We have 45 KPIs on User Support and Training in our database. KPIs serve as vital indicators for assessing the effectiveness of User Support and Training within Information Technology. By tracking metrics such as resolution time, ticket volume, customer satisfaction scores, and first contact resolution rates, organizations can gain insights into the performance and efficiency of their support teams.

For training, KPIs like course completion rates, post-training assessments, and real-world application of skills guide improvements in educational programs and materials. These indicators help ensure that users receive timely, competent assistance, and that training is effective and aligned with the actual needs of the users. Consequently, KPIs enable continuous improvement by identifying areas for investment and highlighting successful strategies, directly impacting user productivity and overall IT service quality.

KPI	Definition	Business Insights [?]	Measurement Approach	Standard Formula
Average After-Call Work Time More Details	The average time spent by support agents on follow-up tasks after a call has ended.	Helps in assessing the efficiency of agents and understanding ancillary tasks related to customer calls.	Time spent by agents on tasks related to the call after the call has ended, such as updating records or sending emails.	(Total Time Spent on After-Call Work / Total Number of Calls)
Trend Analysis [?] An increasing after-call work time may indicate more complex issues being handled by support agents. A decreasing after-call work time can signal improved efficiency in handling follow-up tasks or better training for support agents. Diagnostic Questions [?] What are the common types of follow-up tasks that contribute to the after-call work time? Are there specific areas or skills where support agents may need additional training to reduce after-call work time? Actionable Tips [?] Implement automated systems for documentation and follow-up tasks to reduce manual work for support agents. Provide ongoing training and resources for support agents to improve their efficiency in handling follow-up tasks. Analyze call data to identify patterns and trends that could help in streamlining follow-up processes. Visualization Suggestions [?] Line charts showing the average after-call work time over different time periods to identify trends. Stacked bar charts comparing after-call work time by support agent or team to identify variations and areas for improvement. Risk Warnings [?] High after-call work time can lead to longer resolution times and customer dissatisfaction. Chronic high after-call work time may indicate the need for process improvements or additional resources. Tools & Technologies [?] Customer relationship management (CRM) systems with integrated after-call work time tracking and task management features. Workflow automation tools to streamline follow-up processes and reduce manual effort. Integration Points [?] Integrate after-call work time data with customer satisfaction surveys to understand the impact of follow-up tasks on customer experience. Link after-call work time tracking with performance management systems to identify training and development needs for support agents. Change Impact [?] Reducing after-call work time can lead to faster resolution of customer issues and improved customer satisfaction. However, overly aggressive reduction targets may impact the quality of follow-up tasks and overall customer experience.
Average Handling Time (AHT) More Details	The average time it takes to resolve a support ticket or call, from initial contact to resolution.	Indicates overall efficiency of agents in resolving customer issues and managing their time.	The sum of talk time, hold time, and after-call work time divided by the number of calls handled.	(Total Talk Time + Total Hold Time + Total After-Call Work Time) / Total Number of Calls Handled
Trend Analysis [?] A decreasing AHT may indicate improved efficiency in resolving support issues or better-trained support staff. An increasing AHT could signal growing complexity of support tickets or a need for additional training or resources. Diagnostic Questions [?] Are there common reasons for prolonged resolution times, such as technical complexity or lack of expertise? How does our AHT compare with industry benchmarks or best practices? Actionable Tips [?] Invest in additional training for support staff to improve their technical skills and problem-solving abilities. Implement knowledge management systems to provide quick access to solutions for common issues. Regularly review and update support processes to streamline ticket resolution and reduce unnecessary delays. Visualization Suggestions [?] Line charts showing AHT over time to identify long-term trends and seasonal variations. Stacked bar charts to compare AHT across different support teams or types of issues. Risk Warnings [?] High AHT can lead to customer frustration and dissatisfaction, impacting overall customer experience. Consistently increasing AHT may indicate systemic issues that could affect the overall efficiency of the support function. Tools & Technologies [?] Customer relationship management (CRM) systems with built-in support ticket management and tracking capabilities. Performance analytics tools to identify patterns and bottlenecks in the support process. Integration Points [?] Integrate AHT tracking with customer feedback systems to understand the impact of support performance on customer satisfaction. Link AHT data with employee performance evaluations to identify training needs and recognize top performers. Change Impact [?] Reducing AHT can lead to improved customer satisfaction and loyalty, positively impacting long-term business performance. However, overly aggressive targets for AHT reduction may lead to rushed or incomplete resolutions, compromising support quality.
Average Time to Answer (ATA) More Details	The average time it takes for a support agent to answer a user's call.	Measures the accessibility of support and can highlight staffing or routing inefficiencies.	The average amount of time it takes for a call to be answered after it has been routed to an agent.	(Total Time Taken to Answer Calls / Total Number of Calls Answered)
Trend Analysis [?] An increasing average time to answer may indicate a growing backlog of user calls or a decrease in support agent efficiency. A decreasing average time to answer could signal improved support processes, increased staffing, or better training for support agents. Diagnostic Questions [?] Are there specific times of the day or week when the average time to answer is consistently higher? How does our average time to answer compare with industry benchmarks or customer expectations? Actionable Tips [?] Implement call routing and queuing systems to distribute calls more evenly among support agents. Provide additional training or resources to support agents to improve their efficiency and effectiveness in handling user calls. Regularly review and optimize support processes to identify and eliminate bottlenecks. Visualization Suggestions [?] Line charts showing the average time to answer over different time periods (e.g., daily, weekly, monthly). Bar graphs comparing the average time to answer across different support teams or individual support agents. Risk Warnings [?] A consistently high average time to answer can lead to user frustration and dissatisfaction. Long wait times may result in users seeking alternative support channels or solutions, impacting user retention and loyalty. Tools & Technologies [?] Call center software with real-time monitoring and reporting capabilities to track and analyze average time to answer. Customer relationship management (CRM) systems to capture and analyze user interactions and support history. Integration Points [?] Integrate average time to answer data with workforce management systems to optimize staffing levels based on call volume and patterns. Link support metrics with customer feedback and satisfaction data to understand the impact of average time to answer on user experience. Change Impact [?] Reducing the average time to answer can improve user satisfaction and loyalty, leading to higher customer lifetime value. However, increasing staffing or resources to decrease the average time to answer may impact operational costs and profitability.
