Suite Metrics

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Division, Direction

Supported channel: Omnichannel

Metric type: Historical

Use Case: Monitoring Call Abandonment Rate- A contact center manager notices a high abandonment rate during peak hours. By analyzing this metric, the manager identifies long wait times as the cause. To address this, the manager:

• Increases staffing during peak hours.

• Offers call-back options to reduce hold times.

• Enhances the IVR system for better self-service.

These actions help reduce the abandonment rate, improve customer satisfaction, and ensure more calls are answered promptly.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Up, a higher metric value is best.

Use case: Optimizing Agent Utilization for Better Performance- In a contact center that provides customer support for a financial services company, the Agent Active Time Ratio metric can be used to monitor and optimize agent utilization. For example, if the metric shows a low ratio, it may indicate that agents are spending a significant amount of their available time not handling calls, possibly due to inefficiencies or distractions. The contact center can investigate potential causes such as scheduling issues, agent training, or process bottlenecks. By addressing these issues, the contact center can improve agent utilization, ensuring that agents are effectively managing their time and providing adequate support to customers. Additionally, this metric can help in setting benchmarks and goals for optimal agent utilization, contributing to better overall performance and customer satisfaction.

Calculation: This metric represents the ratio of the total time agents spend on after-call work (ACW) to the duration of contact handling by the agent within a specified period. Essentially, it indicates the proportion of time agents spend on post-call tasks relative to their overall contact handling time.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use case: Optimizing After-Call Work for Improved Efficiency- In a contact center that provides customer support for a healthcare provider, the After-Call Work (ACW) Ratio metric can be used to monitor and optimize the time agents spend on post-call activities. For example, if the metric shows a high ACW ratio, it may indicate that agents are spending a lot of their time on after-call tasks, which could be due to complex documentation requirements or inefficient processes. The contact center can investigate potential causes such as the need for better training, streamlined processes, or improved tools. By addressing these issues, the contact center can reduce the time agents spend on after-call work, ensuring that they are available to handle more calls and improve overall efficiency. Additionally, this metric can help in setting benchmarks and goals for optimal after-call work time, contributing to better overall performance and customer satisfaction.

Calculation: This metric represents the ratio of the total time agents are available to handle calls to the total duration of their sessions within a specified period. Essentially, it indicates how much of their session time agents are available to handle calls.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use case: Maximizing Agent Availability for Improved Service Levels- In a contact center that provides customer support for a financial services company, the Agent Availability Ratio metric can be used to monitor and optimize agent availability. For example, if the metric shows a low availability ratio, it may indicate that agents are spending a significant portion of their session time unavailable to handle calls, possibly due to breaks, meetings, or other non-call-related activities. The contact center can investigate potential causes such as scheduling inefficiencies, high workload, or insufficient staffing. By addressing these issues, the contact center can improve agent availability, ensuring that agents are ready to handle calls more often and provide timely support to customers. Additionally, this metric can help in setting benchmarks and goals for optimal agent availability, contributing to better overall performance and customer satisfaction.

Calculation: This metric represents system-expired contacts divided by all contacts, shown as a percentage.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use case: Tracking Expired Abandon Rate - A contact center can use this metric to track the percentage of contacts that the system times out before an agent can assist them. This helps flag potential capacity or routing issues.

Calculation: This metric calculates the percentage of time agents spend handling customer interactions relative to their total session duration. This metric provides insight into how efficiently agents are utilizing their available time for customer interactions.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Up, a higher metric value is best.

Use case: Evaluating Agent Efficiency - A contact center uses the % Handle Time metric to evaluate agent efficiency. By understanding the amount of time agents spend on customer interactions compared to their total logged-in time, management can identify areas for improvement and ensure agents are effectively using their time. This helps in optimizing workforce management and enhancing overall productivity

Calculation: This metric calculates the percentage of unique contacts handled by agents out of the total unique contacts.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Up, a higher metric value is best.

Use case: Measuring Agent Handling Efficiency- A contact center uses this metric to measure the efficiency of agents in handling customer interactions. By understanding the proportion of unique contacts handled by agents, management can assess how effectively agents are managing their workload. This helps in identifying areas for improvement and ensuring that agents are adequately trained to handle a diverse range of customer inquiries.

Calculation: This metric calculates the percentage of time contacts spend in queue relative to the total duration of contact handling. This metric helps to understand how much time contacts spend waiting in the queue compared to the overall handling time.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use case: Analyzing Queue Efficiency- A contact center uses this metric to analyze the efficiency of its queue management. By understanding the proportion of time contacts spend in the queue, management can identify bottlenecks and implement strategies to reduce wait times, thereby improving customer satisfaction and operational efficiency.

Calculation: This metric calculates the long abandons divided by all inbound contacts, shown as a percentage. A long-abandoned contact is one where the customer hung up after waiting for a significant amount of time without being connected to an agent.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Division, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use case: Reducing Long Abandonment Rates- A contact center uses this metric to monitor and reduce the rate of long-abandoned inbound contacts. By understanding the proportion of calls where customers hang up after waiting too long, management can identify inefficiencies in call handling and implement strategies to improve response times. This helps in enhancing customer satisfaction and reducing the likelihood of losing customers due to long wait times.

Calculation: This metric represents the outbound abandons divided by all contacts, shown as a percentage.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Division, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use case: Tracking Outbound Abandon Rate- In a contact center, use this metric to track the percentage of outbound contacts that are abandoned compared to the total number of contacts. Tracking this percentage over time helps identify trends and measure the impact of changes to outbound campaign strategies.

Calculation: This metric represents the on-hold abandons divided by all contacts, shown as a percentage.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Division, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use Case: Optimizing Held Party Abandon Rate - A contact center uses this metric to measure the share of customers who hang up while on hold, which serves as a key indicator of hold-handling quality. By monitoring this rate, management can assess the effectiveness of agent interactions during hold periods and identify issues such as excessive wait times or poor communication.

