What is average wait time?
Average wait time is the mean time a customer spends waiting for service to begin, measured from the moment they enter a queue, are placed on hold, or submit a message, to the moment an agent, human or AI, starts working the contact. It is reported per channel and per interval, usually in seconds.
Because it is a mean, a few long waits move the figure more than most callers would recognize. A queue where most people wait forty seconds and a handful wait ten minutes can report an average above a minute, so teams pair it with a median or a percentile.
How average wait time is calculated
Three inputs decide the number: a start stamp, a stop stamp, and an inclusion rule. The start stamp lands when a caller leaves the IVR menu and enters the queue, or when a chat visitor sends a message; the stop stamp lands when a person or an AI agent takes the contact. Sum the gaps and divide by the contacts counted.
The inclusion rule is where two teams reading the same queue get different numbers. Take 200 answered calls that waited a combined 12,000 seconds: the average is 60 seconds. If the same interval also had 50 callers who each hung up after 180 seconds, adding their 9,000 seconds and their 50 contacts lifts it to 21,000 divided by 250, or 84 seconds. Counting abandons is what separates average wait time from average speed of answer, which counts answered calls only, so the two diverge on the same queue as call abandon rate climbs.
Upstream, queue length in any interval is set by agents staffed against contacts arrived, so forecast accuracy moves the average before anyone touches a script, and the figure is reported per fifteen or thirty minute interval for that reason.
Average wait time: what counts and what does not
Four inclusion decisions decide what the figure means, and every reporting tool defaults differently.
Queue time after the IVR: The seconds between a caller choosing a menu option and an agent connecting always count, because that is the wait the customer perceives as waiting.
IVR navigation itself: Time spent pressing buttons or talking to a voice menu is usually excluded, which flatters a queue with a long menu tree.
Abandoned contacts: Callers who hang up contribute the seconds they waited before leaving, and dropping them makes a bad interval look better than it was, so report both versions.
Mid-call holds: Being parked after the conversation has started belongs to handling time, and mixing it into wait time double-counts the same seconds in two metrics.
Average wait time vs average speed of answer vs first response time vs average handling time
These four sit next to each other on every dashboard, and the confusion is understandable because all of them are clocks. Average speed of answer counts the queue seconds of answered calls only. First response time counts the hours between a written request and the first substantive reply. Average handling time counts the talk, hold, and wrap-up seconds after the contact has connected. Average wait time is the broadest of the four: it counts every second a customer spent waiting for anyone to start, on any channel, whether or not they stayed.
What it counts | What it misses | Typical benchmark | |
|---|---|---|---|
Average wait time | Every second from queue entry or message sent until a person or AI starts, abandons included | Everything after connection, and how the wait felt | None published across industries; set per channel from your own abandon curve |
Average speed of answer | Queue seconds on answered voice calls | Callers who hung up; chat and email entirely | Paired with the queue’s service level target |
First response time | Elapsed time to the first substantive reply on tickets and email | Whether the reply solved anything; live-channel hold | Written into the SLA by channel and priority |
Average handling time | Talk, hold, and after-call work once a contact has connected | Everything before the connection | Compared with the team’s own history by contact type |
If your question is how long people sit before anyone helps them, across phone, chat, and callbacks, you need average wait time. Use speed of answer for a voice queue’s service level, first response time for tickets, and handling time for capacity planning.
Why average wait time matters for customer experience
A wait is the only part of a support contact the customer experiences alone, which is why it colors everything that follows. When a team does not measure it, the wait surfaces anyway: as a climbing abandon rate, as calls that open with a complaint about the hold, and as agents starting each call with an apology for a delay they did not cause. The same mechanism runs on written channels, where a long first response time is the wait a customer experiences as being ignored.
Wait time also compounds. A caller who gave up and dialed again adds a second contact to the same queue, so a bad hour lengthens the next one.
The tradeoff is explicit: every second cut from the average by staffing costs paid idle minutes in the quiet intervals, and a team that pays for zero wait at peak carries that cost all day.
How is average wait time measured?
No standards body publishes a target for average wait time, and the benchmark figures circulating in vendor decks describe whichever queues the vendor happened to measure. One international recommendation governs delay in a voice call: ITU-T G.114 addresses one-way transmission time once the parties are connected, a network quantity on a different clock from the queue.
What can be measured is the raw wait per contact, exported from the ACD, the chat platform, or the ticketing system as a timestamp pair: queue entry and connection. From those pairs a team derives the mean, the median, and the share of contacts answered inside its own target, and reports them by channel and interval. The mean tells you the cost of the queue, the median what a typical customer felt, and the gap between them how many people waited far longer than either.
How AI agents reduce average wait time
An AI agent removes the wait by removing the queue for the contacts it can take. Its capacity scales with compute, so arrivals stop stacking behind a fixed number of people, and the interval from queue entry to first action collapses from minutes to the model’s own response latency, measured in milliseconds. That reshapes the average in two steps. First, contacts the AI resolves outright leave the human queue, so fewer compete for the same staffed agents in the same interval. Second, escalated contacts arrive with intent classified and account identified, so each takes the human less time, which shortens the queue behind them.
