Average call wait time looks simple until dashboards disagree about what starts the clock, which calls enter the denominator, and whether callbacks count. Those choices can make a queue appear healthier while more callers abandon. A useful measure needs a fixed definition, a companion service-level view, and quality guardrails. Here is how to calculate, diagnose, and reduce queue delay without confusing it with hold time or average handle time.
Average call wait time measures the delay before an answer
Average call wait time is the mean time callers spend in an agent queue before an agent answers. In contact-center reporting, this is usually the same metric as average speed of answer (ASA).
The clock starts when a call enters the queue and stops when it connects to an agent. It does not include time spent navigating an interactive voice response (IVR) menu before the queue, customer hold time after an agent answers, or after-call work. Your platform may use slightly different event boundaries. For example, Amazon Connect's metric definition includes agent and customer whisper time because the contact remains queued until the whisper ends.
Write the start event, stop event, included outcomes, and time zone into the metric definition before comparing queues or periods.
Average wait time formula
The standard ASA formula uses answered calls:
Average call wait time = total queue wait time for answered calls / number of answered calls
This definition excludes abandoned calls because they never reach the stop event, an agent answer. That is the conventional operational metric, but it omits callers who gave up. Because ASA does not include their wait, queueing research recommends reporting abandonment beside it.
Report a second customer-view measure when your data supports it:
Inclusive average queue wait = total queue time for answered and abandoned calls / answered and abandoned calls
Keep the two metrics separate and name them clearly. Also decide how to treat very short abandons, transfers, callbacks, and calls that leave the queue for self-service. A definition change can move the number even when the caller experience stays the same.
Worked example
Suppose five answered calls waited 12, 18, 26, 44, and 60 seconds.
- Total answered-call queue time: 160 seconds
- Answered calls: 5
- Average call wait time: 160 / 5 = 32 seconds
Now add two abandoned calls that waited 40 and 70 seconds. Standard ASA remains 32 seconds because those calls were not answered. The inclusive average is (160 + 40 + 70) / 7, or about 39 seconds.
Neither figure is wrong. They answer different questions. ASA describes the delay among callers who reached an agent. The inclusive figure describes queue time across callers who were answered or abandoned.
Keep queue wait separate from other call-center metrics
The word “hold” often causes reporting errors. Callers may say they were on hold while waiting for an answer, but average hold time is a post-answer metric in most contact-center systems. Amazon's metric definitions explicitly exclude queue time from customer hold time.
| Metric | What it measures | Clock boundary | Main use |
|---|---|---|---|
| Average call wait time / ASA | Mean pre-answer queue delay for answered calls | Queue entry to agent answer | Track access speed |
| Service level | Share of calls handled within a chosen queue-time threshold | Queue entry to answer, using a defined denominator | Control the distribution around a target |
| Abandonment rate | Share of queued callers who disconnect before an answer | Queue entry to caller disconnect | Detect callers lost before service |
| Average hold time | Time the caller is placed on hold after an agent answers | Hold start to hold end during the handled contact | Find post-answer delays |
| Average handle time (AHT) | Time an agent owns a handled interaction, including talk, hold, and wrap-up under the platform's definition | Agent connection through completion | Plan capacity and examine handling work |
The last two metrics belong to the handled interaction. They do not belong in the average call wait time numerator. Our average handle time guide covers AHT separately.
What is a good average call wait time?
A good average call wait time is the shortest delay you can sustain for a specific queue while meeting your service, quality, and cost goals. There is no universal number for every call type.
The familiar 80/20 service level, 80% of calls answered within 20 seconds, is a useful planning reference. Call-center research treats it as a chosen staffing scenario rather than a law; changing demand can cause a fixed team to miss the same target. More urgent calls may justify a far tighter goal. A specialist support queue may accept a longer wait to reach the right person.
Set the target in this order:
- Define the queue and caller need. Separate sales, billing, support, priority, and safety-critical traffic where their urgency or staffing pools differ.
- Choose a service-level threshold. Decide what percentage of eligible calls should be answered within how many seconds. Document whether short abandons and callbacks enter the denominator.
- Set an ASA range. Use ASA as the absolute-time companion to service level, not as the sole target.
- Add outcome guardrails. Track abandonment, transfer rate, repeat contact or first-contact resolution, and customer satisfaction. A faster answer that creates another call has shifted work rather than removed it.
- Model cost and capacity. Use forecast arrival volume, agent availability, and handling demand to find a sustainable staffing level. Revisit the policy when volume, mix, or caller expectations change.
A monthly average is too blunt for operating the queue. Track target attainment in the intervals where staffing decisions are made.
Diagnose queue delay before choosing a fix
Start with the smallest useful cut of the data. Twilio's dashboard guidance breaks waiting time out by queue, day, and hour, alongside queued, handled, and abandoned work. That structure exposes the cause more clearly than one blended number.
- Verify the metric contract. Confirm the enqueue and answer events, outcome filters, short-abandon rule, transfer behavior, callback treatment, and reporting time zone.
