Self-Solve Performance page in Assistant analytics

  • Release version: Australia
  • Updated January 6, 2026
  • 3 minutes to read
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    Summary of Self-Solve Performance page in Assistant analytics

    The Self-Solve Performance page in Assistant analytics provides ServiceNow customers with comprehensive metrics to evaluate how effectively virtual assistants resolve user issues without involving live agents. This dashboard aggregates key data such as self-solved events, deflection rates, live agent transfers, user effort, and deflection outcomes. These insights enable you to monitor assistant success, understand trends, and optimize assistant behavior to reduce reliance on live support.

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    Key Features

    • Total Deflection Events: Measures the total user queries that had potential for deflection during a selected period, helping gauge the volume of deflection attempts.
    • Total Deflections: Shows the count of successfully deflected user queries where issues were resolved through AI agents, skills, topics, or positive user feedback.
    • Total Live Agent Transfers: Tracks the number and percentage change of conversations escalated to live agents, highlighting opportunities to improve assistant capabilities.
    • Deflection Rate: Calculates the percentage of interactions successfully deflected from live agent contact, serving as a benchmark for assistant effectiveness.
    • Deflection Rate Over Time: Displays deflection trends as a time series to help correlate assistant updates or external factors with performance changes.
    • Deflection Outcome Distribution: Breaks down deflection attempts by outcome categories—Resolved, Response Provided, No Response Provided, Not Resolved—to assess resolution success.
    • Deflection Types Offered: Illustrates the distribution of AI assets used for deflection such as catalog items or knowledge articles, aiding evaluation of deflection strategies.
    • Effort Score: Measures user effort levels (High, Medium, Low) during conversations, indicating the ease of the self-solve experience and areas needing friction reduction.

    Key Outcomes

    • Monitor how often users successfully resolve issues independently, reducing the need for live agent support.
    • Identify trends and periods of improvement or decline in assistant performance to inform targeted optimizations.
    • Analyze user effort and deflection outcomes to enhance the user experience and streamline self-service interactions.
    • Understand which AI assets are most effective for deflection and adjust assistant configuration accordingly.
    • Use live agent transfer data to pinpoint gaps in assistant capabilities and prioritize enhancements that minimize human intervention.

    Analyze self-solve and deflection metrics to measure how effectively assistants help users resolve issues without live agent intervention.

    The Self-Solve Performance dashboard page aggregates metrics related to self-solved events (instances where assistants resolved user queries), deflection rates, live agent transfers, and user effort. These metrics enable you to monitor resolution success, track deflection trends over time, and analyze the distribution of deflection outcomes. The insights from these metrics support targeted improvements to assistant behavior and help reduce the need for live agent support.

    Figure 1. Self-Solve Performance dashboard page in Assistant analytics
    A dashboard with score cards for key self-solve metrics, line charts tracking deflection rate and effort scores over time, and pie charts showing outcome and deflection type distributions
    The visualizations on the Self-Solve Performance page help you with the following.
    • Monitor self-solve deflection rates to identify how often users resolve issues without live agent assistance.
    • Track deflection trends over time to evaluate the impact of assistant updates and optimizations.
    • Analyze user effort scores and deflection outcome distributions to guide improvements in user experience.
    Total Deflection Events
    This area of the dashboard shows the total number of user queries in the selected date range that have the potential of being deflected. Use this metric to understand the overall volume of queries where deflection was attempted.
    Figure 2. Total Deflection Events
    A single score card displaying the count of events with potential for deflection. Includes a sparkline showing the trend and comparison to the previous period.
    Total Deflections
    This area of the dashboard shows the number of user queries successfully deflected by the assistant. This count indicates the number of interactions where deflection outcome was Resolved. The deflection can be attributed to AI agent, skill, topic execution or a positive feedback provided by the user.
    Figure 3. Total Deflections
    A single score card displaying the count of successfully resolved self-solve events. Includes a sparkline showing the trend and comparison to the previous period.
    Total Live Agent Transfers
    This area of the dashboard shows the number of conversations transferred to a live agent during the selected period. The percentage change from the previous period helps you track whether transfers are increasing or decreasing over time. Use this metric to identify opportunities to improve assistant capabilities and reduce the need for human intervention.
    Figure 4. Total Live Agent Transfers
    A single score card displaying the count of conversations transferred to live agents. Includes a sparkline showing the trend and percentage change from the previous period.
    Deflection Rate
    This area of the dashboard shows the overall percentage of interactions where users were successfully deflected from contacting a live agent. This is calculated using: ((Total number of self-solved events)/(Total number of events)) x 100.

    A higher deflection rate indicates that the assistant is effectively helping users find solutions independently. Use this metric to benchmark assistant performance and track improvements over time.

    Figure 5. Deflection Rate
    A single score card displaying the average deflection rate as a percentage. Includes a sparkline showing the trend across the selected date range.
    Deflection Rate Over Time
    This area of the dashboard tracks the deflection rate as a trend line across the selected date range. Hover over the trend line to view the deflection rate on a given day. This chart helps you identify periods of improvement or decline in deflection performance and correlate changes with assistant updates or external factors.
    Figure 6. Deflection Rate Over Time
    A line chart with the x-axis showing dates and the y-axis showing deflection rate percentage from 0% to 100%. A single trend line tracks deflection rate changes over time.
    Deflection Outcome Distribution
    This area of the dashboard shows the distribution of outcomes for deflection attempts. The chart displays breakdown of Resolved, Response Provided, No Response Provided events, helping you understand how often deflection attempts lead to successful resolution.
    • Resolved: conversations where at least one AI asset, for example, AI agent, skill, topic was executed or a positive feedback was provided by the user
    • Response Provided: conversations involving small talk or where the assistant provided a synthesized response with no further action from the user
    • No Response Provided: conversations where no response was provided by the assistant
    • Not Resolved: conversations where negative feedback was provided by the user or the query was escalated to a live agent
    Figure 7. Deflection Outcome Distribution
    A pie chart showing the proportion of self-solve outcomes. Each segment represents a different outcome category with its percentage of the total.

    Select a segment in the pie chart to drill down to the Deflection Details page and view conversations associated with that outcome. See Deflection details page in Assistant analytics for more information.

    Deflection Types Offered
    This area of the dashboard shows the distribution of AI assets offered as deflection methods to users. For example, catalog item, synthesized response, knowledge article, and so on. Use this chart to understand which deflection methods are being employed and evaluate their relative usage.
    Figure 8. Deflection Types Offered
    A pie chart showing the distribution of deflection methods offered to users. Each segment represents a deflection type with its percentage of the total.

    Select a segment in the pie chart to drill down to the Deflection Details page and view conversations associated with that deflection type. See Deflection details page in Assistant analytics for more information.

    Effort Score
    This area of the dashboard tracks user effort levels across conversations in the selected date range. Effort is categorized as High, Medium, or Low, indicating how much time and energy users needed to invest during the conversation to resolve their issues. Lower effort scores suggest a smoother self-service experience. Use this chart to help identify trends in user effort over time and prioritize improvements that reduce friction in the self-solve process. Effort score is based on the inferred Effort CSAT (customer satisfaction) factor. See Conversation Insights for more information.
    Figure 9. Effort Score
    A multi-series line chart tracking High, Medium, and Low effort levels over time. The x-axis shows dates; the y-axis shows conversation count.