KPI Library $189/year Navigate your organization to excellence with 17,288 KPIs at your fingertips. Subscribe to the KPI Library CORE BENEFITS 45 KPIs under User Support and Training 17,288 total KPIs (and growing) 360 total KPI groups 107 industry-specific KPI groups 12 attributes per KPI Full access (no viewing limits or restrictions) FlevyPro and Stream subscribers also receive access to the KPI Library. You can login to Flevy here.
Call Abandonment Rate More Details	The percentage of inbound support calls that are abandoned by the caller before reaching a support agent.	Reveals customer frustration or possible insufficiencies in call queue management.	The percentage of calls that are abandoned by the customer before speaking to an agent.	(Total Number of Abandoned Calls / Total Number of Calls Offered) * 100
Trend Analysis [?] An increasing call abandonment rate may indicate longer wait times or inadequate support staffing. A decreasing rate could signal improved call handling processes or better customer service satisfaction. Diagnostic Questions [?] Are there specific times of the day or week when call abandonment rates tend to be higher? What are the common reasons cited by callers for abandoning their support calls? Actionable Tips [?] Implement call routing and queuing systems to distribute calls more evenly among support agents. Provide self-service options for common support issues to reduce the volume of inbound calls. Regularly review and optimize staffing levels based on call volume patterns. Visualization Suggestions [?] Line charts showing call abandonment rates over different time periods (e.g., daily, weekly, monthly). Comparison bar charts to visualize call abandonment rates across different support channels or teams. Risk Warnings [?] High call abandonment rates can lead to customer frustration and dissatisfaction. Chronic call abandonment may indicate underlying issues in support processes or resource allocation. Tools & Technologies [?] Call center management software with real-time monitoring and reporting capabilities. Customer relationship management (CRM) systems to track customer interactions and support history. Integration Points [?] Integrate call abandonment rate data with customer satisfaction surveys to understand the impact on overall customer experience. Link support call data with workforce management systems to optimize staffing and scheduling. Change Impact [?] Reducing call abandonment rates can improve customer retention and loyalty. However, increasing support resources to lower abandonment rates may impact operational costs.
Call Transfer Rate More Details	The frequency with which calls are transferred from one agent to another or to a different support level.	Highlights potential training gaps or mismatches in call routing.	The percentage of calls that are transferred from the initial agent to another agent or department.	(Total Number of Transferred Calls / Total Number of Calls Handled) * 100
Trend Analysis [?] An increasing call transfer rate may indicate a lack of training or knowledge among support agents. A decreasing rate could signal improved agent efficiency or better routing of calls to the appropriate support level. Diagnostic Questions [?] Are there specific types of calls that are frequently being transferred? How does our call transfer rate compare with industry benchmarks or best practices? Actionable Tips [?] Provide additional training and resources for support agents to handle a wider range of issues without transferring calls. Implement call routing and escalation protocols to ensure calls are directed to the most qualified agent or support level from the start. Regularly review and update knowledge bases and support documentation to address common issues that lead to call transfers. Visualization Suggestions [?] Line charts showing the trend of call transfer rates over time. Pie charts to visualize the distribution of transferred calls by issue type or support level. Risk Warnings [?] High call transfer rates can lead to customer frustration and dissatisfaction. Frequent call transfers may indicate a need for process improvements or additional resources in specific areas of support. Tools & Technologies [?] Call center software with reporting and analytics capabilities to track call transfer rates and patterns. Customer relationship management (CRM) systems to capture and analyze customer interactions that result in call transfers. Integration Points [?] Integrate call transfer rate data with customer satisfaction surveys to understand the impact of transfers on customer experience. Link call transfer metrics with workforce management systems to optimize staffing and training based on transfer patterns. Change Impact [?] Reducing call transfer rates can improve customer satisfaction and retention, leading to long-term business growth. However, changes in call transfer protocols may require adjustments in agent workflows and training, impacting short-term productivity.
Change Request Fulfillment Time More Details	The time taken to implement a change requested by a user in the IT system or services.	Assesses the responsiveness and effectiveness of change management processes.	The time taken to complete a change request from the time it was submitted.	(Total Time to Complete Change Requests / Total Number of Change Requests Completed)
Trend Analysis [?] Increasing change request fulfillment time may indicate resource constraints or inefficient change management processes. Decreasing time can signal improved agility in responding to user needs or better automation of change implementation. Diagnostic Questions [?] Are there recurring types of changes that consistently take longer to fulfill? How does our change request fulfillment time compare with industry benchmarks or best practices? Actionable Tips [?] Implement automated change management tools to streamline and expedite the fulfillment process. Regularly review and optimize change management workflows to identify and eliminate bottlenecks. Invest in training and upskilling for IT staff to improve their ability to implement changes efficiently. Visualization Suggestions [?] Line charts showing change request fulfillment time over different time periods to identify trends. Stacked bar charts comparing fulfillment time for different types of changes to pinpoint areas for improvement. Risk Warnings [?] Extended fulfillment time can lead to user frustration and decreased confidence in IT services. Consistently long fulfillment times may indicate underlying issues in IT infrastructure or resource allocation. Tools & Technologies [?] Change management software like ServiceNow or Jira to track and manage change requests more effectively. Performance monitoring tools to identify and address bottlenecks in the change fulfillment process. Integration Points [?] Integrate change request fulfillment time with incident management systems to understand the impact of changes on system stability. Link with project management tools to align change implementation with broader IT initiatives and priorities. Change Impact [?] Reducing change request fulfillment time can enhance user satisfaction and productivity, leading to improved overall IT service quality. However, overly aggressive reduction efforts may compromise the thoroughness and quality of change implementation, potentially leading to system instability or errors.

