Why Average Speed to Answer Can Hide a Bad Queue

A customer waits twenty seconds and gets an answer. Another waits four minutes and leaves. The dashboard can still report an average speed to answer of twenty seconds because the second customer may never enter that average.

That is the important answer. Average speed to answer is useful, but it is not a complete measure of waiting. Read it beside abandonment, service level, long waits, and the customers still waiting now. Otherwise a queue can look faster precisely when its most patient customers give up.

What average speed to answer actually measures

Average speed to answer, often shortened to ASA, is the average queue wait before a representative accepts an incoming conversation. Microsoft’s current queue guidance calculates the wait from queue entry to assignment and acceptance, divided by accepted conversations in the selected period.

That denominator matters. ASA answers a narrow question: how long did the customers who reached a representative wait on average? It does not tell you how long every arrival waited. It does not prove that the queue served most customers. It also says nothing about whether the issue was resolved after acceptance.

This is different from first response time, which may start when a message is created and end at a meaningful reply. It is also different from time to assignment, which can expose work that never found an owner. Write these boundaries in the metric definition before comparing two systems or teams.

Why a faster average can be bad news

Suppose ten accepted conversations contain five minutes of total queue wait. ASA is thirty seconds. Now two customers wait four minutes each and leave before acceptance. If your report keeps the accepted-conversation denominator, the ASA remains thirty seconds. The people with the worst waits are visible only in abandonment data.

The arithmetic is not wrong. The story is incomplete.

Microsoft defines abandon rate separately as customer disconnects before representative acceptance divided by incoming conversations that entered the queue. Its current definition excludes system disconnects and conversations closed by overflow. Those distinctions prevent a routing failure or technical ending from being mistaken for a customer choice.

A queue can therefore move in several directions. ASA may rise because accepted customers waited longer. It may fall because staffing improved. It may also fall because long-waiting customers left, overflowed elsewhere, or never reached the accepted set. The metric cannot tell those stories apart on its own.

Build one evidence set from the same events

Start with event-level records, not a screenshot of dashboard averages. Each incoming conversation needs a stable identifier and timestamps for queue entry, acceptance, customer abandonment, overflow, system closure, transfer, and final end where those events exist.

Microsoft’s segment metrics documentation describes queue segments with first-wait time, engagement state, abandonment state, creation reason, and closure reason. That model is useful even if your system uses different field names. Preserve the path a conversation took instead of flattening it into one final status.

Use the same filter for ASA and its companion measures. If ASA covers one support queue during business hours, abandonment and service level should use that queue, interval, channel, and time zone too. Do not compare an accepted-only voice measure with an all-day, all-channel abandonment rate.

Keep short abandonment visible as a named policy rather than silently deleting it. A quick exit may be a wrong number, a customer who found the answer, or a sign that the entry experience was confusing. The threshold is a reporting decision, not a fact about intent.

Read the queue in small enough slices

A daily ASA can blend a calm morning with a failed lunch hour. Break the evidence into intervals that match staffing decisions. Then segment by queue, channel, skill, entry route, and transfer path. Use enough volume to avoid reacting to a handful of conversations, but do not average away the period people actually experienced.

Look for combinations, not a single red line. Rising ASA with rising abandonment usually points toward a capacity or routing constraint. Falling ASA with rising abandonment deserves immediate checking because the accepted group may be shrinking toward easy cases. Stable ASA with a growing oldest wait may mean a minority is stuck while fresh work is answered quickly.

Volume context matters too. A demand spike is a different problem from a permanent schedule mismatch. Connect the same intervals to your support-volume forecast before adding people or extending hours.

Turn the pattern into one operational decision

Do not ask a team to “improve ASA” without naming the customer outcome. The target invites rushed acceptance and can move waiting time into the conversation after an agent clicks accept.

Choose the action from the pattern. If one interval fails across every skill, change staffing or hours. If one skill fails while capacity is available elsewhere, repair routing or cross-training. If abandonment rises before the published wait, set clearer expectations or offer a real callback. If transfers restart the clock, preserve the original queue journey and fix ownership.

A shared support operation should let the supervisor trace those paths without losing the customer behind the average. Review the change on the same event definition and keep a guardrail for resolution quality. Faster acceptance is valuable only when more customers reach useful service.

Frequently Asked Questions

What is the formula for average speed to answer?

Add the queue wait for accepted incoming conversations and divide by the number accepted. Document exactly when queue entry and acceptance occur in your system.

Does average speed to answer include abandoned conversations?

Common accepted-conversation definitions do not include customers who leave before acceptance. Check your platform’s field logic and report abandonment separately.

What should I measure beside average speed to answer?

Use abandon rate, service level, a wait-time distribution, the longest live wait, and volume by interval. Add resolution quality so fast acceptance does not become empty handling.

How often should a support team review it?

Supervisors can watch live intervals for intervention, while planning teams should review stable weekly patterns. Keep the definition and filters unchanged when comparing periods.