The consequence for the metric is a split. On the AI-handled segment the average approaches zero and stops being interesting; on the escalated segment it becomes the number that matters, and it rises if the human team is cut to the forecast escalation volume and escalations then run above it. That split’s economics are worked through in this response time and cost comparison of automation platforms.
What to look for in average wait time reporting
Coverage comes first: phone queue, chat queue, callback delay, and abandoned contacts have to share one definition, or the channel with the friendliest exclusions will look best. Integration surface decides that: ACD, chat platform, and ticketing system each stamp queue entry differently, and one layer has to reconcile them. Governance means a named owner of the inclusion rules, since changing whether abandons count restates every historical number.
Two frameworks bind the data. GDPR reaches per-contact wait records because they sit joined to a phone number, forcing a retention limit on the raw rows. SOC 2 Type II is the evidence to request from a reporting vendor, because it covers who can read and export those rows.
The operational constraint is sample size. A fifteen minute interval on a small queue holds a handful of contacts, so one caller stranded through a shift change owns that interval’s average and any target set on it.
Average wait time and workforce management
Average wait time is the output of the workforce equation: contacts that arrived, divided across agents actually available in that interval. Two terms explain most of the gap between planned and actual availability. Call center shrinkage is the share of paid time that never reaches the queue, through breaks, training, and absence, so a plan that underestimates it staffs agents who are not there. Schedule adherence is whether the agents who are there are in the state the plan called for at that minute; ten scheduled and seven logged in at the morning peak is a common reason a queue’s average jumps with no change in volume.
What does average wait time mean in plain terms?
Think of average wait time as the length of the line at a counter, translated into seconds per person. It answers one question a manager can act on: on a normal day, how long someone stands there before anyone looks up.
Picture the alternative. Suppose a shop opened a second till only when the line was already out the door. Average wait time is the number that would have told the manager to open it twenty minutes earlier, because it shows the line building before anyone complained.
The tradeoff is the empty till. A counter with no line has someone standing behind it doing nothing during quiet stretches, and that idle time is what a short average costs. Every business picks a point between a line that irritates people and a staff that sits waiting, and the average is how it sees where it landed.
Common average wait time mistakes
Three patterns produce a figure that looks fine while customers are stuck.
Dropping abandoned contacts is the first. Each caller who gives up removes a long wait from the sum, so the worse the queue gets, the better the average of the survivors looks.
Moving the clock is the second. A menu layer, a callback offer, or a chatbot placed in front of the queue moves the moment the wait officially starts without moving the moment the customer started waiting, so a bot that stalls someone before the handoff adds a wait the report cannot see. Handoff design that avoids this is covered in human fallback for AI chat.
Treating a low average as a health signal is the third. A zero-wait queue can be well staffed, or it can be a queue nobody joins because the number fell off the website, and the average alone cannot tell which.
Frequently Asked Questions
What is a good average wait time for customer service?
A good average wait time is whatever your customers tolerate on that channel before abandoning, and no standards body publishes a universal target. Teams set one by watching where abandon rate turns up as wait lengthens, then holding the average and the longest-wait percentile below that point during their busiest intervals.
What is the difference between average wait time and average speed of answer?
Average wait time and average speed of answer measure the same clock with different inclusion rules. Speed of answer averages queue time across answered voice calls only, so callers who hung up vanish from it. Average wait time can include abandoned contacts and spans chat and callbacks too, which is why it usually reports a longer figure on the same queue.
Average wait time vs average handling time: which should you track?
Average wait time and average handling time describe consecutive stretches of one contact, so track both. Wait time covers everything before an agent starts and tells you whether staffing matched arrivals. Handling time covers talk, hold, and wrap-up after the start and tells you how long each contact occupies an agent. Cutting handling time frees capacity, which is one of the ways wait time comes down.
How do you calculate average wait time?
Average wait time is calculated by adding the seconds every contact spent between entering the queue and being taken by an agent, then dividing by the number of contacts included. If forty contacts waited a combined 2,400 seconds, the average is 60 seconds. The result changes with the inclusion rule, so state whether abandoned contacts and IVR time are inside the sum.
Does average wait time include abandoned calls?
Average wait time includes abandoned calls only when the reporting definition says so, and defaults differ between platforms. Including them adds each abandoner’s wait up to the moment they hung up, which raises the average in a bad interval. Excluding them leaves a figure built from the callers who stayed, so the queue looks better exactly when it is failing.
Can an AI agent bring average wait time to zero?
Average wait time falls close to zero on the conversations an AI agent handles itself, because it starts responding within its own processing latency and has no queue. The contacts it escalates still wait for a human, so the honest figure is two averages, one per segment, and the escalated one rises if the human team is staffed below the escalation volume.