Digital Metrics

Calculation: This metric calculates the total duration of agent contacts in seconds. Metric calculations include contacts from Email, SMS, and all digital channels.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Skill, Campaign, Company

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

Supported channel: Digital

Metric type: Historical

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

Use case: Optimizing Agent Allocation Based on Contact Duration - A contact center uses this metric to track the total focus time agents spend on interactions from Email, SMS and Digital channels This helps management allocate resources effectively to Email, SMS, and Digital Channels, ensuring timely responses and improving customer satisfaction.

Calculation: The total time in seconds that agents take to provide their first response to a contact, calculated by summing the first response seconds for all agents.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Skill, Campaign, Company

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

Supported channel: Digital

Metric type: Historical

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

Use case: Improving First Response Times: Managers can use this metric to identify how quickly agents respond to initial customer inquiries, implement strategies to reduce response times, and enhance customer satisfaction by ensuring prompt attention to customer needs.

Calculation: The total number of messages sent by agents, calculated by summing the agent message count.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Skill, Campaign, Company

• Attributes: Media type, Disposition, Direction

Supported channel: Digital

Metric type: Historical

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

Use case: Monitoring Agent Productivity- This metric helps supervisors track how actively agents are engaging with customers, indicating productivity levels, and identifying trends for better resource allocation.

Calculation: The total number of responses sent by agents, calculated by summing the agent response count.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Skill, Campaign, Company

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

Supported channel: Digital

Metric type: Historical

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

Use case: Evaluating Agent Responsiveness: This metric helps supervisors monitor how actively agents respond to customer inquiries. High response counts indicate proactive engagement, while low counts may highlight areas where agents need additional support or training.

Calculation: The total number of unique agent contacts where the agent responded for the first time within a positive time frame.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Skill, Campaign, Company

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

Supported channel: Digital

Metric type: Historical

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

Use case: Evaluating Agent Responsiveness- This metric helps supervisors track the number of unique customer interactions where agents responded promptly. It can be used to assess the efficiency of agents in addressing customer inquiries and ensuring timely responses, which is crucial for maintaining high customer satisfaction.

Calculation: This metric calculates the percentage of agent contacts that received a response from the agent out of the total agent contacts for legacy and digital channels.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Skill, Campaign, Company

• Attributes: Media type, Disposition, Direction

Supported channel: Digital and Voice

Metric type: Historical

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

Use case: Evaluating Agent Responsiveness Across Channels: A contact center uses this metric to evaluate how responsive agents are to customer interactions across legacy channels (like SMS and Emails) and digital channels (like WhatsApp and Facebook Messenger). By understanding the proportion of contacts that receive a response, management can identify areas for improvement in agent performance and ensure that customers are receiving timely and effective responses across all communication platforms. This helps in optimizing agent training and improving overall customer satisfaction.

Calculation: This metric calculates the average time it takes for agents to respond to customer contacts for the first time.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Skill, Campaign, Company

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

Supported channel: t

Metric type: Historical

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

Use Case: Monitoring Agent First Response Time- A contact center uses this metric to monitor how quickly agents respond to customer contacts for the first time. By understanding the average first response time, management can identify areas for improvement in agent responsiveness and implement strategies to reduce response times, thereby enhancing customer satisfaction and ensuring timely support.

Calculation: This metric calculates the average time it takes for agents to respond to customer contacts for the first time.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Skill, Campaign, Company

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

Supported channel: Digital

Metric type: Historical

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

Use Case: Monitoring Agent First Response Time- A contact center uses this metric to monitor how quickly agents respond to customer contacts for the first time. By understanding the average first response time, management can identify areas for improvement in agent responsiveness and implement strategies to reduce response times, thereby enhancing customer satisfaction and ensuring timely support.