Types of User Support and Training KPIs

We can categorize User Support and Training KPIs into the following types:

Operational Efficiency KPIs

Operational Efficiency KPIs measure the effectiveness and productivity of user support and training operations. These KPIs help identify areas where processes can be streamlined or resources can be optimized. When selecting these KPIs, ensure they align with the organization's broader operational goals and consider the scalability of the metrics. Examples include Average Resolution Time and First Contact Resolution Rate.

User Satisfaction KPIs

User Satisfaction KPIs gauge the level of satisfaction and experience users have with support and training services. These metrics are crucial for understanding user sentiment and identifying areas for improvement. Choose KPIs that provide actionable insights and can be measured consistently over time. Examples include Net Promoter Score (NPS) and Customer Satisfaction Score (CSAT).

Training Effectiveness KPIs

Training Effectiveness KPIs assess the impact and quality of training programs on user performance and knowledge retention. These KPIs help determine if training initiatives are meeting their objectives and delivering value. Select KPIs that can be directly linked to performance improvements and business outcomes. Examples include Training Completion Rate and Post-Training Assessment Scores.

Resource Utilization KPIs

Resource Utilization KPIs track the allocation and usage of resources within the user support and training functions. These metrics are vital for ensuring that resources are used efficiently and effectively. Focus on KPIs that highlight both overutilization and underutilization to optimize resource allocation. Examples include Support Staff Utilization Rate and Training Resource Utilization.

