Average Speed of Answer (ASA): How to Track and Improve It

If you’re like me, there have been times when you deliberately missed a call (telemarketers, usually). For businesses, however, a missed call can mean a lost opportunity or a disgruntled customer.

ASA (average speed of answer) is one of the key call center metrics to look at if you're a contact center supervisor or customer support lead. Tracking and improving average speed of answer is one key way to improve customer satisfaction in your contact center.

What is the average speed of answer (ASA)?

Average speed of answer or (ASA) is the average amount of time it takes an agent to answer an inbound call after the caller enters the queue.

ASA excludes time spent before a caller reaches that queue, such as:

• Time spent routing the caller to the right queue

• Time the caller spent interacting with an IVR

• Time spent with an AI receptionist before any handoff to a queue

ASA does include the time an agent's phone is ringing, and it also factors into average handle time.

Why it matters to call centers

If you lead customer support in a contact center, you need to track and monitor multiple key performance indicators (as well as looking to improve those figures). One metric to focus on is ASA.

Even though ASA isn’t the same thing as other KPIs like average handling time (AHT) and CSAT, they are related and it’s important to look at these together to build a holistic view of how you're serving customers.

For example, I'd want my agents to find the fastest path possible to resolving issues and answering questions. I'd also want any customer who calls us to go away satisfied—both with any answers and with their overall customer experience.

A low ASA is good because it generally means your agents are answering calls as quickly as possible, dealing with issues efficiently, and not leaving a customer waiting. Your contact center analytics will help you determine what is working well and where you can make improvement.

ASA also reflects how well your IVR, ACD, or AI receptionist flows are working. Insights from both how quickly calls are answered and how fast they are handled can help you to continuously optimize those flows as well as routing types like skill-based routing, where you route the caller to the agent specialized in their particular needs.

For example, if you see that there’s an IVR option that no one has used in months, then that’s a sign that you can probably remove it and streamline the options for your callers so they don’t have to sit there listening to so many options. Or, if a particular path your AI receptionist is sending callers down is ending up with them routing to multiple agents before resolution, you might need to update your AI knowledge base that the receptionist draws from.

Why improving ASA matters for your business

I've talked about what ASA is and why it's worth tracking, but let's back up for a second. Why does it actually matter if we shave 10 or 15 seconds off our average speed of answer?

• Fewer abandoned calls: The longer someone sits in a queue, the more likely they are to hang up before an agent ever picks up. Every one of those hang-ups can be a missed sale, a support issue that goes unresolved, or a customer who tries a competitor instead.

• Customer satisfaction and retention: Nobody likes waiting on hold. A faster ASA generally means callers feel heard sooner, which can carry through to how they rate the whole interaction, not just the wait.

• A lighter load on agents: When ASA creeps up, it's usually a sign that queues are backing up, which can mean agents are handling calls back-to-back with little breathing room. Getting ASA under control can take some of that pressure off the team.

• Your brand's first impression: The wait itself is often the first thing a caller experiences with your company on any given call. A long one can color how they feel about everything that comes after, even if the agent who eventually picks up does a great job.

None of this means chasing the lowest possible number for its own sake (more on that below), but it's worth understanding that ASA isn't just an internal metric on a dashboard. It's tied pretty directly to how customers may feel about calling you in the first place.

Understanding the average speed of answer formula

The formula for ASA is to take the total waiting time for calls that were answered and divide it by the total number of answered calls.

For example:

Let’s say last week, my team answered 5,000 calls. The total time in a queue for those calls was 200,000 seconds. So for that week, my team’s ASA was 40 seconds.

(That doesn’t mean every call got answered in 40 seconds. This is just the average amount of time and there will always be outliers! You likely want to look at the median time and the extremely short or long times for a full picture.)

Is it better to have an extremely short ASA?

To know if your ASA is good is a bit tricky because it depends on a number of factors. If you have an extremely low ASA, that might seem like a good thing at first—but it could also be a sign that you’re overstaffed. (You may think that increasing staff to reduce ASA would be a good thing—but how much more are you spending per ticket? What’s an acceptable amount?)

If you have an overly high ASA and call center agents are taking too long to answer, then there’s a good chance that a customer will get annoyed, hang up, and add to our abandonment rates. That’s not good for agents or customers. Ideally, you want a balanced approach where calls get answered relatively quickly, your human agents don't get burned out by long queues, and you can staff both human and AI agents in an optimal way for your business.

How AI receptionists and AI customer service agents change the ASA equation

Many customer service teams are adopting some form of agentic AI to help serve their customers. That might be an AI receptionist to answer calls using natural language, provide information, and route to the right human agent. It also might be an AI customer service agent that can resolve many types of customer calls autonomously, and hand off to a human only when needed. In any case, this changes the way I think about ASA.

Here's the thing: a lot of the calls that land in a queue are pretty routine. Someone wants to confirm an appointment, check an order status, or get a quick answer to a question we've answered a thousand times before. Those calls don't necessarily need a human on the other end; they need a fast, accurate answer.

That's where AI agents come in. A lot of that routine volume can be resolved before it ever sits in a queue waiting for a person. The caller gets an answer right away, and my team's queue only has the calls that genuinely need a human's judgment or a bit more back-and-forth.

Here are some practical examples of how this might look:

Routine requests get handled without a wait: Things like business hours, appointment confirmations, and order status lookups can be answered by an AI agent the moment the call comes in, so ASA for that portion of your call volume basically disappears.

