Overcoming Challenges to Meet Average Speed of Answer (ASA) Targets in Customer Support

Average Speed of Answer (ASA) is one of the most important customer support metrics for measuring how quickly customers can connect with a support agent. When Average Speed of Answer (ASA) increases beyond the target, customers may experience longer wait times, higher abandonment rates, and frustration. For customer support leaders, however, simply telling agents to “answer faster” is not the solution. A high Average Speed of Answer (ASA) is usually the result of an underlying operational issue involving forecasting, staffing, scheduling, Average Handle Time (AHT), absenteeism, or schedule adherence. Let’s look at how support leaders can identify the root cause of a poor Average Speed of Answer (ASA) and take corrective action.

What Is Average Speed of Answer (ASA)?

Average Speed of Answer (ASA) measures the average amount of time customers wait before their chat or phone interaction is answered by a support agent. For example, suppose your customer support center has an Average Speed of Answer (ASA) target of 30 seconds. During one hour, five customers wait:

• Customer 1: 10 seconds
• Customer 2: 20 seconds
• Customer 3: 30 seconds
• Customer 4: 40 seconds
• Customer 5: 50 seconds

The average waiting time is 30 seconds, so the Average Speed of Answer (ASA) for that sample is 30 seconds. The objective is not simply to achieve a low Average Speed of Answer (ASA) number. The objective is to balance speed with quality. If agents rush customers through conversations just to improve Average Speed of Answer (ASA), customer satisfaction and first-contact resolution can suffer. Therefore, ASA should always be evaluated alongside other customer support metrics such as AHT, abandonment rate, customer satisfaction (CSAT), and first-contact resolution.

1. Compare Forecasted Volume With Actual Offered Volume

One of the first things a support leader should investigate when Average Speed of Answer (ASA) is above target is whether actual customer demand matched the forecast. For example, imagine your workforce management team forecasts: Forecasted volume: 10,000 chats and calls per month But the actual offered volume is: Actual offered volume: 15,000 chats and calls per month That means the support center received approximately 150% of the forecasted volume. In this situation, it would be unrealistic to expect the existing staffing level to maintain the same Average Speed of Answer (ASA) without making operational adjustments.

What should you ask?

Start with two questions:

1. Did agents maintain schedule adherence during the higher volume?
2. If schedule adherence was strong, did we have enough staffing capacity?

If agents were available according to schedule but demand increased significantly, the problem may not be agent performance. It may be a capacity planning problem. For example, if five agents were scheduled to handle 100 interactions per hour but actual demand increased to 150 interactions per hour, customers will naturally experience longer queues and a higher Average Speed of Answer (ASA).

How to address it

Consider:

• Improving volume forecasting.
• Adding additional staffing during peak periods.
• Using flexible or part-time resources.
• Cross-training agents.
• Creating an overflow team.
• Adjusting schedules based on historical demand patterns.
• Using workforce management tools to identify peak periods.

Reducing AHT can also create additional capacity, but leaders should be careful not to reduce AHT aggressively simply to improve Average Speed of Answer (ASA). An agent who rushes a customer may reduce AHT by one minute but create another contact later because the issue was not properly resolved.

2. What If Actual Volume Is Lower Than Forecast?

Now consider the opposite situation. Your team forecasted: 10,000 interactions But actual volume was: 9,000 interactions Yet the Average Speed of Answer (ASA) target was still missed. This is an important signal. If demand was lower than expected but ASA remained high, the problem may be related to workforce utilization, scheduling, or productivity rather than customer demand.

Review the staffing schedule

Look at the support center hour by hour. You might discover that:

• 10 agents were scheduled between 8 AM and 10 AM.
• Only 4 agents were scheduled between 10 AM and 12 PM.
• Customer demand peaked between 10 AM and 12 PM.

Even though the total monthly staffing level may have been sufficient, the staffing was not aligned with customer demand. This is known as a scheduling efficiency problem. A support center can have enough employees overall and still have a poor Average Speed of Answer (ASA) because the right number of people are not available at the right time.

3. Investigate High Average Handle Time (AHT)

If forecasting and scheduling are working correctly, the next area to investigate is Average Handle Time. AHT includes the time agents spend handling customer interactions and, depending on the support platform, may include related hold and after-contact work. For example: Suppose your team has an AHT of 8 minutes and receives 100 interactions per hour. That creates approximately: 800 minutes of workload per hour. If AHT increases to 12 minutes, the same 100 interactions create: 1,200 minutes of workload per hour. That is a 50% increase in workload without any increase in customer volume. As workload increases, the queue can grow and Average Speed of Answer (ASA) can deteriorate.

What can cause high AHT?

Common causes include:

• New agents.
• Complex customer issues.
• Product defects.
• Poor knowledge documentation.
• Slow internal systems.
• Multiple escalations.
• Marketing campaigns.
• New product launches.
• Inadequate agent training.

For example, imagine a SaaS company launches a new feature. Customers begin contacting support because they do not understand how to use it. If agents do not have a knowledge article or troubleshooting guide, each interaction may take significantly longer. The result can be a higher AHT and, consequently, a higher Average Speed of Answer (ASA).

How to address high AHT

Instead of simply telling agents to work faster, identify why interactions are taking longer. Consider:

• Improving the knowledge base.
• Creating troubleshooting guides.
• Introducing response templates.
• Improving product training.
• Creating escalation procedures.
• Identifying repetitive customer issues.
• Working with the product team to eliminate recurring defects.

The objective should be to remove unnecessary work, not simply make agents work faster.

