Average Handle Time (AHT)

Definition

Average Handle Time (AHT) is a contact center and service metric that measures the average duration of a customer interaction from the moment an agent accepts it until all related work is complete. It typically includes talk or chat time, hold time, and after-call work (ACW) such as documentation or follow-up tasks.

From a marketing and customer experience perspective, AHT is a core operational metric that reflects how efficiently a brand handles customer inquiries and issues. While marketers tend to obsess over journeys, funnels, and segments, customers care very much about “how long this will take.” AHT connects experience quality, operational cost, and customer effort across service channels that influence loyalty, churn, and word-of-mouth.

AHT is used across voice, chat, email, social care, and in-app support. While the exact thresholds vary by industry and interaction type (complex B2B troubleshooting will take longer than a simple password reset), the concept is consistent: the average time required to fully handle an interaction.

How to calculate Average Handle Time (AHT)

The standard formula for AHT is:

AHT = (Total Talk Time + Total Hold Time + Total After-Call Work Time) ÷ Number of Handled Interactions

Where:

• Total Talk Time = Sum of all agent–customer live interaction time (phone, chat, video).
• Total Hold Time = Sum of all time customers spend on hold during those interactions.
• Total After-Call Work (ACW) = Sum of all time agents spend finishing tasks directly related to those interactions (notes, CRM updates, follow-up emails, workflow updates).
• Number of Handled Interactions = Count of completed interactions within the measurement period (calls, chats, tickets, etc.).

For asynchronous channels such as email or tickets, organizations often adapt the formula to focus on active handling time, not elapsed clock time. In that case, AHT may rely on timestamps and agent “work” durations rather than simple start/end times. The principle remains the same: average active time to handle.

How to utilize Average Handle Time (AHT)

AHT is primarily used to:

• Plan and staff contact centers
  Workforce management teams use AHT along with volume forecasts and desired service levels to determine staffing requirements by channel and time window. Lower AHT (without harming quality) means fewer hours required to handle the same volume. Higher AHT requires more staffing or a different support strategy.
• Identify process and experience friction
  Sudden increases in AHT can indicate process issues, product defects, confusing marketing claims, or broken self-service flows. For example, if AHT spikes after a new offer launches, marketing and service teams can review interaction reasons, IVR paths, and content to see where expectations are misaligned with reality.
• Align CX, support, and marketing messaging
  If a campaign promises “frictionless onboarding” but AHT for onboarding calls is high, that gap becomes a cross-functional improvement target. Marketers can adjust messaging, create clearer help content, or improve proactive communications to reduce the need for long support interactions.
• Optimize channel mix and self-service
  By comparing AHT across channels and issue types, teams can decide which intents should be automated, deflected to digital self-service, or prioritized for assisted support. Simple, repetitive inquiries with low AHT are good candidates for automation; complex, high-AHT issues may require more specialized routing and agent training.
• Support quality monitoring and coaching
  AHT at the agent or team level highlights where additional training, better tools, or improved knowledge content may be needed. Used carefully (and not as a blunt instrument), AHT helps leaders detect outliers and pair them with qualitative quality monitoring.

Comparison to similar metrics

Metric	Primary focus	Basic calculation (concept)	Main use in marketing/CX	Relationship to AHT
Average Handle Time (AHT)	Total handling duration per interaction	(Talk + Hold + ACW) ÷ Handled Interactions	Operational efficiency, experience friction, staffing, channel design	Core duration metric; influences cost and perceived effort
Average Speed of Answer (ASA)	Time to connect to an agent	Total Wait Time ÷ Number of Answered Contacts	Measures how long customers wait before reaching an agent	ASA happens before AHT starts; together they describe total time
Time to Resolution (TTR)	Time to fully resolve the issue	Total Time from First Contact to Resolution ÷ Resolved Issues	Overall experience and operational effectiveness across touchpoints	AHT affects each interaction within TTR, especially for complex cases
First Contact Resolution (FCR)	Resolution in a single contact	Resolved on First Contact ÷ Total Contacts (for that issue type)	Customer effort, satisfaction, loyalty	Lower AHT is useful, but high FCR with slightly higher AHT often wins
Customer Satisfaction (CSAT)	Customer-reported satisfaction	% of positive responses on post-interaction surveys	Measures perceived experience quality	AHT can correlate with CSAT but does not guarantee it
Net Promoter Score (NPS)	Brand-level advocacy	% Promoters − % Detractors based on “likelihood to recommend”	Measures relationship strength and loyalty	Repeated high AHT on key journeys can erode NPS over time
Customer Effort Score (CES)	Perceived effort to resolve an issue	Average score on “effort” question	Tracks how hard customers feel they had to work	High AHT usually increases effort, but process design also matters
Call Abandonment Rate	% of contacts ended before connection	Abandoned Contacts ÷ Total Incoming Contacts	Indicates frustration, capacity issues	High AHT and high ASA together can drive higher abandonment

