Analyze the overall usage and adoption trends across all Customer Service
Representative (CSR) groups to identify key areas of success and opportunity. Assess service
efficiency by reviewing overall average handle time (AHT) and monitor quality by tracking the
overall employee satisfaction score (ESAT) and response quality metrics. For example, a service
manager can find patterns that explain fluctuations in agent efficiency and customer
dissatisfaction. Filter by overall AHT, negative feedback use reasons, and AHT comparison trend
to isolate reasons for extended handle times or negative feedback. Implement targeted
interventions, such as coaching on complex case types or modifying the AI suggestions. These
actions address the underlying issues, and also enhance customer sentiment.
Required Editions
| Available in: Lightning Experience |
| Available in: Enterprise and Unlimited Editions with Agentforce 1
Service Edition |
Learn about the key metrics on the KPIs tab.
| METRIC |
DESCRIPTION |
| Overall Usage |
Shows the total number of cases where agents used the selected AI product. Use
this metric to understand the volume of platform adoption. |
| As-is Usage |
Shows the percentage of AI recommendations that users accepted without making any
edits. Use this metric to evaluate the accuracy and quality of the AI model. |
| Overall AHT |
Shows the average handle time for all service cases within the selected period.
Use this metric to track key efficiency metrics across the service center. |
| Overall ESAT |
Shows the employee satisfaction score calculated from direct agent feedback
(thumbs up or down). Use this metric to measure the quality and effectiveness of
AI-driven services. |
| AHT Comparison |
Shows the average handle time for cases resolved with AI assistance compared to
those resolved. Use this metric to quantify the operational efficiency gained by using
Einstein for Service. |
| AHT Comparison Trend |
Shows the historical change in average handle time for both AI-assisted and
non-assisted cases over time. Use this metric to monitor long-term efficiency shifts
and validate ROI. |
| Feedback Distribution |
Shows the ratio of positive versus negative agent feedback. Use this metric to
evaluate the overall sentiment and satisfaction of your team with AI
recommendations. |
| Feedback Trend |
Shows the volume of positive and negative feedback over a selected period. Use
this metric to track how agent sentiment evolves in response to model updates or
process changes. |
| Negative Feedback Reasons |
Shows the reasons agents cited for rejecting AI suggestions. Use this metric to
prioritize and resolve the root causes of dissatisfaction. |
