Collecting effective user feedback for agent quality improvement involves a multi-faceted approach, primarily leveraging post-interaction surveys immediately after an interaction. These surveys often include CSAT or NPS questions, coupled with crucial open-ended comment fields allowing users to elaborate on their experience and provide specific details. The key is to directly link this feedback to individual agent interactions, enabling targeted coaching and performance reviews based on specific customer experiences and observed behaviors. Furthermore, AI-driven sentiment analysis of chat and call transcripts can identify common pain points and areas for improvement, even without explicit survey responses, by detecting emotional cues and frequently discussed topics. Regular analysis of these aggregated insights helps identify recurring training needs or systemic issues in agent knowledge or soft skills. This systematic approach ensures that feedback is not just collected, but actively used to foster continuous agent quality enhancement through iterative training and process refinements. More details: https://sunny-beach.biz/revive/www/delivery/ck.php?ct=1&oaparams=2__bannerid=24__zoneid=4__cb=08461ad063__oadest=https://infoguide.com.ua