Calculation: This metric calculates the average number of responses received from customers per contact.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Skill, Campaign, Company

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

Supported channel: Digital

Metric type: Historical

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

Use Case: Measuring Customer Engagement- A contact center uses this metric to measure customer engagement by tracking the average number of responses received from customers per contact. By understanding how actively customers are responding, management can assess the effectiveness of communication strategies and identify opportunities to enhance customer interaction. This helps in improving customer satisfaction and ensuring that agents are effectively engaging with customers

Calculation: This metric calculates the average time agents take to provide follow-up responses during customer interactions.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Skill, Campaign, Company

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

Supported channel: Digital

Metric type: Historical

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

Use Case: Monitoring Follow-Up Response Efficiency- A contact center uses this metric to monitor how efficiently agents provide follow-up responses during customer interactions. By understanding the average follow-up response time, management can identify areas for improvement in agent performance and implement strategies to reduce response times, thereby enhancing customer satisfaction and ensuring timely support

Calculation: This metric calculates the average resolution time for customer contacts. This metric helps understand how efficiently customer issues are being resolved.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Skill, Campaign, Company

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

Supported channel: Digital

Metric type: Historical

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

Use Case: Monitoring Resolution Time Efficiency- A contact center uses this metric to monitor the efficiency of resolving customer issues. By understanding the average resolution time, management can identify areas for improvement in agent performance and implement strategies to reduce resolution times, thereby enhancing customer satisfaction and ensuring timely support.

Calculation: The total number of unique contacts that have been successfully closed. This metric represents the count of distinct contacts where the interaction has been completed and closed by the agents.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Skill, Campaign, Company

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

Supported channel: Digital

Metric type: Historical

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

Use case: Tracking Successfully Closed Contacts- In a contact center, tracking the number of unique contacts that have been successfully closed is important. It helps understand how efficient and effective the agents are. A high number of closed contacts shows that agents are good at resolving customer inquiries and closing interactions. This metric can help identify trends in agent performance, highlight areas where agents excel, and find opportunities for further training or process improvements. Additionally, it can be used to set performance targets and measure the success of initiatives aimed at improving contact resolution rates.

Calculation: The total number of responses received from customers. This metric represents the cumulative count of customer responses during interactions with agents, providing insight into customer engagement and feedback.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Skill, Campaign, Company

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

Supported channel: Digital

Metric type: Historical

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

Use case: Evaluating Customer Engagement Through Response Count- In a contact center, tracking the total number of customer responses is crucial. It helps understand how actively customers are engaging during interactions. For example, if the number of customer responses is high, it shows that customers are actively participating in conversations, asking questions, and giving feedback. This metric can help identify trends in customer engagement, measure the effectiveness of communication strategies, and ensure that agents are fostering a responsive and interactive environment. Additionally, it can be used to assess the impact of customer service initiatives and find areas for improvement in agent-customer interactions.

Calculation: The total number of times agents have focused on specific tasks or interactions. This metric represents the cumulative count of focus instances, indicating how often agents are concentrating on particular activities or customer interactions.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Skill, Campaign, Company

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

Supported channel: Digital

Metric type: Historical

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

Use case: Assessing Agent Focus on Customer Interactions- In a contact center, tracking the total number of focus instances is crucial. It helps understand how often agents are dedicating their attention to specific tasks or customer interactions. For example, if the focus count is high, it suggests that agents are frequently concentrating on important activities, which can lead to better customer service and higher efficiency. This metric can help identify trends in agent behavior, measure the effectiveness of focus-related training programs, and ensure that agents are prioritizing their tasks appropriately. Additionally, it can be used to assess the impact of initiatives aimed at improving agent focus and productivity.

Calculation: The total number of seconds agents are actively engaged in interactions through specific channels. This metric represents the cumulative active time agents spend on interactions through digital channels.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Skill, Campaign, Company

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

Supported channel: Digital

Metric type: Historical

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

Use case: Evaluating Agent Active Time Across Digital and Other Channels: In a contact center, tracking the total active time agents spend on interactions through digital channels (such as channels 1 and 7) and other specified channels is crucial for understanding channel utilization and agent productivity. For example, if agents are spending significant time on digital channels, it may indicate a high volume of customer interactions through online chat, email, or social media. This metric can help identify trends in digital channel usage, measure the effectiveness of digital communication strategies, and ensure that agents are efficiently managing their time across various channels. Additionally, it can be used to optimize resource allocation and improve overall customer service by focusing on the most utilized channels, both digital and otherwise.

