Every customer support team needs to be aware of this critical KPI that measures the speed in which a customer gets their first response. This metric largely influences the quality of support that you provide your customer
Two key reasons for focussing on your first response time (FRT)
First response time is always a critical metric to be tracked and improved on. Here’s why.
- Customer experience: Whether you are resolving a complex issue or a simple ‘How-To’ query, a faster first response strengthens the customer relationship.
- Establishing trust: A quicker response time becomes critical when customers are facing trouble. When they report incidents, it is essential to acknowledge the issue with an informed response.
Measure how fast you respond
Once a ticket is created, measure the time taken by an agent to add the first response to the ticket. This first reply time needs to be captured at a ticket level. To aggregate the First Response Time for all tickets, calculate the average of all the First Response Times of each ticket.
Average First Response Time = Sum of First Response Time of all tickets / Total Number of Tickets.
An efficient ticketing system is required in order systemically capture response times.
Five ways to analyze and improve on your first response
Once you measure your first response time, the next step is to analyze it deeper in the context of your operations and make decisions to improve your performance. Here are five ways to achieve it.
1. What type of tickets are you responding slow
One way to find the root cause of slow response times is to look at the response times of tickets by multiple attributes. These ticket attributes could be any of the following:- Type of the incoming request, assignee of the ticket, location, department, module, priority, support channels or any other attribute that you capture as part of your ticket.
You can typically do this attribute-wise analysis and derive enormous insights into a single numerical metric like First Response Time. This helps you to make decisions like
- Identify support agents who are responding slow when compared to others
- Check if high priority tickets are getting immediate attention.
- Deep dive into departments, customer segments who are not getting an immediate response
- Check if incidents and issues are promptly responded
2. Going beyond averages: Get a statistical perspective of your first response time
Measuring the number of minutes or hours for the first response is the first step. It gives you the awareness of your current reality and enables you to dig deeper into data and make better decisions.
It is critical to understand this metric beyond average time. While the average value of the First response gives you an introductory perspective, knowing statistical measures like standard deviation, coefficient of variability gives you deeper insights into process maturity and performance. For example, the standard deviation value (3.17) gives you a sense of the spread of first response times from the average value. Additionally, the coefficient of variation indicates the ratio of Standard deviation to the mean. A lower value of CV indicates a disciplined process of agents in responding to customers.
HappyFox BI can help you get a statistical perspective into data and perform deeper analytics.
3. Knowing your outliers
While you have first response time calculated by considering all support requests, it is critical to know the distribution of first response times. i.e How many tickets have First response times in the different time intervals. Let’s take an example.
Response time is typically measured in hours. If you were to know the count of tickets for each 4-hour interval of First response time, the above histogram would help you with it. In this example, it shows tickets are primarily responded within 4 to 8 hours or 8 to 12 hours. However, what you begin to realize is the count of that is beyond the 16-hour limit. This gives you an insight into the outliers that exist.
By drilling down in the final section gives you to see the actual tickets being responded over 16 hours. This enables you to understand special cases and make better decisions while committing to service level agreements.
4. When during the day are you responding the slowest
As you begin to manage larger teams, you need to ensure your teams working in different shifts consistently maintain the response SLA. You can begin to do this by analyzing First response time simultaneously involving 2 dimensions – Hour of the day and Ticket volume
The above graph shows you how the first response times vary over the hour of the day. You can identify your business hours where the response time is fastest or slowest. Additionally, compare it with the ticket volume. Is the response getting slower due to the higher volume of tickets? Is it slower due to resource unavailability during the hour? Are your agents getting a time out during that particular hour? You may be able to answer your queries that you face and take better decisions to improve your operational efficiency.
5. Time trending analysis of your First Response Time
As your customer service team matures over time, it is important to objectively gauge the improvements that they have made. Analyzing the trend of first response time over the past weeks, months and years is a useful tool.
You begin to notice patterns over time. As you compare each time frame with the major milestones or changes that occurred in your team, you are able to understand the influence of changes that occur within the first response time of agents. Also, if you have brought in initiatives at various time periods with a focus on bringing down first response time, you would know if they really worked.
Leveraging automation for bringing down your average response time
One major way to drastically reduce first response times is by embracing automation. With workflow automation tools like HappyFox Workflows, you could send automated responses to customers. This ensures a timely response to customers and also agent productivity. You can also set up triggers to automatically notify stakeholders during times of SLA breach.
Analyzing first response times 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 satisfaction. 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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