Calculation: This metric calculates the prequeue abandons divided by all inbound contacts, shown as a percentage. A prequeue abandoned contact is one where the customer hung up before being placed in the queue.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Division, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use case: Reducing Prequeue Abandonment Rates - A contact center uses this metric to monitor and reduce the rate of prequeue abandoned inbound contacts. By understanding the number of calls where customers hang up before entering the queue, management can identify inefficiencies in the initial call handling process and implement strategies to improve the customer experience, such as optimizing IVR menus or providing better initial information. This helps in enhancing customer satisfaction and reducing the likelihood of losing customers due to early abandonment.

Calculation: This metric calculates the percentage of time contacts spend in the pre-queue phase relative to the total contact handling time for legacy channels like phone call, SMS, Emails etc. This metric helps understand how much time contacts spend before being placed in the queue compared to the overall handling time.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use case: Analyzing the Prequeue Time Percentage - A contact center uses this metric to analyze the efficiency of its prequeue management for legacy channels like voice, emails, SMS etc. By understanding the proportion of time contacts spend in the prequeue phase, management can identify bottlenecks and implement strategies to reduce prequeue times, thereby improving customer satisfaction and operational efficiency.

Calculation: This metrics represents the percentage of inbound calls that spend time in the queue. It helps identify how often callers are waiting before being connected to an agent.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use case: Monitoring Queue Rate- A contact center manager notices that many inbound calls are spending time in the queue. By analyzing the queue rate, the manager identifies peak times with high queue rates and takes action to:

• Optimize staffing levels during busy periods.

• Implement advanced call routing to distribute calls more efficiently.

• Enhance self-service options in the IVR system.

These measures help reduce the queue rate, improve customer experience, and ensure calls are handled promptly.

Calculation: This metrics measures the proportion of time agents spend refusing calls compared to the total time they are unavailable. It is calculated by dividing the total time agents spend refusing calls by the total time they are unavailable.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use Case: Monitoring Refusal Rate- A contact center manager notices an increase in the refusal rate, indicating that agents are frequently refusing calls. By analyzing this metric, the manager identifies potential issues such as high call volume or agent fatigue. To address this, the manager:

• Adjusts agent schedules to ensure adequate coverage during peak times.

• Provides additional training to help agents handle calls more efficiently.

• Implements wellness programs to reduce agent fatigue and improve overall performance.

These actions help reduce the refusal rate, improve agent productivity, and enhance customer satisfaction.

Calculation: This metrics measures the percentage of calls that are answered within the predefined service level agreement (SLA). It is calculated by dividing the number of calls answered within the SLA by the total number of calls.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Up, a higher metric value is best.

Use Case: Monitoring Service Level Compliance Rate- A contact center manager notices that the Service Level Compliance Rate is below the target. By analyzing this metric, the manager identifies periods when calls are not being answered within the SLA. To address this, the manager:

• Adjusts staffing levels to ensure adequate coverage during peak times.

• Implements training programs to improve agent efficiency.

• Optimizes call routing to prioritize high-priority calls.

These actions help improve the Service Level Compliance Rate, ensuring that calls are answered promptly and customer satisfaction is maintained.

Calculation: This metrics measures the percentage of inbound calls that are abandoned shortly after being placed. It is calculated by dividing the total number of short-abandoned calls by the total number of inbound calls.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Division, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use Case: Monitoring Short Call Abandonment Rate- A contact center manager notices a high short call abandonment rate, indicating that many callers are hanging up quickly. By analyzing this metric, the manager identifies potential issues such as long wait times or ineffective IVR menus. To address this, the manager:

• Reduces wait times by optimizing staffing levels during peak hours.

• Improves IVR menus to make navigation easier for callers.

• Offers call-back options to prevent callers from hanging up.

These actions help reduce the short call abandonment rate, improve customer satisfaction, and ensure that more calls are successfully handled.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Division, Direction

Supported channel: Omnichannel

Metric type: Historical

Filters:

• Employee group: Agent, Team, Company

• Contact group: Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Calculation: This metric measures the total amount of time that contacts spent waiting in the queue before being abandoned as Short or Long Abandons

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Division, Direction

Supported channel: Omnichannel

Metric direction: Down, a lower metric value is best.

Use Case: Reducing Call Abandonment Rates - Managers can use this metric to identify average wait times before abandonment, improve queue management, enhance customer experience, and train agents for efficiency.

Calculation: This metric represents the total time customers spent waiting before abandoning an interaction that ultimately expired without being handled.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Division, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use Case: Understanding Expired Abandon Duration — In a contact center, managers can use this metric alongside abandons expired to understand how long contacts wait before expiring in the queue. Tracking this duration over time helps identify whether queue timeout settings are appropriate for the contact volume.

Calculation: The total number of times a held party (customer) abandons the call while on hold. This metric represents the cumulative count of instances where customers hang up or disconnect while they are placed on hold by agents.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Division, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Up, a higher metric value is best.

Use case: Monitoring Customer Abandonment During Hold- In a contact center, tracking the number of times customers abandon calls while on hold is crucial for understanding customer patience and satisfaction. For example, if the number of abandoned calls is high, it may indicate that customers are experiencing long hold times or are dissatisfied with being placed on hold. This metric can help identify trends in hold times, measure the impact of hold practices on customer satisfaction, and ensure that agents are minimizing hold times to improve the overall customer experience. Additionally, it can be used to assess the effectiveness of initiatives aimed at reducing hold times and preventing customer abandonment.

Calculation: This metric represents the total time customers spent waiting before abandoning an outbound interaction without being successfully engaged.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Division, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use Case: Analyzing Outbound Abandon Duration — In a contact center, managers can use this metric alongside Abandons Outbound to understand how long outbound contacts wait before being abandoned. Tracking this duration over time helps identify delays in outbound contact handling and measure the impact of process improvements.

Calculation: This metric measures the total queue wait time for contacts abandoned within the short abandon threshold while in queue.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Division, Direction

Supported channel: Omnichannel

Metric direction: Down, a lower metric value is best.