Handoffs to a human keep the context: When an AI agent does need to bring in a person, it's a two-stage handoff (AI agent to human agent), and it can pass along the reason for the call, what's already been tried, and even a read on the caller's sentiment. My human agents aren't starting from zero, which can help them resolve the call faster once it does reach them.

Coverage doesn't stop at 5 p.m.: A lot of ASA problems show up after hours or during unexpected spikes, when staffing can't flex up fast enough. An AI agent can pick up around the clock, so those gaps don't turn into long queues (or voicemails) the next time a human agent logs on.

Taken together, this is really a shift in what ASA is measuring in the first place. When AI receptionists can answer 24/7 and AI agents can resolve routine calls on their own, ASA on the human side reflects a smaller, more complex slice of your total call volume, and the calls that do reach a queue tend to be ones that genuinely benefit from a person's judgment. A low ASA still matters, but it's increasingly a measure of how well your team handles what's left after AI has already taken care of the easy wins.

How to optimize your call center’s ASA

Let’s imagine for a minute that you’re looking at your call center’s analytics. Most of the figures are looking good—but you have noticed that your ASA is a lot higher than you would like.

How can you address that issue and optimize it? Here are a few recommendations.

1. Use an in-queue call back

We don’t like having our customers facing long waits in queues. But there are times when our team is overwhelmed by the volume of calls.

That’s where the in-queue call back option in Dialpad is a good fit. This way, after a set amount of time, the caller can opt to receive an outbound call back from the agent when their ticket reaches the front of the queue. They can be called back on the number they called from or can input an alternative number.

2. Investigate your call forecasting

Forecasting your contact center's call volume is much easier today than it was even a few years ago. Many contact center solutions (including Dialpad's) provide heatmaps that shows the busiest times of day or week, and workforce management (WFM) tools are often integrated with contact center solutions now as well.

Knowing your daily, weekly, and monthly call patterns, as well as seasonal spikes throughout the year, means you can plan staffing levels to fit. For example, if you notice that you receive twice the number of calls on weekends than weekdays, it makes sense to raise staffing levels to meet demand.

When planning, make a habit of comparing your historical call forecast with the actual figures to see if they’re accurate. You have to do this regularly to make sure your forecasts stay accurate. If not, and if there is a pattern of deviation, then adjust your future forecasts to fit with any differences.

3. Take advantage of your call routing system

While it may sound basic, I find call routing to be one of the most useful tools available to contact centers. Sometimes the fundamentals make some of the biggest impact (in CX and otherwise).

Ideally, you’ll have a variety of routing options. How you use call routing will depend on a number of factors, from operating hours to call volumes. If you can combine it with an effective IVR menu, it’s a great one-two punch for keeping customers happy. (Of course today, AI tools like AI receptionists can do this using natural language, but IVRs are still the right fit for many businesses.) In either case, it’s helpful to analyze your historical data to inform routing, IVR, or AI receptionist setup. While you can never predict 100% what your needs will be, there will be patterns that can help you determine the best way to get your customers the answers they are calling for.

4. Train (and retrain) agents as needed

One thing I always tell new call center agents is that once their initial training is finished, their real training begins.

We operate in a sector that constantly sees new tech arriving and old tech evolving. But we also learn from everything we do each day—and from things that happen within the call center industry as a whole.

We’re always updating our training materials and again, here’s where Dialpad really helps our team. You can use Dialpad’s AI Live Coach Cards to automate this part of training.

For example, if you find that certain topics are tripping up my agents, you can create a card to pop up automatically on their screens with tips on how to answer those questions—when certain keywords are spoken on a call. This way, you can make sure your team is still learning and being coached on new info—but do it at scale.

Additional Dialpad features that help with ongoing training include AI Playbooks (especially helpful for outbound calling and sales) and AI Scorecards.

5. Give agents access to real-time analytics

It’s often said that you are your own biggest critic. And with some agents, this is very true! Often, it’s the agent who ends up identifying any issues with their performance or figures.

By allowing your agents to see analytics in real time (as you can with Dialpad), you’re empowering them to see what’s working and what may need a little change in their approach. Don’t overlook the accessibility of your data. It’s often helpful for not just you, but your agents too.

6. Let an AI agent handle overflow and after-hours calls

Not every ASA problem is something you can staff your way out of, especially outside business hours or during a sudden spike in call volume. This is where an AI agent can take some pressure off. Instead of routine calls piling up in a queue (or going to voicemail) when your team is at capacity, an AI agent can pick up, handle what it can on its own, and hand off anything more complex to a human agent with the context already in hand.

When you're first setting up AI agents, it's helpful to start with the call types that are the most repetitive: hours, location, order status, appointment confirmations. Those are usually the easiest wins and the ones most likely to be sitting in your queue driving ASA up.

Reduce your call center’s ASA with the best strategies and tools

For some folks who first start working in a contact center, it's tempting to think that all the job entails is answering phones and replying to queries.

Today, we have multiple workforce management strategies and ways to optimize every area of call center performance, from CRM software and automation to AI agents that can resolve calls before they ever reach your team. ASA is just one metric, but it's a good gut check for how well all of those pieces are working together. When it's trending in the right direction, it usually means your queues, your routing, and your team all have what they need to answer callers quickly and well.