This chart displays the number of tickets registered per day over the past five weeks. Two lines are shown:

• Light blue: The daily average number of tickets based on the 13 weeks prior to the displayed period. This average is calculated per weekday — for example, the value shown for a Monday represents the average of all Mondays in those 13 weeks. This provides a baseline for what is typically expected on each day of the week.
• Dark blue: The actual number of tickets registered on each specific day.

By comparing these two lines, you can quickly identify whether the ticket volume on a given day was higher or lower than usual. This helps to spot trends, peaks, or anomalies, which may be caused by seasonal effects, campaigns, disruptions, or other external factors.

This chart shows the number of active connectors per day over the past five weeks. Two lines are displayed:

• Light blue: The average number of active connectors per weekday, based on the 13 weeks prior to the displayed period. For example, the value shown for a Tuesday represents the average of all Tuesdays in those 13 weeks. This provides a baseline for what is typically expected on each day of the week.
• Dark blue: The actual number of active connectors recorded on each specific day.

By comparing these two lines, you can quickly identify whether the number of active connectors on a given day was above or below the expected level. This helps to detect trends, anomalies, or shifts in usage patterns, which may be influenced by technical issues, customer behavior, or external events.

This chart presents the daily revenue over the past five weeks. Two lines are shown:

• Light orange: The average daily revenue per weekday, calculated over the 13 weeks prior to the displayed period. For example, the value shown for a Friday represents the average revenue of all Fridays in those 13 weeks. This provides a benchmark for what is typically expected on each day of the week.
• Dark orange: The actual revenue recorded on each specific day.

By comparing these two lines, you can easily identify days where revenue was significantly higher or lower than usual. This helps to detect patterns, anomalies, or the impact of specific events, promotions, or external factors on daily performance.

This chart displays the volume of incoming work for the most recent day, divided into two categories:

• Primary tickets: These are tickets submitted directly by our own customers. They represent the core workload from our direct client base.
• Secondary tickets: These tickets are received via other service providers within the network. They reflect collaborative or externally sourced service activity.

This snapshot provides a clear view of the distribution of incoming work on the latest day, helping to assess the balance between direct and network-driven service demand.

This chart provides insight into how incoming work was processed on the most recent day. The work is categorized into three handling types:

• Outsourced: Tickets that were forwarded to another service provider within the network.
• Automated: Tickets that were resolved automatically using Automated Functions, without manual intervention.
• Inbound work: Tickets that were picked up and resolved by your internal team.

This breakdown helps to evaluate operational efficiency and the distribution of workload across automation, internal resources, and external partners.

This chart shows the average number of tickets handled per operator per day over a four-week period. Additionally, it displays the average success rate for each day.

• Average tickets per operator: This metric reflects the daily workload distribution across the team, helping to assess operational capacity and individual productivity.
• Success rate: This indicates the percentage of tickets successfully resolved, providing insight into service quality and efficiency.

By combining these two metrics, the chart offers a balanced view of both workload and performance, helping to identify trends, bottlenecks, or opportunities for improvement in team operations.

This chart displays two key metrics per day over a four-week period:

• Average lead time: The average duration from ticket creation to resolution. This reflects the overall time it takes to fully handle a ticket.
• Average pickup delay: The average time between ticket creation and the moment it is first picked up by an operator. This indicates how quickly incoming work is being addressed.

Together, these metrics provide insight into both responsiveness and processing efficiency. Monitoring these trends helps identify bottlenecks, evaluate team performance, and improve service delivery.

This chart visualizes the relationship between average hourly earnings and average ticket duration per brand, using an XY scatter plot:

• Y-axis (vertical): Represents the average hourly earnings per brand, calculated based on ticket handling time and revenue.
• X-axis (horizontal): Shows the average duration (in minutes) of tickets for each brand.
• Bubble size: Indicates the ticket volume per brand — larger bubbles represent brands with a higher number of tickets.

This visualization helps identify which brands generate higher earnings relative to the time spent per ticket, and how ticket volume correlates with efficiency and profitability.