In practice, AHT should be read alongside these metrics. A short AHT with low FCR and poor CSAT is “fast but ineffective.” A right-sized AHT with high FCR and strong CSAT is a better outcome, even if it means a slightly longer interaction.

Best practices

• Balance speed and quality
  Treat AHT as a guide, not a hard quota. Overemphasis on “shorter is always better” can push agents to rush customers, transfer unnecessarily, or skip important steps, all of which hurt satisfaction and increase repeat contacts.
• Segment AHT by intent, channel, and segment
  Report AHT separately for different interaction types (billing vs. technical issues), channels (phone vs. chat), and customer segments (e.g., high-value accounts). A single blended AHT hides important differences and leads to poor targets.
• Tie AHT to journey stages and campaigns
  Map AHT to journey stages (onboarding, renewal, complaint handling) and to specific campaigns or product launches. This helps marketing teams see downstream impacts of messaging and promotions and adjust upstream content to prevent longer interactions.
• Leverage knowledge management and content quality
  Well-structured knowledge bases, internal playbooks, and customer-facing help content reduce AHT by giving both agents and customers faster paths to correct answers. For marketers, ensuring product, promotion, and policy content is precise and consistent across channels can prevent lengthy clarification calls.
• Use technology to reduce manual steps
  Integrate CRM, ticketing, marketing automation, and telephony/chat platforms so agents do not waste time switching systems. Features like screen pops with campaign information, next-best-action suggestions, and auto-populated notes reduce ACW and bring AHT down without sacrificing quality.
• Monitor outliers and trends, not single points
  Track AHT over time and across cohorts. Focus on trends, shifts after process changes, and significant deviations between teams or regions rather than individual day-to-day variances. Use qualitative reviews to understand why AHT is high or low for a given group.
• Align AHT targets with business goals
  AHT expectations should vary by interaction complexity, regulatory environment, and relationship value. High-value B2B customers may warrant longer, consultative calls. Short AHT is not inherently a win if it damages revenue, retention, or trust.

Future trends

• Outcome-aware AHT
  Organizations are increasingly tying AHT to outcomes such as FCR, CSAT, conversion, retention, or upsell. Metrics will shift from “shorter is better” to “optimized for outcome,” where slightly longer AHT is encouraged for high-value interactions that drive renewal or expansion.
• AI-assisted interactions
  Generative AI and agent assist tools will continue to handle data entry, summarization, and knowledge lookup, reducing ACW and some talk time. AHT will likely decrease for simple, repeatable intents that can be automated, while complex, high-empathy cases remain with humans and may keep higher AHT.
• Conversation analytics and intent-level AHT
  Speech and text analytics will refine AHT analysis at the intent and script level. Teams will track “AHT by reason code,” “AHT by next best action,” and “AHT by campaign,” making the metric far more actionable for marketing, product, and CX teams.
• Omnichannel normalization
  As customers move seamlessly across channels, AHT will be tracked consistently across voice, chat, in-app messaging, and social care. The focus will be less on “call AHT” and more on “interaction AHT per intent,” aligned with a unified customer profile and journey analytics.
• Proactive and predictive service
  With better data and predictive models, some issues will be detected and resolved before the customer reaches out. In those scenarios, AHT shifts from reactive measurement to supporting proactive outreach efficiency (how long it takes to handle early-warning alerts or triggered outbound contacts).
• Integration with marketing attribution and LTV
  As data unifies across marketing, sales, and service, AHT will be examined in relation to customer lifetime value (LTV), engagement, and campaign performance. High AHT for certain issues may be treated as a marketing cost driver and factored into offer design, channel mix, and pricing strategy.

Related Terms

1. First Contact Resolution (FCR)
2. Time to Resolution (TTR)
3. Average Speed of Answer (ASA)
4. Service Level (SL)
5. Call Abandonment Rate
6. Customer Satisfaction (CSAT)
7. Net Promoter Score (NPS)
8. Customer Effort Score (CES)
9. Workforce Management (WFM)
10. Contact Center Utilization Rate