Calculation: The total number of agent responses, excluding the first response for each interaction. This metric represents the cumulative count of follow-up responses made by agents during interactions, which helps in understanding the level of engagement and follow-up required after the initial response.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Skill, Campaign, Company

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

Supported channel: Digital

Metric type: Historical

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

Use case: Analyzing Agent Follow-Up Responses- In a contact center, tracking the number of follow-up responses made by agents is crucial for understanding the depth of engagement required to resolve customer inquiries. For example, if the number of follow-up responses is high, it may indicate that agents need to provide additional information or clarification after the initial response. This metric can help identify trends in customer interactions, measure the effectiveness of initial responses, and ensure that agents are providing thorough and satisfactory follow-ups. Additionally, it can be used to assess the impact of training programs aimed at improving the quality of agent responses and reducing the need for multiple follow-ups.

Calculation: The total number of seconds agents spend on follow-up responses. This metric represents the cumulative time agents dedicate to providing additional responses after the initial interaction with customers.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Skill, Campaign, Company

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

Supported channel: Digital

Metric type: Historical

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

Use case: Measuring Agent Time Spent on Follow-Up Responses- In a contact center, tracking the total time agents spend on follow-up responses is crucial for understanding the effort required to resolve customer inquiries fully. For example, if agents are spending significant time on follow-up responses, it may indicate that initial interactions are not fully addressing customer needs, necessitating further communication. This metric can help identify trends in follow-up response times, measure the effectiveness of initial responses, and ensure that agents are providing thorough and timely follow-ups. Additionally, it can be used to assess the impact of training programs aimed at improving the quality of initial responses and reducing the need for extensive follow-ups.

Calculation: This metric represents the amount of time taken to resolve a contact within a specified period. It indicates the duration from when a contact is initiated to when it is resolved, specifically for digital channel contacts.

Filters:

• Employee group: Company

• Contact group: Skill, Campaign, Company

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

Supported channel: Digital

Metric type: Historical

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

Use case: Enhancing Resolution Efficiency for Digital Channels- In a contact center for an e-commerce platform, the Resolution Time metric helps monitor and improve the efficiency of resolving customer inquiries through digital channels (e.g., chat, email). A high resolution time may show that digital inquiries take longer to resolve, possibly due to complex issues or inefficient processes. The contact center can look into causes like agent training, process bottlenecks, or technical issues. By fixing these, the center can reduce resolution times, ensuring customers get timely and effective support. This metric also helps set benchmarks and goals to improve resolution efficiency, boosting overall customer satisfaction and service quality.

Calculation: This metric represents the total number of interactions between agents and customers within a specified period. It indicates the cumulative number of responses exchanged between agents and customers during calls.

Filters:

• Employee group: Agent, Team, Company

• Contact group: Skill, Campaign, Company

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

Supported channel: Digital

Metric type: Historical

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

Use case: Evaluating Customer Engagement and Interaction Quality- In a contact center for a technology company, the Total Interaction Count metric helps evaluate engagement and interaction quality between agents and customers. A high total interaction count may show that agents are actively engaging with customers and addressing their concerns thoroughly. Similarly, a low metric count may suggest brief interactions 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 interaction levels, boosting overall performance and customer satisfaction.

Calculation: This metric represents the number of contacts associated with each tag within a specified period. It indicates how many contacts are linked to each tag.

Filters:

• Employee group: Company

• Contact group: Company

• Attributes: Tag name

Supported channel: Digital

Metric type: Historical

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

Use case: Analyzing Tag Usage for Improved Categorization- In a contact center for a software company, the Tag Count by Contact metric helps analyze how tags are used to categorize customer inquiries. A high count for certain tags may show that those tags are often used for specific types of inquiries. The contact center can look into why certain tags are used frequently, such as common issues or frequent topics. By understanding tag usage, the center can optimize the categorization process, ensuring inquiries are accurately and efficiently categorized. This metric also helps set benchmarks and goals for tag usage, boosting overall organization and management of customer inquiries.