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First Response Time (FRT)

Definition

First Response Time (FRT) is a customer support metric that measures the elapsed time between when a customer submits a support request and when they receive the first meaningful response. It is one of the most important indicators of support responsiveness.

How it works

FRT is measured differently by channel:

  • Email: Time from email receipt to first agent reply (SuperOffice measured a 12-hour, 10-minute average)
  • Live chat: Time from customer message to first agent response (LiveChat reported a 35-second global average)
  • AI chatbot: Time from customer message to AI response (seconds, at any hour)

FRT does not measure resolution time, it only tracks the first response. However, fast FRT strongly correlates with higher CSAT because customers feel acknowledged and valued when they get a quick initial response.

Why it matters

Customers increasingly expect instant responses. In HubSpot's research, 90% of customers rate an immediate response as important, and 60% define immediate as 10 minutes or less. An AI agent replies in seconds at any hour, which is hard for a human-only team to match. Reducing FRT is one of the clearest ROI justifications for AI customer support.

How Chatsy uses it

Chatsy's AI replies as soon as the answer is ready, so the first response never waits for an agent to come online. For conversations that escalate to human agents, the AI has already gathered context and information, reducing the effective human FRT.

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What else should I know about First Response Time (FRT)?

AI chatbots reply in seconds at any hour, which is hard for human-only teams to match

Key takeaways

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Example scenarios

  1. 01

    AI chatbot instant response

    A customer asks "what are your business hours?" at 2 AM. The AI chatbot responds in 1.8 seconds with the correct answer. Without AI, this email would sit in the queue until 9 AM: a 7-hour FRT versus under 2 seconds.

  2. 02

    Peak traffic FRT consistency

    During a Black Friday sale, a retailer receives 5x normal chat volume. Human agents have a 12-minute FRT due to queue backup, but the AI chatbot maintains a consistent 2-second FRT for the 75% of questions it can handle automatically.

  3. 03

    Human FRT improvement via AI context

    When a conversation escalates to a human agent, the AI has already collected the customer email, order number, and issue description. The agent responds with a substantive answer in 45 seconds instead of the usual 3 minutes of information gathering.

Key takeaways

  • FRT measures the time between a customer submitting a request and receiving the first meaningful response
  • AI chatbots reply in seconds at any hour, which is hard for human-only teams to match
  • HubSpot: 60% of customers define an immediate response as 10 minutes or less
  • Fast FRT strongly correlates with higher CSAT because customers feel acknowledged quickly
  • AI chatbots maintain consistent FRT regardless of time of day, holidays, or traffic spikes

Frequently asked questions

For live chat: under 1 minute. For AI chatbots: under 5 seconds. For email: under 1 hour (SuperOffice measured a 12-hour, 10-minute average). AI chatbots deliver the best FRT consistently because they respond instantly 24/7.

Yes. Customers who wait less tend to rate the interaction better, and HubSpot found that 90% of customers rate an immediate response as important. The relationship is strongest in chat support, where customers expect real-time interaction.

Sum all individual first response times and divide by the number of conversations. Most support platforms calculate this automatically and segment by channel (email, chat, AI). Exclude outliers like tickets submitted over weekends if you measure business-hours FRT.

FRT measures only the time to the first reply. Resolution time measures the total time from initial contact to the issue being fully resolved. A conversation can have a fast FRT (instant AI reply) but a longer resolution time if it requires multiple exchanges or human escalation.

Related terms

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Further reading

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