Our goal is for Frame AI's tag management layer to change the way you think about tagging, reporting, and acting on your conversations — turning an impossible chore into the place where you feel you can leverage all of your hard work communicating with customers to solve bigger problems.
Here’s how we help a few common scenarios:
1. Modify Existing Tags
You’re staring at years of “tag debt, and that new schema you dreamed up 14 months ago still hasn’t been implemented. If only you could freeze time, update all your data, and ask your reps to perfectly tag everything going forward!
Rename tags and merge similar tags together. Hide tags that are irrelevant for current reporting. Or hide all your tags, create a new schema, and bulk-edit your historical data to apply them.
2. Organize Tags Into Groups
Within your existing tag hierarchy, you use a broad “catch-all” tag, like “Product,” followed by sub-tags that specify which product it’s about. This adds noise to your tag-specific reporting.
Create a group called “Product,” put all the sub-tags into it, and hide the catch-all “Product” tag from reports. As a bonus, you can search on groups of tags with the syntax
tag.group:Product and use groups to interact with visualizations, making it easier to explore your data and create exact reporting views.
3. Bulk Edit / Auto Tag
You’ve found a bunch of conversations that weren’t tagged properly but are now closed/resolved, so you export everything to a spreadsheet and re-tag it manually.
Quickly highlight one to 100,000 conversations and apply or remove the desired tag(s). You can find these conversations manually or with Frame’s neural tagging help, which will suggest conversations that look like they match a particular tag. (It only takes about 20 tagged conversations for a tag’s neural model to become helpful.)
For ultimate assistance, turn on auto-tagging for a given tag, and let Frame AI automatically find and update matching conversations as they arrive.
4. Create New Tags
You or your boss has come up with something new they want to have lots of information about. If only you had been tagging the conversations this way all along!
In Frame AI, create a new tag, then bulk edit any set of conversations you can find as described above. Once you’ve tagged a few, turn on a neural model and let Frame find more similar conversations to bulk edit. With a few minutes, you’ve conducted a research effort that can become a permanent part of your process or undone easily by deleting the tag.
Back up: what’s the context here?
The Frame AI hub is a full-stack solution for unifying, understanding, and acting on your customer conversations happening in other systems. Whereas your product and marketing teams have tools like Segment and Heap to make data-driven decisions and workflows about application events, Frame brings your conversation data to the same level.
Tag management is the critical gateway between exploring the existing, unmodified data and using re-organized, enriched data. Changes you make in Frame affect how all historical and future data render in the reporting UI and in data exports.
Here’s an overview of the stack and where tag management fits in.
- Connect your existing data sources (1-click login or custom integration)
- Backfill all historical tickets
- Subscribe to new messages
- Normalize and unify data for each contact and organization
- Explore existing data (search/browse UI)
→ Manage tags (clean up and re-map tag schema)
- Enrich conversations (manual and auto-tagging)
- Annotate conversations (sentiment moments)
- Manual export (CSV)
- Automatic sync (push all data data warehouse)
- Triggered push (push tag or sentiment events to webhook)
Understanding your own data with Frame AI is easy. Just sign up, connect your existing ticketing software, and sit back as we backfill and clean up your data.