Component | Description | Uses |
Tickets Overview | Displays the number of requests in important statuses like open, on hold, overdue, due today, and due in an hour as cards | Helps you prioritize tickets and solve them on time. |
Ticket Stats | The number of requests in different states over time represented as a stacked graph. | Identify trends that you can capitalize on or correct course. |
Traffic Analysis | The number of tickets submitted from each channel represented as a pie chart. | Identify the channels that receive the most and least traffic to manage resources allocation and improve service. |
Average Handling Time | First response time, average response times, and resolution time each represented separately using meter graphs. | Evaluate the quality and efficiency of your service. |
Happiness Rating | The number of good, okay, and bad ratings received along with the total count depicted as a pie chart. | Find out if your customers are happy with your service. |
Component | Description | Uses |
Open request by status | The number of open tickets in each status visualized as a bar graph. This component only displays the current stats. | Identify the status that stagnates the open tickets. |
Requests by state | The number of tickets in each state is illustrated in a stacked graph. | Get insights on how efficiently the tickets are resolved. |
Re-opened request | The number of tickets reopened each day is displayed in a bar graph. | Identify recurring issues. |
Average handling time by status | The time tickets spend in each status represented in a bar graph. | Identify the status that delays the resolution. |
Average handling time by agents | The average handling time of each agent in a tabular view. This component can be filtered by status. | Evaluate individual agent performance. |
Component | Description | Use |
Customer happiness comparison | The number of good, okay and bad ratings received over a course of time represented as a stacked graph | Identify patterns and trends in customer happiness. |
Happiness ratings | A donut chart that visualizes the number of good, okay and bad ratings received against the total number of ratings. | Gauge the efficiency of your service. |
Happiness rating by agents | A tabular view of the percentage and count of good, bad, and okay ratings each agent received. | Evaluate agent performance and set targets for your team. |
Customer happiness analytics | A tabular view of the feedback associated with the rating received by one or all of your agents. | Learn what your customers liked or did not like about the help they received. |
Component | Description | Uses |
SLA overview report | Displays the number of SLA violated tickets, SLA violations, residual time and violation time as cards. | Get an overview of these key SLA health signifiers in a glance. |
SLA violated tickets | A bar chart that displays the number of request that breached SLA targets over a period of time. | Identify complex tickets that require a significant amount of time to solve. |
Achieved vs Violated tickets | A donut representation of the number of requests that achieved and violated the applied SLAs. |
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Achieved vs violated count | A donut chart visualization of the number of times an SLA target was achieved to and violated |
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Violations by SLAs | A tabular view of the number of times an SLA was applied, attained and violated | Helps you analyze and optimize your SLA policies. |
Violations by Agents | A tabular view of the number of times an SLA target was applied to an agent's ticket and the number of times they achieved and violated them. | Discover how well your agents are adhering to SLAs. |
Violations by time | Displays the number of SLA violation at every hour in the current day in a line chart. | Identify the times when SLAs are violated the most to improve your workflows. |
Violations by status | The number of violations in each ticket status visualized as a bar chart. | Pinpoint the status that contributes to the maximum violations and see if you could use the onhold state for better compliance. |
Violations by channels | The number of violations in each support channel visualized as a bar chart.
| Determine the channel that has the maximum violations and check if more agents could be assigned to handle requests in those channels. |
Components | Description | Uses |
Call metrics | A line graph to show number of answered or missed, inbound and outbound call each day over a period of time. | Identify peak times and period of inactivity for better resource allocation. |
Inbound vs Outbound | A donut chart representation of the number of inbound and outbound calls against the total number of calls. | Understand your phone channel traffic. |
Inbound answered vs missed | A donut chart depiction of the number incoming calls that were answered and missed. | When you know the number of calls you've missed, you can drill down to the reason behind missing those calls and schedule follow-ups. |
Outbound answered vs missed | A donut chart depiction of the number of answered and missed outbound calls. | Find out how often your agents reach out to your customers proactively. |
Agent call stats | A tabular view of the number of calls associated with an agent, the number of calls they accepted (inbound), the number of calls they made and the overall time they spent on calls (talk time) | Monitor your phone agents performance. |
Components | Description | Uses |
Online | A list of agents who are available, on call, or engaged in after call work, and how long they've been in that status. | Know which agents are available to take calls and the agents who will soon be freed up. |
Busy | A list of agents who are engaged on calls and how long they've been in that status. | Know which agents are unavailable to take calls. |
Offline | A list of agents who are offline and how long they've been in that status. | See which agents are offline for a prolonged amount time. |
Component | Description | Uses |
Active Blueprint tickets | A multi graph representation of the number of tickets that currently have a blueprint applied and the number of tickets that have violated a state-level SLA. | Monitor and evaluate process adherence. |
SLA violated states | A bar graph to depict the number of times each blueprint state violated a state-level SLA. | Discover which blueprint states that act as bottlenecks in your process. |
SLA violated tickets | A dial chart that visualizes the number of tickets that adhered to and violated a state-level SLA in a blueprint. | This component can help you analyze the cause of violations and devise strong escalation procedures to deal with them. |
Average time per blueprint | A bar chart representation of the average amount of time tickets spend in a blueprint. | Zero in on the blueprints that are tedious and time-consuming and optimize them. |
Average time per state | A bar chart representation of the average amount of time tickets spend in a blueprint state. | Find out which the blueprint states that slow down your process. |
Transition occurrence | A bar chart depiction of the number of times each blueprint transition is performed. | Evaluate the frequency of transition occurrence and identify the most performed transitions. |
Component | Description | Uses |
Trend | Multi-line graphs to show the trend in incoming/outgoing responses for the present day and the previous 30 days along with anomalies. | Identify peak and drop times along with anomalies. These insights can be used to manage resource allocation. |
Trending auto tag | A word cloud of the most used ticket tags in the last 24 hours. | Identify the issues for which you get the most tickets. |
Sentiment analysis | The count and percentage of positive, neutral and negative sentiments in ticket responses, depicted using appealing emoticons. Time frame - last 24 hours | Get insights on the general sentiment of customers towards your business and service. |
Sentiment trend analysis | A stacked bar chart to show the number of positive, negative and neutral ticket responses over a period of time. Available time filter - last 24 hours, last 7 days. | Identify trends in customer sentiment and use them to enhance the quality of your support. |
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