Sentiment page in Assistant analytics
Summarize
Summary of Sentiment page in Assistant analytics
The Sentiment page in Assistant analytics empowers ServiceNow customers to analyze user sentiment through inferred customer satisfaction (CSAT) scores and various emotional factors from assistant conversations. By leveraging this data, organizations can improve user interactions and assistant performance.
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Key Features
- Overall Sentiment: Displays the average inferred CSAT score from analyzed conversations, enabling tracking of sentiment changes over time.
- Conversations Analyzed: Indicates the total number of conversations assessed for sentiment, providing a data-backed basis for CSAT scores.
- High Empathy Rate: Shows the percentage of conversations with high empathy, indicating the assistant's responsiveness to user emotions.
- Conversations with Negative Emotions: Highlights the percentage of conversations exhibiting confusion or frustration, helping to monitor and reduce negative interactions.
- Average Inferred CSAT Over Time: Tracks daily average CSAT scores, revealing trends in user sentiment.
- Transfers and Escalations Over Time: Monitors the frequency of conversations escalated to live agents, indicating the need for human intervention.
- Average Inferred CSAT (Virtual Agent & Live Agent): Benchmarks satisfaction scores for both Virtual Agent and live agent interactions, guiding performance improvements.
- Assistant Recommended Next Steps: Assesses the clarity of guidance provided to users, promoting enhanced user experience.
- Conversation Insight Inferred Resolution State: Evaluates whether user issues were resolved during interactions.
- Empathy Levels Distribution Over Time: Displays the distribution of empathy levels in responses, informing emotional intelligence assessments of the assistant.
- Negative Emotion Feedback Over Time: Tracks trends in negative emotional feedback, aiding in identifying and addressing user experience issues.
Key Outcomes
Using the Sentiment dashboard, ServiceNow customers can effectively monitor user satisfaction, enhance assistant behavior, and improve overall user experience. By understanding sentiment trends and emotional feedback, organizations can make targeted improvements to reduce negative interactions and foster better customer relationships.
Analyze user sentiment through customer satisfaction (inferred CSAT) score and CSAT factors such as empathy, frustration and confusion, transfers and escalations from conversations with assistants to improve the quality of user interactions.
- Monitor user satisfaction and sentiment trends to identify strengths and areas for improvement in assistant interactions.
- Track emotional feedback and empathy levels, enabling you to address frustration, confusion, and other negative emotions.
- Analyze conversation outcomes and recommended next steps to guide assistant optimization and enhance resolution rates.
- Overall Sentiment
- This area of the dashboard shows the overall average inferred CSAT score for analyzed conversations in the selected date range. The CSAT score is measured on a scale from 0 to 5, where 0 represents the lowest satisfaction and 5
represents the highest. Use this metric to track changes in sentiment over time and evaluate the impact of assistant updates.
Figure 2. Overall Sentiment - Conversations Analyzed
- This area of the dashboard shows the total number of conversations analyzed for sentiment in the selected date range. This number indicates the breadth of data supporting CSAT scores.
Figure 3. Conversations Analyzed - High Empathy Rate
- This area of the dashboard shows the percentage of conversations where high empathy was detected in assistant responses. It's calculated as ((Number of conversations with high empathy)/(Total number of conversations analyzed)) x
100. High empathy rate is an indication of the assistant's ability to respond with empathy to users queries.
Figure 4. High Empathy Rate - Conversations with Negative Emotions
- This area of the dashboard shows the percentage of conversations where negative emotional feedback in terms of confusion or frustration was detected. It's calculated as ((Number of conversations with Frustration or
Confusion)/(Total number of conversations analyzed)) x 100. This metric highlights the prevalence of negative experiences in assistant interactions. Use the metric to monitor and reduce negative emotion rates through targeted
assistant improvements.
Figure 5. Conversations with Negative Emotions - Average Inferred CSAT Over Time
- This area of the dashboard shows daily average of inferred CSAT scores in the selected data range. The CSAT scores are measured on a
scale from 0 to 5, where 0 represents the lowest satisfaction and 5 represents the highest. This chart highlights periods of improvement or decline in user sentiment.
Figure 6. Average Inferred CSAT Over Time - Transfers and Escalations Over Time
- This area of the dashboard tracks the number of conversations transferred or escalated to live agent. Hover over the trend line to view the number of conversations transferred or escalated to live agent on a given day. This chart
helps you with how often assistants require human intervention.
Figure 7. Transfers and Escalations Over Time - Average Inferred CSAT (Virtual Agent)
- This area of the dashboard shows the average Inferred CSAT score for Virtual Agent interactions in the selected period. For conversations involving both Virtual Agent and live agent, this score reflects only
the Virtual Agent CSAT. Scored on a 5-point scale, 0 = least satisfied and 5 = most satisfied. Use this metric to benchmark assistant performance and prioritize improvements where satisfaction is lowest.
Figure 8. Average Inferred CSAT (Virtual Agent) - Average Inferred CSAT (Live Agent)
- This area of the dashboard shows the average Inferred CSAT score for live agent interactions in the selected period. For conversations involving both Virtual Agent and live agent, this score reflects only the live agent CSAT.
Scored on a 5-point scale, 0 = least satisfied and 5 = most satisfied. Use this metric to benchmark assistant performance and prioritize improvements where satisfaction is lowest.
Figure 9. Average Inferred CSAT (Live Agent) - Average Inferred CSAT (Session)
- This area of the dashboard shows the average Inferred CSAT score for all interactions handled by Virtual Agent or a combination of Virtual Agent and live agent in the selected period. Scored on a 5-point scale, 0 = least satisfied
and 5 = most satisfied.
Figure 10. Average Inferred CSAT (Session) - Assistant Recommended Next Steps
- This area of the dashboard shows how clearly the assistant explained what happens next or what the user should do. Low: Conversations where no clear guidance was provided. Medium: Conversations where some guidance was provided.
High: Conversations where clear and complete guidance was provided.
Figure 11. Assistant Recommended Next Steps - Conversation Insight Inferred Resolution State
- This area of the dashboard shows the conversations where the user's issue was resolved. Yes: conversations where the assistant met the user's needs. No: conversations where the assistant didn't meet the user's needs. Unknown:
conversations where the resolution state couldn’t be determined.
Figure 12. Conversation Insight Inferred Resolution State - Empathy Levels Distribution Over Time
- This area of the dashboard shows the distribution of empathy levels (High, Medium, Low) in assistant responses in the selected date range. Use this chart to assess the emotional intelligence of assistant interactions and target improvements.
Figure 13. Empathy Levels Distribution Over Time - Negative Emotion Feedback Over Time
- This area of the dashboard tracks the feedback related to negative emotions: frustration and confusion in conversations with assistants. This chart helps you identify trends in negative user experiences and take necessary steps to
reduce the negative feedback.
Figure 14. Negative Emotion Feedback Over Time