Detect interactions where the customer's issue remained unresolved at the end of the interaction.
The evaluation focuses on whether unresolved at interaction end during the interaction.
Customer intent is often expressed indirectly, buried in the flow of a conversation that is ostensibly about something else. A billing inquiry becomes a churn signal. A support call reveals an upsell opportunity. These moments are invisible in traditional QA because scorecards are not designed to look for them.
Detecting issue unresolved across your entire interaction volume transforms what used to be anecdotal observations into measurable intelligence. Operations leaders can see trends, segment by customer type or agent team, and connect conversation signals to business outcomes.
Compass analyzes the full context of the conversation to determine whether issue unresolved occurred. This is not keyword matching or phrase detection. The evaluation considers meaning, sequence, and conversational dynamics to distinguish genuine instances from surface-level similarities.
This signal also includes context-aware logic that suppresses false positives in situations where the surface-level language does not reflect actual intent. The same words can mean very different things depending on the type of interaction and who initiated it.
Operations leaders use this signal to understand what customers are actually telling them, at scale. Individual interactions become data points in a larger picture of customer needs, friction, and intent.
Retention and CX teams use it to trigger proactive workflows. When the signal fires, it can inform follow-up actions, routing decisions, or escalation paths, turning a reactive service model into a responsive one.
Product and strategy teams use aggregated signal data to identify systemic issues. If this signal spikes for a particular product, segment, or time period, it usually means something changed upstream that needs attention.
This signal is part of Chordia’s Signal Intelligence capabilities.
