Deflection Rate
A legacy metric measuring the percentage of customer inquiries that do not reach a human agent. While traditionally used as a proxy for automation success, deflection rate can be misleading because it doesn't indicate whether customer issues were actually resolved.
The Problem with Deflection-First Thinking
Deflection Rate focused purely on whether queries reached a human agent, assuming that if a customer didn't reach a human, their issue was resolved. But that's not always true. A high deflection rate could mean excellent automation—or it could mask customer dissatisfaction and eroded trust.
In the era of capable AI Agents, deflection alone doesn't tell you enough. What matters is whether the issue was resolved, regardless of who handled it. Modern AI Agents like Fin can resolve the majority of queries end-to-end, making resolution rate—not deflection rate—the more meaningful success metric.
Shifting from Deflection to Resolution
Teams scaling AI successfully are moving away from deflection-focused metrics toward resolution-first measurement. Instead of asking "Did we prevent this from reaching our team?" they ask "Did we actually solve the customer's problem?" This shift reflects a fundamental change in how support organizations think about AI—not as a deflection tool, but as a resolution engine.
Deflection rate can still be useful as an early signal, but it should never be the primary measure of success. Without resolution, deflection can actually harm customer experience by leaving issues unresolved and forcing customers to find alternative ways to get help.