Saved reply library audit: find duplicates, long replies and stock phrases
Saved replies pile up. Paste yours, separated by a line of three dashes, and see which to merge, shorten or reword.
Replies
3
Near-duplicate pairs
1
Over 120 words
0
With no placeholder
1
With stock phrases
1
Merge or delete: Refund approved and Refund done.
- Refund approved · 18 words
- Refund done · 18 words
- Password reset · 14 words · no placeholder · reword: kindly
Keep each reply short, give it a placeholder for the customer's name, and keep one version of each answer.
Questions
Why audit saved replies?
Libraries grow by adding. Over time they collect near-duplicates, very long replies and stock phrases that sound like a template. A short audit keeps them useful.
What counts as a near-duplicate?
Two replies that share at least 80 percent of their words. They usually say the same thing and can be merged into one.
Why flag replies with no placeholder?
A reply with no placeholder, such as the customer's name in curly or square brackets, is more likely to read as a form letter. It is a prompt to check, not an error.
How long should a saved reply be?
The tool flags replies over 120 words. Long replies are often two answers in one and are easier to use when split.
Is my text stored anywhere?
No. The audit runs in your browser and nothing is sent to a server.
Link to this page
Free to use and share. If it helped, a link back is the best thanks.
<a href="https://flatdeskfinal.vercel.app/free-tools/saved-reply-library-audit">Saved reply library audit</a>Guides that go with this tool
- How to write support macros (saved replies) that people actually useWhich answers to turn into macros, how to write them so they sound human, and how to keep them from going stale.
- How to write a support tone of voice guide your whole team can followA short tone of voice guide for support: how to sound, words to use and avoid, how to apologise, and how to keep human and AI replies consistent.
- Support customers in other languagesHow to support customers who write in languages your team does not speak: detect the language, use translation carefully and know when to bring in a person.
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