Use Case: Monitoring Short Abandon Threshold- In a contact center, supervisors can use this metric to understand the total waiting time accumulated by contacts that are abandoned within the short abandon threshold. Monitoring this metric over time helps identify patterns of quick drop-offs, assess the impact of short queue delays, and compare behavior across different queues, campaigns, or skills to improve real-time queue management and staffing decisions.

Calculation: This metric measures the total queue wait time of all long-abandon contacts.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Division, Direction

Supported channel: Omnichannel

Metric direction: Down, a lower metric value is best.

Use Case: Checking Abandon Long Abandon Wait Time- In a contact center, supervisors can use this metric to measure the total waiting time accumulated by contacts that are abandoned after longer queue durations. Tracking this metric over time helps identify patterns of prolonged waiting, evaluate how extended delays affect customer patience, and compare performance across different queues to improve staffing and queue management strategies.

Calculation: This metric measures the total queue wait time of all contacts abandoned as short or long abandons.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Division, Direction

Supported channel: Omnichannel

Metric direction: Down, a lower metric value is best.

Use Case: Improving Overall Abandon Wait Time (Short & Long) - In a contact center, supervisors can use this metric to understand the overall waiting time accumulated by contacts before they are abandoned, whether classified as short or long abandons. Tracking this combined metric over time helps identify patterns in queue delays, evaluate the total in-queue exposure experienced by customers, and compare abandonment behavior across different campaigns, queues, or skills to improve staffing and routing strategies.

Calculation: This metric measures the total time before queue entry for contacts abandoned during IVR / intent refinement.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Division, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use case: Improving Abandon Prequeue time- In a contact center, you can use this metric to understand how long customers remain in the IVR or self-service system before abandoning.

Calculation: This metric measures the total time customers spent on hold before hanging up.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Division, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use case:Managing the On-hold Call Time - In a contact center, use this metric to understand how much time customers spend on hold before disconnecting. For example, when an agent places a customer on hold to check something and the customer hangs up while waiting, this metric helps identify such cases. Tracking this over time helps flag excessively long hold durations and supports improvements in hold management and call handling practices.

Calculation: The total number of calls that were abandoned, including both short and long abandoned calls.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Division, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use case: Reducing Call Abandonment - Managers can use this metric to identify patterns in call abandonment, implement strategies to reduce wait times, and improve customer satisfaction by ensuring calls are answered promptly.

Calculation: This metric represents the total number of interactions that were marked as expired abandons across all customers.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Division, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use case: Reducing Queue Expiration - A contact center can use this metric to track the number of contacts that expire in the queue without being handled. A high count may indicate that queue timeout settings are too short or that staffing levels are insufficient to handle contact volume. Tracking this metric over time helps identify opportunities to adjust queue settings and staffing to ensure more contacts are handled before expiration.

Calculation: The total number of outbound interactions that were abandoned before the customer could be successfully engaged or assisted.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Division, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use case: Monitoring Outbound Abandonment - A contact center running outbound campaigns can use this metric to track the number of outbound contacts that are abandoned before reaching an agent. A high count may indicate issues with dialing strategies, contact list quality, or agent availability. Tracking this metric over time helps identify opportunities to adjust campaign settings and staffing to reduce outbound abandonment and improve campaign effectiveness.

Calculation: This metric measures the total number of abandoned contacts. It is calculated by counting the distinct contact numbers for contacts where the abandoned flag is set to true.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Division, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use Case: Tracking Overall Abandonment Health – A contact center uses this metric as a quick, at-a-glance view of total contact loss. It helps identify abandonment health and service gaps. This supports timely actions to improve customer retention and performance.

Calculation: This metric measures the total number of abandoned contacts. It is calculated by summing the count of short and long abandoned contacts across all records.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Division, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use Case: Analyzing Shorty and Long Queue Abandonment - Supervisors can use this metric to track the total number of customers who disconnect while waiting (both short and long abandons), identify patterns in queue drop-offs, and take corrective actions such as adjusting staffing levels, optimizing wait times, or improving call routing to reduce overall abandonment rates.

Calculation: The total active time that agents spend handling contacts, calculated by summing the active seconds for all agents.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Agent, Team, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use case: Monitoring Agent Productivity - Supervisors can use this metric to identify top-performing agents, provide support to those needing improvement, and optimize scheduling for better efficiency.

Calculation: The total time in seconds that agents spend in After Call Work (ACW), calculated by summing the ACW seconds for all agents.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use case: Improving After Call Work Efficiency - Managers can use this metric to identify how much time agents spend on post-call activities, streamline processes to reduce ACW time, and provide training to improve efficiency.

Calculation:

The total number of handled contacts divided by the total agent time in hours.

Agent CPH =

[Handled] / {([Available Time] + [Working Time]) / 3600}

Filters:

• Employee group: Agent, Team, Company

• Contact group: Company

• Attributes: Channel, Disposition, Direction

Supported channels: Omnichannel (voice and digital)

Metric type: Historical

Metric direction: Up, a higher metric value is best.

Use case: Monitoring overall agent productivity – A supervisor uses this metric to see how many contacts agents handle for every hour they are available and working. By comparing Agent CPH across agents, skills, or campaigns, the supervisor can identify high performers, uncover training needs, and adjust staffing to improve productivity and service levels.

Calculation:

The total number of inbound handled contacts divided by the total agent time in hours.

Agent Inbound CPH =

[Inbound Handled] / {([Available Time] + [Working Time]) / 3600}

Filters:

• Employee group: Agent, Team, Company

• Contact group: Company

• Attributes: Channel, Disposition, Direction

Supported channels: Omnichannel (voice and digital)

Metric type: Historical

Metric direction: Up, a higher metric value is best.

Use case: Evaluating inbound efficiency – A contact center that handles sales and support calls uses this metric to understand how efficiently agents handle inbound demand. By viewing Agent Inbound CPH by skill or campaign, managers can see where inbound handling is strong, where queues may be understaffed, and where process changes or training may be needed.

