Deflection rate
Deflection rate is the percentage of user questions resolved by self-service, such as a docs AI chatbot, that would otherwise have become support tickets.
A basic estimate divides self-service conversations without a follow-on ticket by total conversations. Treat that figure as directional: users can abandon after a poor answer, so reliable measurement also needs feedback or linked support outcomes.
The hard part is attribution. A user who got a wrong answer and gave up counts as deflected under naive measurement. Better measures combine conversation outcomes, user feedback, and whether the same user opened a ticket afterwards.
Deflection rate is a lagging metric; the leading inputs are answer accuracy and docs coverage. Unanswered questions help you investigate coverage, retrieval, or configuration problems. Review the conversation and relevant docs before deciding what to fix.
Frequently asked questions
What is a good deflection rate for a docs chatbot?
There is no single target for every product. Track your own rate over time alongside answer feedback and follow-on tickets. Investigate questions that still needed support.
Does a high deflection rate always mean the chatbot works?
No. Users who abandon after a bad answer look deflected. Pair the rate with answer feedback and follow-on ticket tracking before trusting it.