“Ticket volume is so simple to track, yet so hard to manage” Every customer support team aims to be aware of the volume of the incoming and outgoing tickets. The ability to interpret this information and make better decisions determines the success of the support team.
Two key reasons for mastering this metric
Ticket volume is always a critical metric to be tracked and improved on. Here’s why.
- Conveys business pattern (demand): Understanding ticket volume gives you important clues to gauge customer demand. You begin to predict demand based on cyclicity and past volumes.
- Promotes better staffing (supply): When you know your ticket volume well, you can better estimate the required support agents and the areas to focus on. You could match supply with the incoming demand.
Measure your inflows
Measure ticket volume by the number of customer requests that arrived over a particular time frame. It would help you if you are measuring the ticket volume at every instant of the day, week, month, and so on.
Ticket Volume = Total number of tickets over a defined time period
Additionally, for every ticket, you could capture the product, services, or offerings for which the ticket is raised. You can use custom fields to capture such information. These will be invaluable at a later stage when you analyze your ticket volume.
Three ways to analyze and improve your performance
Once you measure this KPI, the next step is to analyze it deeper in the context of your operations and make decisions to improve your performance. Here are three simple ways to achieve it.
1. Analyze patterns of ticket volume over time
The best way to start analyzing your ticket volume is to know how this metric trends over time. This gives you important clues to understand your business demand.
It is important to analyze the ticket volume across different time frames so as to get a holistic view of your business demand. Analyze your inflow by daily, weekly, monthly, quarterly, and yearly. You can also correlate peaks and troughs with business process changes, major events that are happening in public, and user behavior.
You can also compare the volume of data across different time periods. For example, a support team for an eCommerce business can compare the volume of tickets of a particular sale week of the current year with that of the previous year. An internal support team can see the ratio of internal requests of before and after key compliance audits.
2. Finding your peak hours and detecting cyclicity pattern
Every dataset tends to show cyclicality patterns over time. While you get to know the volume of tickets coming in daily and weekly frequencies, it helps to understand the time period during which ticket inflow is high during your day. You can track tickets by the hour of the day that shows the hours during which your volume is high. This helps you to plan your agent shifts effectively.
Similarly, when you know the volume by the day of the week, you can ensure increased staffing during those days in a week. In the same way, the week of the month and month of quarter gives you insights based on which you can make informed decisions for your support.
You can extend the above concept to ‘week of the month’, ‘month of the quarter’ also. As you do this, you get to know the cyclicity pattern revealed by your data.
3. Analyze volume by various ticket types
While it is important to understand the total ticket volume, it is critical to know the ticket volume by the various products, services, or offerings that you serve your customers. Doing this is a fundamental activity while analyzing your support tickets.
True to the phrase ‘A dollar saved is a dollar earned’, you would help your support by spotting ticket types where there is an increased proportion of tickets. Then, for a given ticket type, you need to know the distribution of tickets by the sub-types. A doughnut chart with a drill-down would serve you well.
Once you begin to know the high-density ticket areas of your business, you could make decisions to address each aspect of it. A heat map can also enable you to quickly spot the high-volume areas across your support areas and modules.
Here are two aspects that a customer support manager can focus on after identifying the high volume areas of the support.
- Increase user awareness – Frequent tickets that primarily involve FAQs and giving information to your customers can be areas where you increase your knowledge base. A robust customer support software can help you provide all the required capabilities.
- Improve your product and service capabilities – Areas where customers predominantly raise issues or Incidents need strengthening. You can plan to address those customer pain points and requests.
Reduce ticket volume by increasing your deflection rate
Every ticket that needs human attention and effort adds to the cost of the support team. As you face high ticket volume, every support manager needs to find ways to deflect a portion of the incoming tickets. Here are three ways to achieve this is by
- Implement workflow automation – Workflow automation software like HappyFox Workflows can automatically respond to tickets, perform data entry operations, and auto-close tickets. Based on your business process, automation can prove to be an invaluable tool.
- Deploy a chatbot for your customers – AI Chatbot will help you deflect tickets and reduce support volume. HappyFox Chatbot is a custom-built bot solution that can help your business.
- Encourage self-service for users – Setting up a strong knowledge base would provide your customers with a rich source of self-remedy. HappyFox provides you a simple online knowledge base software that’s searchable, social media ready, and responsive for the mobile.
Conclusion
Analyzing ticket volume through multiple ways is fundamental to better understand your support team and also take the right decisions to improve them. This ensures the highest levels of customer experience. HappyFox BI, a cutting edge BI tool that is built for customer support teams can empower you to make data-driven decisions and ensure maximum business value.
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