Calculation: The total number of contacts offered to agents, calculated by summing the agent offered count.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Up, a higher metric value is best.

Use case: Evaluating Agent Workload - This metric helps supervisors assess if the workload is evenly distributed among agents, ensuring no one is overburdened and maintaining high service quality.

Calculation:

The total number of outbound handled contacts divided by the total agent time in hours.

Agent Outbound CPH =

[Outbound Handled] / {([Available Time] + [Working Time]) / 3600}

Filters:

• Employee group: Agent, Team, Company

• Contact group: Company

• Attributes: Channel, Disposition, Direction

Supported channels: Omnichannel (voice and digital)

Metric type: Historical

Metric direction: Up, a higher metric value is best.

Use case: Measuring outbound campaign productivity – A team running outbound sales or collections campaigns uses this metric to see how many outbound contacts agents complete per hour. By tracking Agent Outbound CPH across campaigns or time periods, managers can evaluate list quality, dialing strategies, and agent performance, and then adjust workloads or scripts to improve results.

Calculation: The total duration of agent sessions in seconds. This metric represents the cumulative time agents spend logged into the system during their sessions, providing insight into agent activity and engagement.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Up, a higher metric value is best.

Use case: Measuring Total Agent Session Duration- In a contact center, tracking the total duration of agent sessions is crucial for understanding agent activity and productivity. For example, if the total session duration is high, it indicates that agents are spending significant time logged into the system, which can be a sign of high engagement and workload. This metric can help identify trends in agent activity, measure the effectiveness of scheduling and shift management, and ensure that resources are allocated appropriately to maintain high levels of agent productivity. Additionally, it can be used to assess the impact of training programs or system improvements on agent engagement and efficiency.

Calculation: The total duration of all contacts handled by agents, calculated by summing the agent contact duration seconds.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Tag Name, Direction, Contact Type

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use case: Evaluating Agent Efficiency- This metric helps supervisors monitor the total time agents spend on customer interactions. It can be used to assess efficiency, identify agents who may need additional training, and ensure that customer interactions are handled within acceptable time frames.

Calculation:

The total number of handled contacts divided by the total Talk Time in hours.

Agent Handled per Talk Hour =

[Handled] / {[Talk Time] / 3600}

Filters:

• Employee group: Agent, Team, Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Direction

Supported channels: Omnichannel (voice and digital)

Metric type: Historical

Metric direction: Up, a higher metric value is best.

Use case: Understanding handling efficiency during talk time – A supervisor uses this metric to see how many contacts agents complete for each hour they spend talking to customers. By comparing Agent Handled per Talk Hour with average talk time and handle time, the supervisor can identify agents who resolve contacts efficiently, spot long calls that may signal complex issues, and fine tune coaching and staffing plans.

Calculation:

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Snapshot

Metric direction: Down, a lower metric value is best.

Calculation: The total amount of time agents are available to take contacts, calculated by summing the AVAILABLE SECONDS.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use case: Monitoring Agent Availability- This metric helps supervisors track the total time agents are available to handle customer interactions. It can be used to ensure that there are enough agents available during peak times and to identify periods when agent availability is low, allowing for better scheduling and resource management.

Calculation: This metric represents the average time customers waited before abandoning an interaction that eventually expired without being handled.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Division, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use case: Benchmarking Expired Abandon Wait Times- Supervisors can use this metric to understand the average time before contacts are automatically timed out by the system. This insight helps fine-tune timeout thresholds, ensuring they align with customer expectations and operational goals.

Calculation: This metric represents the total outbound abandon time divided by the number of outbound abandons.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Division, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use case: Benchmarking Outbound Abandon Wait Times- In a contact center, supervisors can use this metric to understand the typical wait time before an outbound contact is abandoned. Tracking this metric over time helps identify trends and compare outbound handling delays across campaigns or skills.

Calculation: This metric measures the average amount of time spent in the queue before being abandoned. It is calculated by dividing Abandon Time by the number of Abandons.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Division, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use Case: Evaluating Average Abandon Time - In a contact center, supervisors can use this metric to understand how long customers typically wait before abandoning the queue (including both short and long abandons). This insight helps establish realistic wait-time benchmarks, evaluate overall queue performance, and make informed staffing and scheduling decisions to reduce abandonment rates.

Calculation: This metric measures the total short-abandon wait time divided by the number of short abandons.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Division, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use Case: Monitoring Average Short Abandon Time- In a contact center, this metric represents the average time customers wait in the queue before abandoning within the short abandon threshold. Tracking this over time helps understand how quickly customers drop off during initial wait periods and supports improvements in queue management and early routing efficiency.

Calculation: This metric measures the total long-abandon wait time divided by the number of long abandons.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Division, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use Case: Benchmarking Avg Long Time Abandonment- In a contact center, this metric represents the average time customers wait in the queue before abandoning after longer durations. Tracking this over time helps understand how long customers are willing to wait, supports setting realistic queue-time targets, and aids in improving queue management and staffing decisions.

Calculation: This metric measures the total short and long abandon wait time divided by the number of short plus long abandons.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Division, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use Case: Reducing Avg Short & Long Time Abandonment- In a contact center, use this metric to measure the average wait time across both short and long abandons, helping benchmark overall queue performance and identify areas for improvement.

Calculation: This metric measures the total prequeue abandon time divided by the number of prequeue abandons.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Division, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use Case: Optimizing Self-Service Abandon Time - In a contact center, this metric represents the average time customers spend in self-service before abandoning. Tracking it helps determine the ideal IVR duration, identify friction points, and improve overall self-service efficiency.

Calculation: This metric measures the total on-hold abandon time divided by the number of on-hold abandons.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Division, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use Case: Monitoring Average On-hold Abandon Time - In a contact center, tracking the average time customers spend on hold before abandoning helps understand how long they are willing to wait. Monitoring this over time helps identify excessive hold durations, assess response delays, and support improvements in hold management and call handling efficiency.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Up, a higher metric value is best.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Calculation:

Filters:

• Employee group: Agent, Team, Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Snapshot

Metric direction: Down, a lower metric value is best.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best..