- Split the day into 15- or 30-minute intervals. Compare arrivals, answered calls, abandonments, available agents, and ASA. A daily average can hide a damaging morning spike inside a quiet afternoon.
- Split by queue and skill. One overloaded queue beside an underused team points to a routing or cross-skilling problem. A system-wide spike points more often to demand, availability, or an incident.
- Split by intent and entry path. Track what callers need and how they entered the queue. A release, billing event, broken self-service flow, or long IVR path may generate a concentrated surge.
- Inspect the tail. Add the median, 90th or 95th percentile, and longest wait. A low mean can coexist with a small group of very long waits.
- Read outcomes together. Compare ASA with service level, abandonment, transfers, callback completion, resolution, and repeat contact.
Use the pattern across metrics to choose the intervention:
| Pattern | Likely issue | First check |
|---|---|---|
| ASA rises when arrivals exceed forecast | Forecast or interval staffing mismatch | Arrival forecast, schedule fit, adherence, outages |
| One queue spikes while capacity exists elsewhere | Routing or skill constraint | Eligibility rules, priorities, cross-skilled agents |
| ASA improves while abandonment rises | Excluded callers are making the average look better | Abandon waits, short-abandon rule, IVR exits |
| Live-call ASA improves while callback delay grows | Callback work is being deferred | Callback priority, age, attempts, completion |
| ASA falls while repeat contact rises | Self-service or rushed handling is not resolving the need | Resolution by intent, transfer and repeat-contact rates |
How to reduce average call wait time without hiding worse outcomes
Match staffing to the arrival curve
Forecast volume and required capacity by interval, then place breaks, training, flexible coverage, and cross-skilled staff around the real peaks. Hiring may be necessary when sustained demand exceeds feasible capacity, but schedule fit is the first check.
Guardrail: Watch occupancy, schedule adherence, absence, service level, and quality. Persistently extreme occupancy leaves little recovery capacity for unexpected arrivals.
Route calls by intent, skill, and age
Capture only the context needed to place the call correctly. Route to eligible agents, then escalate priority as the wait ages. Modern routing systems can match task attributes to worker skills, capacity, and availability, as shown in Twilio's routing model.
Guardrail: Track misroutes, transfers, and the oldest wait by queue. Overly narrow skill rules can strand callers even while agents are available elsewhere.
Offer a queued callback
A callback lets the caller leave the line while retaining a virtual place in the workload. It improves the waiting experience even when it does not reduce the time until service. Decide whether virtual wait enters the same reporting series or a separate callback measure.
Amazon Connect's callback flow shows why implementation details matter: callbacks can keep the original queue position, use a dedicated queue, carry their own priority, and create duplicate requests if repeat callers are not handled.
Guardrail: Track time to callback, callback completion, attempts, duplicates, customer answer rate, and live-call service level. A callback promise that arrives late is still a queue failure.
Use self-service for bounded intents
Let callers complete narrow, low-risk tasks without entering the agent queue. Good candidates have clear inputs, deterministic tool actions, and an outcome the caller can confirm, such as checking an order state or rescheduling within defined rules.
Guardrail: Measure completion and repeat contact by intent. Keep an immediate human path when the system cannot understand, authenticate, or complete the request. An extra automation loop adds pre-queue delay even though ASA stays unchanged.
Use voice AI for resolution, intake, and structured escalation
A voice AI agent can answer immediately, resolve suitable intents, collect context, invoke approved tools, or send an unresolved call to the right human queue with a structured handoff. This can remove eligible demand from the queue and reduce time lost to misrouting.
Guardrail: Measure successful resolution by intent, repeat contact, transfer success, tool errors, caller opt-out, and the wait after escalation. “Contained” is not a quality result when the caller hangs up or calls back.
Average call wait time FAQ
Is average call wait time the same as average speed of answer?
Usually, yes. Both commonly mean the average pre-answer queue delay across answered calls. Some platforms use “wait time” more broadly, so compare the actual start event, stop event, and included outcomes before treating two reports as equivalent.
Should abandoned calls be included in average call wait time?
Do not add them to standard ASA without relabeling the metric. Keep ASA for answered calls and report an inclusive queue-wait measure across answered and abandoned calls beside it. This preserves comparability and shows the experience of callers who gave up.
Does IVR time count as average call wait time?
Usually, the ASA clock begins when the call enters the agent queue, after the IVR path. Track IVR or pre-queue time separately if it is material, and consider an end-to-agent measure for the caller's full access delay. Otherwise, a long menu can get worse while ASA appears unchanged.
Where Dasha fits
We build Dasha for technical teams creating production voice AI products. It fits a wait-time program when you need to build and operate a voice agent for bounded self-service, intake, or structured escalation, with telephony, integrations, testing, monitoring, and call execution in one managed foundation.
Dasha is not workforce-management software, and voice AI cannot repair a bad staffing forecast or an under-resourced specialist queue. The strongest use case has a clearly defined intent, an approved action path, an observable success condition, and a fast handoff when the agent cannot finish the job.
If your queue strategy includes that production voice AI layer, evaluate Dasha against the intents, guardrails, and escalation path you have defined.