...
With the information collected by various modules (interactions, tickets, tasks, etc.), Sagicc generates a dashboard with statistics on the work and performance of each campaign. Users with administrator and supervisor roles can access the dashboard for each campaign, analyze the presented information, and make decisions to help improve productivity and service quality. By default, when entering the Campaign Dashboard, the information for the campaign selected by the user in the Campaign Selector will be displayed.
| Info |
|---|
Only users with administrator and supervisor the "Administrator" and "Supervisor" roles can access the Campaign Dashboards. Users with the "Supervisor" role will only be able to view the dashboards for the campaigns assigned to them. |
You can access the Campaign Dashboard through the sidebar menu.
...
This section shows the total number of managements performed by agents on campaign tickets, within the date range specified in the filter, grouped by the creation hour. This information will allow you to identify the hours of highest and lowest agent productivity during the workday, helping you to improve operational efficiency.
Recent Tasks
In this section, you can quickly access the latest tickets managed by users in the campaign. The list will show basic information about each managed ticket, and by clicking on the ticket number, you will be redirected to the management view of the ticket.
Service Level, Productivity, and Average Handling Time (AHT)
...
AHT (Average Handling Time): The AHT compliance percentage is calculated by comparing the actual handling time of the tickets (time from ticket creation to closure) with the average handling time configured in the campaign. If the campaign has an AHT configured for 600 seconds (10 minutes), and out of 50 tickets handled, 40 were resolved in less than 600 seconds, the AHT compliance percentage will be 80%. This indicator is key to measuring how quickly tickets are resolved, which directly impacts customer satisfaction and process efficiency.
Interactions by Hour
...
This section shows the total number of interactions received (incoming) or generated (outgoing) by the agents in the campaign, within the date range specified in the filter, grouped by the hour of interaction creation. This information will allow you to identify the hours with the highest and/or lowest volume of interactions during the day, helping you optimize your team's resources.
Interactions by Channel
...
This section shows the total number of interactions received (incoming) or generated (outgoing) by the agents in the campaign, within the date range specified in the filter, and grouped by channel. This information will allow you to determine the channels with the highest and/or lowest volume of interactions, helping you optimize your team's resources.
Tickets by Channel
...
This section shows the number of tickets assigned to the campaign agents and created within the date range specified in the filter, grouped by the type of channel that generated the ticket. This information will allow you to determine which channels are most and least used by customers to initiate conversations with the company, and focus your strategies on these channels.
SLA
This section shows and compares data related to SLA rules and the compliance/non-compliance with the times associated with these rules. You will find an indicator showing the total number of tickets in the campaign for which SLA rules were validated, and another showing the total number of tickets with non-compliance, meaning tickets where the times for at least one SLA metric were not met.
...
This section shows a detailed table of SLA metric compliance or non-compliance for the 20 most recent tickets in the campaign. The table includes the ticket number, the assigned agent, the evaluated metric, the compliance status, and the date corresponding to the compliance or non-compliance.
SLA Compliance Effectiveness
...
This section offers a detailed comparison between the percentage of tickets that achieved compliance with both SLA metrics and those that failed to meet one of them. The analysis helps to identify the proportion of tickets that reached the established service goals, contrasted with tickets that showed deviations in meeting critical metrics.
| Cambiar historial |
|---|