Customer Service Metric: A Deep-dive Into First Response Time

Every business today focuses on improving what’s called the first response time, the time elapsed between when customers make a query and when they get the first response. Whether you are trying to answer a product inquiry, address a disappointed customer, or close a sales deal, responding fast could make all the difference. 

Let’s take a deeper look at the first response time, why you should measure it, and how to improve. 

Why Should You Measure the First Response Time?

First response time is a critical customer support KPI you can’t overlook anymore — as it can help you improve customer satisfaction, increase conversions, and even win back customers. First response times drive better customer experience and brand trust. 

  1. Customer experience: Customers don’t want to wait for answers, particularly those who are facing trouble. When you reply fast, you put them at ease and enable a friendly atmosphere for a mutually beneficial conversation.
  2. Brand trust: A quicker response time becomes critical when a customer is in the middle of a purchase decision or an issue. Having to wait could be the last straw for your customers. A fast reply could restore their trust and make them feel heard.

Although the below examples share similar conversations, you can see the difference a fast response makes. Case A shows a frustrated customer abandoning the support process and case B shows a similar frustrated customer calmed by a quick first response. 

A

Customer: “Hello. Why is my user access revoked even after making the payment?”

—- Please wait. Our chat agent will be right with you. —-

Customer: “There?”

(Left)

B

Customer: “Hello. Why is my user access revoked even after making the payment?”

—- Please wait. Our chat agent will be right with you. —-

Agent: “Hello! I’m happy to assist you today 🙂 Can you please give me the payment details so I can check right away?”

Customer: “Sure. Please give me a minute”.

How to Measure the First Response Time?

Once a ticket is created, measure the time taken by an agent to add the first response to the ticket. You need to capture it at a ticket level.

Average First Response Time = Sum of First Response Time of all tickets / Total Number of Tickets.

Remember, automatic responses don’t count. A first response always refers to that made by a customer service representative.

Now that you have measured the first response time, you need to put it under the microscope and assess the response patterns. What leads to a delayed first response? Which agents respond the fastest and how do they manage to do that? You need these questions answered in order to guide your improvement efforts in the right direction. There are many ways to do that. 

Five Steps to Measure & Improve Your First Response Time

1. Find Which Tickets Get Delayed Responses

One way to find the root cause of slow response times is to analyze the ticket response times by multiple attributes: Type of the incoming ticket, assignee of the ticket, location, function, module, priority, complexity, support channels, and any other attribute you capture when a ticket is created. You can use attribute-wise analysis to gain deeper visibility into your response times.

Benchmarks help you identify the right type of tickets to focus

All tickets are created equal but not all responses; you may prioritize a product inquiry or a high-level ticket over a simple query. The analysis will help you streamline and improve this process of triaging tickets and, in turn, improve the response times. 

You can leverage the results to inform many important decisions:

  1. Identify support reps who typically respond slow
  2. Check if high-level tickets are getting immediate attention 
  3. Deep dive into functions, tickets, and channels that receive the most delayed first responses
  4. Design an effective workflow to triage incoming tickets based on priority

2. Get a Statistical Perspective of Your First Response Time

The next step is to go beyond the averages and measure the number of hours or even minutes upon which you send your first responses. It is critical to understand this KPI beyond the average time. While the average value of first response times gives you an introductory perspective, understanding statistical measures like standard deviation and coefficient of variation gives you the drilled-down insights you need to drive specific improvements.

Statistical measures give you the right perspectives on data

Indeed, an average first response time is easily comparable with industry benchmarks. But statistical measurements can give you a microanalytical perspective of first response times from the average value. For instance, a lower value of the coefficient of variant indicates a systematic process of agents in responding to customers.

Tools like HappyFox Business Intelligence can help you get a statistical perspective into data and perform deeper analytics.

3. Know Your Outliers

Once you have calculated the first response time, you need to understand its distribution across service requests. Let’s take an example.

A histogram indicates the distribution of FRT

Response time is typically measured in hours. If you were to know the ticket count for each 4-hour interval of the first response time, the above histogram would help you with it. In this example, tickets receive responses within 4 – 8 hours or 8 – 12 hours. However, what you can also calculate is the count beyond the 16-hour limit. This gives you an insight into the outliers that exist.

You can see the actual tickets being responded to over 16 hours. This helps you understand special cases and make better decisions when committing to service-level agreements (SLA).

4. Find When You Respond the Slowest

As you begin to manage larger teams, you need to ensure that your teams working in different shifts consistently maintain the response SLA. You can get started by analyzing the first response time with 2 dimensions: the hour of the day and the ticket volume.

FRT and Tickets Count showed by the hour of the day

The above graph shows you how the first response times vary over the hour of the day. You can identify the business hours when the response time is fastest and vice versa. Additionally, you can compare it with the ticket volume. Is the response getting slower due to any spike in the ticket volume? Is it slower due to the resource unavailability during the hour? Or simply, are your agents on time-out during that particular hour?

Multilevel graphs may sound complex, while in reality, HappyFox Business Intelligence can help you generate custom graphs in minutes.

5. Leverage Automation to Cut the Grunt Work

One way to drastically reduce the first response time is automation. With workflow automation tools like HappyFox Workflows, you could automate recurring processes in ticket management. You can also set up triggers to automatically notify stakeholders during times of SLA breach. As a result, your agents can drive focus where it matters, in major customer issues that require immediate attention.

A time-series graph enables you to see patterns over time

Finally, As your customer service team grows, you should start gauging their improvement efforts objectively. Analyze the first response times over weeks, months, and years for a broader perspective. Because breakthroughs only happen over time.

First Response Time Matters More than You Think

Better first response times drive better customer experience, enhance brand trust and loyalty, and position your brand as customer-centric. The stakes are high — it’s high time to make first response time your competitive differentiator. 

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. Get a demo of HappyFox BI to accelerate your analytics journey.