Quality Managers and Supervisors derive actionable insights from the AI-generated quality scores that help them understand the reasons behind agent performance ratings. This enables them to identify areas for coaching and performance improvement. Additionally, agents can use these insights, along with AI-generated corrective actions and recommendations, to address their weak areas.

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Understanding AI Insights

To explore detailed insights behind the AI-generated quality scores, refer to the following points:

1. AI Score Breakdown Widget
   In the AI Score Breakdown widget, click on the AI score category. This displays the distribution of AI scores across different parameters, such as:

   • Grammar

   • Agent Introduction

   • Tonality

   • Empathy

   • Feedback

   • And more

2. Detailed Explanations
   In-depth explanations of how each parameter is evaluated, along with practical examples of behaviors that contribute to good performance are found here. For instance:

   • Courtesy: Learn which phrases are perceived as courteous by AI and see examples to emulate.

   • Grammar: Gain insights into grammatical mistakes made by agents, with specific corrections to guide coaching.

   • Empathy: Discover which phrases customers find empathetic and what makes a strong opening and closing statement.

3. AI Score Colors in AI Breakdown Widget

   Currently, the color representation for scores in the AI Breakdown widget is predefined and not configurable:

   • Quality Score 0–40: Red

   • Quality Score 41–70: Yellow

   • Quality Score 71–100: Green

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Using the AI Recommendations

Agents can use the detailed corrective actions and AI-generated recommendations to work on their weaknesses. By understanding what constitutes success in each parameter, agents can improve their performance, leading to higher customer satisfaction and quality scores.

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Once the checklist has been configured, the next step is to set up AI Scoring and begin generating scores for agent interactions. In this stage, you will define the scoring rule, apply it to real cases, and review the AI-generated evaluations. This enables the system to assess conversations based on the checklist logic you have established.

After scores are generated, it is important to interpret the resulting insights. The platform provides detailed outputs that help you understand the rationale behind each score and analyze agent performance effectively.

Additionally, you can provide feedback on the AI-generated results. This feedback plays a crucial role in improving scoring accuracy and can be used to refine and optimize checklist rules over time.

For more details on enabling scoring, analyzing AI insights, and submitting feedback refer, AI Scoring and Insights.