Calculation:

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Snapshot

Metric direction: Up, a higher metric value is best.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Calculation: The total number of seconds agents spend in conference calls. This metric represents the cumulative time agents are engaged in conference calls, which can include calls with customers, supervisors, or other team members.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use case: Monitoring Agent Collaboration Time in Conference Calls- In a contact center, tracking the total conference call time is important to understand how much time agents spend collaborating with others. For example, if an agent frequently needs to consult with a supervisor or other team members during calls, this metric can help identify areas where additional training or resources might be needed to improve efficiency. Additionally, it can be used to monitor the effectiveness of team collaboration and ensure that agents are not spending excessive time in conference calls, which could impact their availability for handling customer inquiries.

Calculation: The total number of conference calls agents have participated in. This metric represents the cumulative count of conference calls involving agents, which can include calls with customers, supervisors, or other team members.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use case: Tracking Agent Participation in Conference Calls- In a contact center, tracking the total number of conference calls is essential for understanding how often agents need to collaborate with others. For example, if an agent frequently participates in conference calls, it may indicate that they require additional support or need to consult with supervisors and colleagues regularly. This metric can help identify areas where agents might benefit from more training or resources to handle calls independently. Additionally, it can be used to monitor the effectiveness of team collaboration and ensure that agents are not spending excessive time in conference calls, which could impact their availability for handling customer inquiries.

Calculation: The total duration of time (in seconds) that each contact spends with agents who have specific skills. This metric represents the cumulative time contacts are handled by agents with particular skills, grouped by the contact number.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use case: Analyzing Contact Handling Time by Agent Skills- In a contact center, tracking the total duration of time contacts spend with skilled agents is important to understand how effectively agents are using their skills. For example, if a contact requires specialized assistance, this metric can help identify how much time is being spent by agents with the necessary skills to resolve the issue. This information can be used to optimize agent training programs, ensure that the right agents are handling the right contacts, and improve overall customer satisfaction by reducing the time needed to resolve issues.

Calculation: The total count of contacts processed by the system, including inbound and outbound interactions. Each contact represents a unique interaction initiated by a customer or agent.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Up, a higher metric value indicates greater interaction volume.

Use case: Monitoring Interaction Volume Across Channels- Tracking the number of contacts is essential for understanding workload and resource allocation in a contact center. For example, a sudden increase in contacts may indicate a spike in customer demand, requiring additional staffing or automated solutions. This metric can also help identify trends in customer engagement and optimize operational planning.

Calculation:

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Tag Name, Direction, Contact Type

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Up, a higher metric value is best.

Calculation: The total number of seconds agents spend handling interactions. This metric represents the cumulative time agents are actively engaged in handling customer interactions, providing insight into agent workload and efficiency.

Filters:

• Employee group: Agent

• Contact group: Skill, Campaign, Team, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use case: Measuring Agent Handling Time- In a contact center, tracking the total handling time is crucial for understanding how much time agents spend on customer interactions. For example, if the handling time is high, it may indicate that agents are dealing with complex issues that require more time to resolve. This metric can help identify trends in agent workload, measure the efficiency of handling customer inquiries, and ensure that agents are managing their time effectively. Additionally, it can be used to assess the impact of training programs aimed at improving agent efficiency and reducing handling times, ultimately leading to better customer service and satisfaction.

Calculation: The total number of unique contacts that have been handled by agents. This metric represents the count of distinct customer interactions where agents have spent time handling the contact, providing insight into the volume of unique customer engagements.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Up, a higher metric value is best.

Use case: Tracking Unique Customer Interactions Handled by Agents- In a contact center, tracking the number of unique contacts handled by agents is essential for understanding the reach and effectiveness of the contact center. For example, if the number of unique contacts is high, it indicates that agents are engaging with a large number of different customers, which can be a sign of broad customer reach and engagement. This metric can help identify trends in customer interaction, measure the effectiveness of outreach efforts, and ensure that resources are allocated appropriately to handle the volume of unique customer inquiries. Additionally, it can be used to assess the impact of marketing campaigns or service improvements on customer engagement.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Up, a higher metric value is best.

Calculation: The total number of interactions that were handled within the Service Level Agreement (SLA). This metric represents the cumulative count of contacts that were resolved within the predefined time frame set by the SLA, excluding the short & long abandons and placed calls, indicating the efficiency and timeliness of the contact center's operations.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Up, a higher metric value is best.

Use case: Evaluating SLA Compliance in Contact Handling- In a contact center, tracking the number of interactions handled within the SLA is crucial for understanding the efficiency and effectiveness of the service provided. For example, if the count of interactions within SLA is high, it indicates that agents are successfully resolving customer inquiries within the expected time frame, which can lead to higher customer satisfaction. This metric can help identify trends in SLA compliance, measure the impact of process improvements, and ensure that agents are meeting performance targets. Additionally, it can be used to assess the effectiveness of training programs aimed at improving agent efficiency and reducing resolution times.

Calculation: The total number of unique inbound contacts that have been active for more than two seconds. This metric represents the count of distinct inbound customer interactions where the contact duration exceeds two seconds, providing insight into the volume of meaningful inbound interactions.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Up, a higher metric value is best.

Use case: Tracking Meaningful Inbound Customer Interactions- In a contact center, tracking the number of unique inbound contacts with a duration of more than 2 seconds is crucial for understanding the volume of meaningful customer interactions. For example, if the count of these interactions is high, it indicates that the contact center is effectively engaging with customers who have genuine inquiries or issues. This metric can help identify trends in inbound customer engagement, measure the effectiveness of inbound communication strategies, and ensure that resources are allocated appropriately to handle the volume of meaningful inbound inquiries. Additionally, it can be used to assess the impact of marketing campaigns or service improvements on customer engagement.

Calculation: The total number of unique inbound contacts. This metric represents the count of distinct inbound customer interactions, providing insight into the volume of unique customer engagements initiated by customers.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Up, a higher metric value is best.