4. Marketing Campaigns Can Affect ASA

Marketing campaigns can create unexpected increases in Average Speed of Answer (ASA). For example, suppose your company launches a promotional campaign offering discounted event tickets. The campaign generates 30% more customer interactions than normal. If the support team was staffed based on normal demand, the sudden increase can create longer queues. Support leaders should therefore work closely with marketing teams. Before major campaigns, ask:

• How many customers are we expecting?
• What types of questions might customers ask?
• When will the campaign generate the highest volume?
• Do we need additional staffing?
• Do agents need specific training?
• Should we create FAQs or self-service content?

Planning ahead can prevent a marketing success from becoming a customer support problem.

5. New Agents Can Increase ASA

New agents are another common factor affecting Average Speed of Answer (ASA). A new agent may take longer to navigate systems, find information, understand processes, or resolve complex issues. For example, an experienced agent may resolve an interaction in six minutes, while a new agent may require ten minutes. This does not mean the new agent is performing poorly. It means the organization needs to account for the learning curve. Support leaders can help by providing:

• Structured onboarding.
• Mentoring.
• Side-by-side coaching.
• Knowledge base training.
• Call and chat monitoring.
• Regular quality reviews.
• Access to experienced agents for escalation.

Over time, better training should improve both productivity and Average Speed of Answer (ASA).

6. High Absenteeism Can Quickly Increase ASA

Even with an accurate forecast and well-designed schedule, absenteeism can negatively affect Average Speed of Answer (ASA). Imagine 10 agents are scheduled to work during a peak period, but three agents call in sick. The team has suddenly lost 30% of its planned capacity. The remaining agents may face longer queues, increased workload, and higher customer wait times.

How can leaders respond?

Consider creating a staffing buffer through:

• Cross-trained employees.
• Flexible schedules.
• Overtime availability.
• Backup resources.
• Part-time employees.
• Voluntary shift extensions during peak periods.

However, consistently relying on overtime is not a sustainable long-term strategy. If absenteeism remains high, leadership should investigate the underlying causes rather than simply adding more overtime.

7. Schedule Adherence Directly Impacts ASA

Schedule adherence is another critical factor affecting Average Speed of Answer (ASA). Suppose your workforce management plan requires 10 agents to be available from 2 PM to 3 PM because customer demand is expected to peak during that period. If three agents are late returning from breaks, attending meetings, or handling non-queue activities, only seven agents may actually be available. The schedule says you have 10 agents. The operation effectively has seven. This gap can quickly increase Average Speed of Answer (ASA). Support leaders should monitor:

• Late logins.
• Extended breaks.
• Early logouts.
• Unplanned offline time.
• Meeting schedules.
• Training schedules.
• Non-queue activities.

Real-time monitoring can help managers identify problems before they significantly affect the customer queue.

A Practical ASA Root-Cause Framework

When your Average Speed of Answer (ASA) target is missed, don’t immediately assume the agents are the problem. Use this sequence: Step 1: Check actual volume vs. forecast Was customer demand higher than expected? ↓ Step 2: Check staffing Did you have enough people to handle the actual workload? ↓ Step 3: Check schedule efficiency Were enough agents scheduled during peak demand? ↓ Step 4: Check schedule adherence Were scheduled agents actually available? ↓ Step 5: Check AHT Were interactions taking longer than expected? ↓ Step 6: Identify the reason for high AHT Was it training, product complexity, system issues, or process inefficiency? ↓ Step 7: Take corrective action Address the root cause rather than simply pushing agents to answer faster.

Protect Customer Experience While Improving ASA

The ultimate purpose of improving Average Speed of Answer (ASA) is not simply to achieve a KPI. It is to create a better customer experience. Support leaders should avoid creating a culture where agents feel pressured to sacrifice quality to meet an ASA target. For example, reducing an interaction from 10 minutes to 6 minutes may initially look positive. But if the customer needs to call again because the issue was not resolved, the organization has not actually improved the customer experience. A balanced support center should therefore monitor ASA, AHT, CSAT, abandonment rate, first-contact resolution, and quality scores together. The goal is to achieve the right balance between speed, quality, efficiency, and customer satisfaction.

Final Thoughts

Improving Average Speed of Answer (ASA) requires more than asking agents to answer calls and chats faster. When ASA targets are missed, customer support leaders should investigate the complete operation—from forecasting and staffing to scheduling, adherence, AHT, absenteeism, training, and product issues. A strong support leader does not ask only: “Why didn’t we meet our ASA target?” The better question is: “What operational condition caused us to miss the ASA target, and what can we change to prevent it from happening again?” By taking a data-driven approach to Average Speed of Answer (ASA), support leaders can improve operational efficiency while protecting the customer experience. Ultimately, the objective is not simply to make the queue move faster. It is to build a support operation that is predictable, scalable, efficient, and capable of delivering an excellent customer experience.

Take the Next Step in Your Support Leadership Journey

Improving Average Speed of Answer (ASA) is not simply about asking agents to answer faster. It requires support leaders who can understand the operational factors behind performance—forecasting, staffing, scheduling, AHT, absenteeism, adherence, training, and customer experience. Strong support leadership means looking beyond the KPI and asking the right questions: What is causing the performance gap? What can we change? And how do we improve efficiency without compromising quality? At The Customer Support School (TCSS), our certification program helps current and aspiring support leaders develop the practical skills needed to manage people, performance, and customer experience effectively.

Customer Support Team Leader Certification – TCSS

Learn how to:

• Manage and interpret support KPIs such as ASA, AHT, CSAT, and SLA
• Identify root causes behind performance gaps
• Coach agents without sacrificing quality for speed
• Improve team productivity, accountability, and schedule adherence
• Build effective support processes and team routines
• Lead with data-driven decision-making and a customer-first mindset

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