{"id":359122,"date":"2022-05-23T16:31:57","date_gmt":"2022-05-23T23:31:57","guid":{"rendered":"https:\/\/www.questionpro.com\/blog\/?p=359122"},"modified":"2026-07-23T01:40:29","modified_gmt":"2026-07-23T08:40:29","slug":"time-to-resolution","status":"publish","type":"post","link":"https:\/\/www.questionpro.com\/blog\/time-to-resolution\/","title":{"rendered":"Time to Resolution: What It Is, Formula, and How to Reduce It"},"content":{"rendered":"\n

The moment a customer asks for help, a clock starts. It only stops when they get a complete answer. This total wait time is known as time to resolution. It is a vital metric that directly impacts customer loyalty and agent efficiency.<\/p>\n\n\n\n

Simply tracking the hours is not enough, though. Many teams make costly mistakes by prioritizing speed over actual problem-solving. You need the right context to see where you truly stand.<\/p>\n\n\n\n

In this blog, we’ll break down what time to resolution really means. We will also cover the exact formula, current channel benchmarks, and how to avoid common tracking mistakes.<\/p>\n\n\n\n\n\n

What is time to resolution?<\/strong><\/h2>\n\n\n\n

Time to resolution (TTR) is the average time between a customer opening a support request and that request being marked resolved. It’s also called mean time to resolution (MTTR), average resolution time, or full resolution time. The name changes depending on which help desk or reporting tool your team uses.<\/p>\n\n\n\n

TTR covers the entire lifecycle of a ticket, not just the first reply. A conversation might get an instant acknowledgment but still take three days to actually fix. That ticket still counts as a three-day resolution. A fast first response and a fast resolution are two different wins. Teams that only track one often misread how their support is performing.<\/p>\n\n\n\n

A lower TTR generally signals a more efficient support process. But the number only means something once you know what it’s being measured against. That’s why benchmarks and clear definitions matter more than the raw figure alone.<\/p>\n\n\n\n

Time to resolution vs. First response time vs. First contact resolution<\/strong><\/h2>\n\n\n\n

These three metrics get confused constantly. They all describe speed, but each one answers a different question. Mixing them up in a report can make a team look faster or slower than it actually is.<\/p>\n\n\n\n

\n \n \n \n \n \n \n \n
Metric<\/th>\n What it measures<\/th>\n When it counts as “done”<\/th>\n <\/tr>\n <\/thead>\n
Time to resolution (TTR)<\/td>\n Total time from ticket opened to ticket resolved<\/td>\n When the issue is fully fixed and closed<\/td>\n <\/tr>\n
First response time (FRT)<\/td>\n Time from ticket opened to first human reply<\/td>\n When the customer gets any reply, even if unresolved<\/td>\n <\/tr>\n
First contact resolution (FCR)<\/td>\n Whether the issue got solved in a single interaction<\/td>\n When no follow-up contact was needed<\/td>\n <\/tr>\n <\/tbody>\n <\/table>\n<\/div>\n\n\n\n

A team can post an excellent FRT and a poor TTR at the same time. Answering fast but bouncing a ticket between three departments still produces a long resolution time. The first response looked great on paper. The actual fix took days.<\/p>\n\n\n\n

How to calculate time to resolution<\/strong><\/h2>\n\n\n\n

The formula is simple. Add up the total resolution time across every resolved ticket in a period. Then divide by the number of tickets resolved in that same period.<\/p>\n\n\n

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Average TTR = Total resolution time for all resolved tickets \u00f7 Total number of resolved tickets<\/strong><\/p>\n<\/blockquote>\n\n\n

Here’s a worked example using a small support queue:<\/p>\n\n\n\n

    \n
  1. Ticket A took 2 hours to resolve.<\/li>\n\n\n\n
  2. Ticket B took 6 hours to resolve.<\/li>\n\n\n\n
  3. Ticket C took 1 hour to resolve.<\/li>\n\n\n\n
  4. Ticket D took 15 hours to resolve.<\/li>\n\n\n\n
  5. Ticket E took 4 hours to resolve.<\/li>\n<\/ol>\n\n\n\n

    Add those five times together: 2 + 6 + 1 + 15 + 4 = 28 hours. Divide by 5 resolved tickets. The average time to resolution is 5.6 hours.<\/p>\n\n\n\n

    Notice Ticket D. A single 15-hour outlier pulled the average up noticeably. This is why many teams report the median alongside the mean. The median for this set is 4 hours. That’s a more honest picture of what a typical customer actually experiences.<\/p>\n\n\n\n

    What counts as a good time to resolution?<\/strong><\/h2>\n\n\n\n

    A “good” TTR depends heavily on channel and ticket priority. A single target number across your whole support operation usually hides more than it reveals.<\/p>\n\n\n\n

    \n \n \n \n \n \n \n \n \n \n
    Channel or priority<\/th>\n Reasonable target<\/th>\n <\/tr>\n <\/thead>\n
    Live chat<\/td>\n Under 10 minutes<\/td>\n <\/tr>\n
    Standard email inquiry<\/td>\n Under 24 business hours<\/td>\n <\/tr>\n
    Phone support<\/td>\n Resolved on the call where possible<\/td>\n <\/tr>\n
    Critical (P1) technical issue<\/td>\n Under 4 hours<\/td>\n <\/tr>\n
    Standard (P2\/P3) technical issue<\/td>\n 24 to 72 hours<\/td>\n <\/tr>\n <\/tbody>\n <\/table>\n<\/div>\n\n\n\n

    Cross-industry data from Help Scout<\/a> puts general resolution benchmarks in a similarly wide range. A billing question and a multi-team technical escalation were never going to resolve at the same speed. Set separate targets by ticket type instead of one number for everything.<\/p>\n\n\n\n

    Why time to resolution matters<\/strong><\/h2>\n\n\n\n

    Speed shapes how customers judge your entire support experience, not just the individual ticket. According to HubSpot’s customer service research<\/a>, response time is one of the top three metrics customer service leaders track. It sits right alongside CSAT (customer satisfaction score) and retention, because the three tend to move together in practice.<\/p>\n\n\n\n

    Forrester’s customer satisfaction benchmark research<\/a> has consistently linked faster problem resolution to stronger loyalty and repeat business. That connection also shows up in Net Promoter Score<\/a> data. Customers who wait too long for a fix are far more likely to become detractors than promoters.<\/p>\n\n\n\n

    TTR has a quieter effect on revenue too. A customer who gets fast, complete resolutions tends to stick around longer. That feeds directly into customer lifetime value<\/a>. Slow resolutions do the opposite. They erode trust well before a customer ever files a formal complaint.<\/p>\n\n\n\n

    How to choose the right TTR target for your team<\/strong><\/h2>\n\n\n\n

    There’s no universal “good” number. Pick a target by weighing what actually shapes your customers’ patience and your team’s capacity.<\/p>\n\n\n\n