Use case: Tracking Unique Inbound Customer Interactions- In a contact center, tracking the number of unique inbound contacts is essential for understanding the volume of customer-initiated interactions. For example, if the count of unique inbound contacts is high, it indicates that the contact center is effectively engaging with a large number of different customers who are reaching out for assistance. This metric can help identify trends in inbound customer engagement, measure the effectiveness of inbound communication strategies, and ensure that resources are allocated appropriately to handle the volume of unique inbound inquiries. Additionally, it can be used to assess the impact of marketing campaigns or service improvements on customer engagement.

Calculation: The total number of unique inbound contacts that have been handled by agents. This metric represents the count of distinct customer interactions initiated by customers that were actively managed by agents, providing insight into the volume of unique inbound engagements.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Up, a higher metric value is best.

Use case: Tracking Unique Inbound Customer Interactions Handled by Agents- In a contact center, tracking the number of unique inbound contacts handled by agents is essential for understanding the reach and effectiveness of the contact center. For example, if the number of unique inbound contacts is high, it indicates that agents are engaging with a large number of different customers who are reaching out for assistance. This metric can help identify trends in inbound customer engagement, measure the effectiveness of inbound communication strategies, and ensure that resources are allocated appropriately to handle the volume of unique inbound inquiries. Additionally, it can be used to assess the impact of marketing campaigns or service improvements on customer engagement.

Calculation: The total number of seconds contacts spend in queue. This metric represents the cumulative time that all contacts have waited in the queue before being handled by an agent.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use case: Analyzing Total Queue Time for Contacts- In a contact center, tracking the total queue time is crucial for understanding how long customers are waiting before their inquiries are addressed. For example, if the total queue time is high, it may indicate that the contact center is experiencing high call volumes or that there are insufficient agents available to handle the incoming contacts promptly. This metric can help identify trends in queue times, measure the impact of staffing levels on customer wait times, and ensure that resources are allocated appropriately to minimize queue times. Additionally, it can be used to assess the effectiveness of initiatives aimed at reducing wait times and improving the overall customer experience.

Calculation: the total number of unique interactions. This metric represents the count of distinct interaction IDs, providing insight into the volume of unique customer engagements handled by the contact center.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Up, a higher metric value is best.

Use case: Tracking Unique Customer Interactions- In a contact center, tracking the number of unique interactions is essential for understanding the volume of distinct customer engagements. For example, if the count of unique interactions is high, it indicates that the contact center is effectively handling a large number of different customer inquiries or issues. This metric can help identify trends in customer engagement, measure the effectiveness of communication strategies, and ensure that resources are allocated appropriately to handle the volume of unique interactions. Additionally, it can be used to assess the impact of marketing campaigns or service improvements on customer engagement.

Calculation: The total number of unique agent sessions. This metric represents the count of distinct sessions where agents have logged in and interacted with the system, providing insight into agent activity and engagement.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use case: Tracking Unique Agent Sessions- In a contact center, tracking the number of unique agent sessions is essential for understanding agent activity and engagement. For example, if the count of unique agent sessions is high, it indicates that agents are frequently logging in and actively participating in their roles. This metric can help identify trends in agent engagement, measure the effectiveness of scheduling and shift management, and ensure that resources are allocated appropriately to maintain high levels of agent activity. Additionally, it can be used to assess the impact of training programs or system improvements on agent engagement and productivity.

Calculation: This metric represents the number of calls that were abandoned by the caller after waiting for a long period of time. In other words, it counts the calls where the caller hung up before being connected to an agent, typically due to extended wait times.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Division, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use case: Identifying Peak Times for Call Abandonment- Imagine a contact center that handles customer service inquiries for a large retail company. The Long Abandoned Count metric can be used to identify periods when callers are experiencing unusually long wait times and are abandoning their calls. For example, if the metric shows a high number of abandoned calls during certain hours of the day, the contact center can investigate the cause, such as understaffing or high call volumes, and take corrective actions like adjusting staffing levels or implementing call-back options to improve customer satisfaction.

Calculation: This metric represents the longest time a caller spent in the queue before their call was answered or abandoned within a specified period. Essentially, it indicates the longest duration a caller waited in the queue.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use case: Identifying Bottlenecks in Call Handling- In a contact center that provides customer support for a telecommunications company, the Maximum In-Queue Time metric can be used to identify instances where callers experienced unusually long wait times in the queue. For example, if the metric shows a particularly high value during certain hours or days, the contact center can investigate potential causes such as high call volumes, technical issues, or insufficient staffing. By addressing these issues, the contact center can reduce in-queue times and improve overall customer satisfaction. Additionally, this metric can help in setting benchmarks and goals for maximum acceptable in-queue times, ensuring that service levels are maintained.

Calculation: This metric represents the longest time taken for a call to be answered by an agent within a specified period. It indicates the longest wait time experienced by a caller before their call was answered.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use case: Monitoring Longest Wait Times for Improved Response- In a contact center that provides technical support for a software company, the Maximum Speed of Answer metric can be used to identify instances where callers experienced unusually long wait times. For example, if the metric shows a particularly high value during certain hours or days, the contact center can investigate potential causes such as high call volumes or insufficient staffing. By addressing these issues, the contact center can reduce wait times and improve overall customer satisfaction. Additionally, this metric can help in setting benchmarks and goals for maximum acceptable wait times, ensuring that service levels are maintained.

Calculation: This metric represents the longest time a caller waited in the queue before abandoning the call after being placed in the queue for the first time within a specified period. It indicates the longest duration a caller waited before giving up on the call.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use case: Reducing Call Abandonment Due to Long Wait Times- In a contact center for a healthcare provider, tracking the Maximum Abandoned After First In-Queue Time metric is crucial. It helps identify when callers experience long wait times and abandon their calls. For example, if this metric is high during certain hours or days, the contact center can investigate causes like high call volumes, technical issues, or insufficient staffing. By addressing these issues, the contact center can reduce call abandonment rates and improve customer satisfaction. Additionally, this metric can help set benchmarks and goals for acceptable wait times, ensuring service levels are maintained.

