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 logged by the assistant show exactly where coverage is missing.
Frequently asked questions
What is a good deflection rate for a docs chatbot?
It varies with product complexity and docs quality, so benchmarks mislead. Track your own trend over time and investigate the conversations that still ended in escalation.
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.