Cost Management KPIs

Cost Management KPIs monitor the financial aspects of user support and training operations. These KPIs help in managing budgets, reducing costs, and improving financial efficiency. Prioritize KPIs that provide clear visibility into cost drivers and enable proactive cost control. Examples include Cost Per Ticket and Training Cost Per Employee.

Acquiring and Analyzing User Support and Training KPI Data

Organizations typically rely on a mix of internal and external sources to gather data for User Support and Training KPIs. Internal sources include helpdesk software, learning management systems (LMS), and customer feedback tools, which provide detailed analytics on metrics like Average Resolution Time and Training Completion Rate. External sources such as industry benchmarks and market research reports from firms like Gartner and Forrester offer valuable context and comparative data.

Once data is acquired, the next step is analysis. Advanced analytics tools and techniques, including data visualization software and machine learning algorithms, can uncover patterns and insights that might not be immediately apparent. For example, using predictive analytics can help forecast future support demand based on historical data, enabling better resource planning.

According to a McKinsey report, organizations that leverage advanced analytics in their support functions can achieve up to a 30% increase in efficiency. This underscores the importance of not just collecting data but also investing in robust analytics capabilities. Regularly reviewing and updating KPIs based on these insights ensures they remain relevant and aligned with organizational goals.

Moreover, integrating data from various sources into a centralized dashboard can provide a holistic view of performance. This facilitates real-time monitoring and quicker decision-making. For instance, a sudden spike in Average Resolution Time can be immediately flagged, prompting a deeper investigation into potential causes such as increased ticket volume or staffing issues.

In summary, acquiring and analyzing User Support and Training KPIs involves a combination of internal data sources, external benchmarks, and advanced analytics tools. By doing so, organizations can gain actionable insights that drive continuous improvement and operational excellence.

KPI Library

$189/year

Navigate your organization to excellence with 17,288 KPIs at your fingertips.

Subscribe to the KPI Library

CORE BENEFITS

• 45 KPIs under User Support and Training
• 17,288 total KPIs (and growing)
• 360 total KPI groups

• 107 industry-specific KPI groups
• 12 attributes per KPI
• Full access (no viewing limits or restrictions)

FlevyPro and Stream subscribers also receive access to the KPI Library. You can login to Flevy here.

FAQs on User Support and Training KPIs

What are the most important KPIs for measuring user support effectiveness?

The most important KPIs for measuring user support effectiveness include Average Resolution Time, First Contact Resolution Rate, and Customer Satisfaction Score (CSAT). These metrics provide insights into how efficiently and effectively user issues are being resolved.

How can I measure the effectiveness of my training programs?

Measure the effectiveness of training programs using KPIs such as Training Completion Rate, Post-Training Assessment Scores, and Knowledge Retention Rate. These metrics help evaluate the impact of training on user performance and knowledge retention.

What sources can I use to gather data for User Support and Training KPIs?

Sources for gathering data include internal systems like helpdesk software and learning management systems (LMS), as well as external benchmarks and market research reports from firms like Gartner and Forrester. These sources provide comprehensive data for KPI measurement.

How often should I review and update my KPIs?

Review and update your KPIs at least quarterly to ensure they remain aligned with organizational goals and reflect any changes in operational priorities. Regular updates help maintain the relevance and accuracy of the KPIs.

What tools can I use for analyzing User Support and Training KPIs?

Use advanced analytics tools such as data visualization software, machine learning algorithms, and centralized dashboards. These tools help uncover patterns, provide real-time monitoring, and facilitate quicker decision-making.

How do I ensure my KPIs are actionable?

Ensure your KPIs are actionable by selecting metrics that provide clear insights into performance and can be directly linked to specific actions or improvements. Regularly review and refine KPIs based on feedback and changing business needs.

What role do external benchmarks play in KPI management?

External benchmarks provide valuable context and comparative data, helping organizations understand how their performance stacks up against industry standards. This information can guide goal-setting and performance improvement initiatives.

How can predictive analytics enhance KPI management?

Predictive analytics can enhance KPI management by forecasting future trends and identifying potential issues before they arise. This proactive approach enables better resource planning and more effective decision-making.

KPI Library

$189/year

Navigate your organization to excellence with 17,288 KPIs at your fingertips.

Subscribe to the KPI Library

CORE BENEFITS

• 45 KPIs under User Support and Training
• 17,288 total KPIs (and growing)
• 360 total KPI groups

• 107 industry-specific KPI groups
• 12 attributes per KPI
• Full access (no viewing limits or restrictions)

FlevyPro and Stream subscribers also receive access to the KPI Library. You can login to Flevy here.

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