Calculation: This metric represents the longest time taken by an agent to complete after-call work (ACW) within a specified period. After-call work includes tasks such as updating records, sending follow-up emails, and completing any other necessary documentation after a call has ended. It indicates the longest duration an agent spent on post-call activities.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use case: Optimizing Post-Call Processes for Efficiency- In a contact center for a financial services company, tracking the Maximum After-Call Work Time metric is crucial. It helps identify when agents spend too long on post-call activities. For example, if this metric is high during certain hours or days, the contact center can investigate causes like complex customer issues, inefficient processes, or lack of training. By addressing these issues, the contact center can reduce after-call work times and improve efficiency. Additionally, this metric can help set benchmarks and goals for acceptable after-call work times, ensuring agents handle calls more effectively and reduce idle time between calls.

Calculation: This metric represents the proportion of time agents spend handling calls compared to their total available time. It indicates how efficiently agents are being utilized.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Up, a higher metric value is best.

Use case: Maximizing Agent Efficiency- In a contact center for a technology company, tracking the Agent Utilization Rate metric is crucial. It helps monitor how effectively agents are being utilized. For example, if this metric shows a low utilization rate, it may indicate that agents are spending too much time idle or not handling enough calls. The contact center can investigate causes like inefficient call routing, insufficient training, or technical issues. By addressing these issues, the contact center can improve agent utilization, ensuring agents handle calls more efficiently and reduce idle time. Additionally, this metric can help set benchmarks and goals for optimal agent utilization, contributing to better performance and customer satisfaction.

Calculation: This metric represents the number of inbound calls that were not answered within the configured Service Level (SLA) threshold. It includes short and long abandoned calls when the corresponding abandon flags are enabled for the skill. All other unanswered inbound calls are evaluated using the standard out‑of‑SLA logic.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use case: In a contact center for an internet service provider, tracking the Total Out of SLA Count metric is crucial. It helps monitor and improve compliance with service level agreements. For example, if this metric shows a high number of calls not handled within the SLA time frame, the contact center can investigate causes like high call volumes, insufficient staffing, or process inefficiencies. By addressing these issues, the contact center can reduce out-of-SLA calls, ensuring customers receive timely support and improving satisfaction. Additionally, this metric can help set benchmarks and goals for SLA compliance, ensuring service levels are consistently met.

Calculation: This metric represents the total number of unique outbound calls made by the contact center within a specified period. It indicates how many unique outbound calls were made.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Up, a higher metric value is best.

Use case: Tracking Outbound Call Campaign Effectiveness- In a contact center for a marketing agency, tracking the Unique Outbound Contacts Count metric is crucial. It helps monitor the effectiveness of outbound call campaigns. For example, if this metric shows a high number of unique outbound calls, it may indicate that the campaign is reaching a large audience. Conversely, if the metric is low, the contact center can investigate causes like issues with call lists, dialing systems, or agent performance. By addressing these issues, the contact center can improve the reach and effectiveness of outbound campaigns, ensuring marketing efforts are maximized. Additionally, this metric can help set benchmarks and goals for outbound call campaigns, contributing to better performance and customer engagement.

Calculation: This metric represents the total number of unique outbound calls that were successfully handled by agents within a specified period. It indicates how many unique outbound calls were effectively managed by the agents.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Up, a higher metric value is best.

Use case: Evaluating Outbound Call Handling Efficiency- In a contact center for a telecommunications company, tracking the unique Handled Outbound Contacts Count metric is important. It helps evaluate the efficiency of outbound call handling. For example, if this metric shows a high number of unique handled outbound calls, it could mean that agents are effectively managing their outbound call responsibilities. If the metric is low, the contact center can investigate causes like issues with call lists, dialing systems, or agent performance. By dealing with these issues, the contact center can improve the effectiveness of outbound call campaigns, and ensure agents reach and engage with more customers. Additionally, this metric can help set benchmarks and goals for outbound call handling, contributing to better performance and customer engagement.

Calculation: This metric represents the total number of contacts that gave up while still in the IVR / self-service, before ever reaching the queue.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Division, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use case: Reducing Prequeue Call Abandonment- In a contact center for an insurance company, tracking the Total PreQueue Abandoned Count metric is crucial. It helps identify when callers abandon their calls before entering the queue, often due to challenges such as getting stuck navigating IVR menus or experiencing self-service friction. For example, if this metric shows a high number of prequeue abandoned calls, the contact center can investigate causes like confusing menu options, long initial wait times, or technical issues. By addressing these issues, the contact center can reduce prequeue abandoned calls and ensure more customers successfully reach the queue and receive support. Additionally, this metric helps set benchmarks and goals for reducing early call drop-offs, improving overall service efficiency and customer satisfaction.

Calculation: This metric represents the total number of unique calls that had some activity before being placed in the queue within a specified period. It indicates how many unique calls had interactions before entering the queue.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Up, a higher metric value is best.

Use case: Analyzing Prequeue Interactions for Better Call Handling- In a contact center for a healthcare provider, the Unique Pre-Queue Contacts Count metric helps analyze interactions before calls enter the queue. A high number of unique pre-queue contacts may show that callers spend too much time on IVR menus or automated systems before reaching the queue. The contact center can look into causes like complex IVR menus, unclear instructions, or technical issues. By fixing these, the center can streamline pre-queue interactions, helping callers reach the queue faster and get timely support. This metric also helps set benchmarks and goals to improve pre-queue processes, boosting overall customer satisfaction and service efficiency.

Calculation: This metric represents the total amount of time callers spent in pre-queue activities within a specified period. It indicates the cumulative duration callers spent before being placed in the queue.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use case: Optimizing Prequeue Processes for Better Call Handling- In a contact center for a utility company, the total prequeue time metric helps analyze prequeue process efficiency. A high total prequeue time may show that callers spend too much time on IVR menus or automated systems before reaching the queue. The contact center can look into causes like complex IVR menus, unclear instructions, or technical issues. By fixing these, the center can streamline prequeue interactions, helping callers reach the queue faster and get timely support. This metric also helps set benchmarks and goals to improve prequeue processes, boosting overall customer satisfaction and service efficiency.

Calculation: This metric represents the total number of unique calls that spent time in the queue within a specified period. It indicates how many unique calls were placed in the queue.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Up, a higher metric value is best.

Use case: Analyzing Queue Management for Improved Call Handling- In a contact center for a banking institution, the Unique In-Queue Contacts Count metric helps analyze queue management efficiency. A high number of unique in-queue contacts may show that callers are often placed in the queue, possibly due to high call volumes or insufficient staffing. The contact center can look into causes like peak call times, agent availability, or technical issues. By fixing these, the center can optimize queue management, helping callers spend less time in the queue and get timely support. This metric also helps set benchmarks and goals to improve queue processes, boosting overall customer satisfaction and service efficiency.

Calculation: This metric represents the total number of times a contact was refused.

Filters:

• Employee group: Company

• Contact group: Agent, Skill, Campaign, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use case: Managing Agent Workload and Call Refusals- In a contact center for a software company, the total refused count metric helps monitor when agents refuse calls. A high number of refused calls may show that agents are overwhelmed or there are issues with call routing. The contact center can look into causes like high call volumes, insufficient staffing, or technical issues. By fixing these, the center can reduce refused calls, ensuring more customers get timely support. This metric also helps set benchmarks and goals to manage agent workload, boosting overall performance and customer satisfaction.

Calculation: This metric represents the total amount of time spent routing calls to the appropriate agents or departments within a specified period. It indicates the cumulative duration callers spent while their calls were being routed.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use case: Optimizing Call Routing Efficiency- In a contact center for a telecommunications company, the Total Routing Time metric helps analyze call routing efficiency. A high total routing time may show that callers spend too much time being transferred between agents or departments. The contact center can look into causes like complex routing rules, insufficient training, or technical issues. By fixing these, the center can streamline call routing, helping callers connect to the right agents faster and get timely support. This metric also helps set benchmarks and goals to improve routing efficiency, boosting overall customer satisfaction and service efficiency.

Calculation: This metric represents the total number of calls that were abandoned by the caller shortly after being placed in the queue within a specified period. It indicates how many calls were dropped by callers after a brief wait time.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Division, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use case: Reducing Short Call Abandonment- In a contact center for an online retail company, the Total Short Abandoned Count metric helps identify when callers abandon their calls shortly after being placed in the queue. A high number of short abandoned calls may show issues like long initial wait times, unclear IVR instructions, or technical problems. The contact center can look into these causes and fix them to reduce short abandoned calls, ensuring more customers stay on the line and get the support they need. This metric also helps set benchmarks and goals to reduce call abandonment rates, boosting overall customer satisfaction and service efficiency.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Snapshot

Metric direction: Up, a higher metric value is best.

Calculation: This metric represents the total amount of time taken to answer calls within a specified period. It indicates the cumulative duration callers waited before their calls were answered.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use case: Improving Response Times for Better Customer Experience- In a contact center for a financial services company, the Total Speed of Answer Time metric helps analyze call handling efficiency. A high total speed of answer time may show that callers experience long wait times before their calls are answered. The contact center can look into causes like high call volumes, insufficient staffing, or technical issues. By fixing these, the center can reduce wait times, ensuring callers get timely support. This metric also helps set benchmarks and goals to improve response times, boosting overall customer satisfaction and service efficiency.

Calculation: This metric represents the total amount of time agents spent talking to customers within a specified period. It indicates the cumulative duration of all conversations between agents and customers.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Skill, Campaign, Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use case: Monitoring Agent Engagement with Customers- In a contact center for a healthcare provider, the Total Talk Time metric helps monitor how much time agents spend on calls with customers. A high total talk time may show that agents are spending significant time addressing customer issues, indicating complex inquiries or thorough service. A low metric may suggest quick resolutions or insufficient engagement. The contact center can look into causes like call complexity, agent training, or process efficiency. By analyzing this metric, the center can ensure agents provide adequate support while managing their time effectively. This metric also helps set benchmarks and goals for optimal talk time, boosting overall performance and customer satisfaction.

Calculation: This metric represents the total amount of time agents were unavailable to take calls within a specified period. It indicates the cumulative duration agents were not available to handle calls.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Down, a lower metric value is best.

Use case: Managing Agent Availability for Optimal Performance- In a contact center for a travel agency, the Total Unavailable Time metric helps monitor and manage agent availability. A high total unavailable time may show that agents are often unavailable, possibly due to breaks, meetings, or other non-call activities. The contact center can look into causes like scheduling inefficiencies, high workload, or insufficient staffing. By fixing these, the center can reduce the time agents are unavailable, ensuring more customers get timely support. This metric also helps set benchmarks and goals to manage agent availability, boosting overall performance and customer satisfaction.

Calculation: This metric represents the total amount of time agents spent actively working on contacts within a specified period. It indicates the cumulative duration agents were engaged in handling contacts.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Company

• Attributes: Channel, Disposition, Direction

Supported channel: Omnichannel

Metric type: Historical

Metric direction: Up, a higher metric value is best.

Use case: Enhancing Agent Productivity and Contact Handling- In a contact center for a software company, the Total Working Contacts Time metric helps monitor and enhance agent productivity. A high total working contacts time may show that agents spend significant time handling customer inquiries, indicating high engagement and thorough service. Conversely, a low metric may suggest agents aren't spending enough time on contact handling, possibly due to inefficiencies or distractions. The contact center can look into causes like call complexity, agent training, or process efficiency. By analyzing this metric, the center can ensure agents manage their time effectively and provide adequate support. This metric also helps set benchmarks and goals for optimal working contacts time, boosting overall performance and customer satisfaction.