Jan 8, 2026
7 min read
Average Handle Time (AHT) Explained: Meaning, Formula, and How to Improve It in Call Centers
What does AHT mean in a call center? The AHT abbreviation stands for Average Handle Time: the average amount of time your team spends to complete a call from start to finish. It’s one of the most common call center operations metrics, but it’s only useful when you treat it like a diagnostic ratio—not a scorecard.
In insurance sales (such as Medicare, ACA/U65, and Life Insurance), AHT is especially easy to misread because one blended number often mixes revenue-producing sales conversations with non-sales work (wrong-fit leads, service calls, consent-only calls, etc.). This guide shows how to split AHT into the buckets that actually tell you what to fix.
What does AHT mean in a call center? AHT meaning + basic formula
The AHT meaning call center teams use is straightforward: it’s the total time agents spend handling calls divided by the number of calls they handled.
Common formula:
AHT = (Talk Time + Hold Time) ÷ (Total Calls Handled)
The most important thing for real-world comparisons:
Be explicit about what counts as “handle time.” Most teams include talk and hold time. Some companies also include after-call work. Including after-call work is useful to track the "complete time" it takes to handle a call. But we have found it more useful to exclude it from the AHT calculation as after-call processes are fundamentally different than what his happening while on a call with a consumer. We instead prefer to break out after-call work time separately and manage that metric separately. Decide what’s in scope and keep it consistent.
Quick example: if your agents spent 540 minutes on talk + hold across 90 handled calls, your AHT is 6 minutes.
AHT is a ratio, not a scorecard
Average Handle Time is best used as a signal of workflow efficiency. It helps you spot when agents are spending more (or less) time per call, and it points you toward where to investigate.
What AHT cannot tell you by itself:
Whether calls were effective (conversion, appointment set rate, etc.)
Whether calls were compliant (required disclosures, consent captured, etc.)
Whether time spent was profitable (good long calls vs. bad long calls)
Once you can explain the AHT definition and formula, the next problem is interpretation: one blended number hides two very different businesses, so you need to separate them.
One blended AHT hides two different businesses: sales and non-sales
A single blended AHT often mixes conversations that deserve very different operational decisions.
Bucket | What it includes | How to interpret long calls |
|---|---|---|
Sales AHT | Calls that enter a sales/enrollment path and could reasonably end in a policy sale or booked appointment | Can be acceptable if conversion and compliance hold (or improve) |
Non-sales AHT | Wrong-fit leads, service requests, consent/identity-only calls, complaints, calls that never reach qualification | Usually a capacity leak that reduces sale-ready conversations |
Track these two AHTs separately by campaign and channel (e.g., inbound paid vs. outbound prospecting). If you use multiple call flows, also segment by script or intent (quotes vs. enrollments vs. service).
With the split visible, you can ask the operator-level question: are long calls producing value, or just absorbing licensed agent time?
Treat non-sales AHT as a capacity leak until proven otherwise
After isolating non-sales calls, decide which of that time is defensible by defining the outcomes that justify it.
Desirable outcomes for non-sales calls: booked appointment, qualified transfer to enrollment, verified consent for future contact, corrected lead data that enables follow-up.
Everything else is a capacity leak until you can show how it reliably leads to one of the outcomes above.
Common (fixable) causes of inflated non-sales AHT:
Unclear intent from marketing creative or lead forms (the caller expected something else).
Missing or inconsistent data at connect that forces repeated identity/consent steps.
Agents trying to “rescue” bad-fit leads instead of dispositioning quickly.
Repetitive verification because systems don’t share a single person record.
A clear plan to handle non-sales calls that is shared with agents can help reduce AHT. An effective strategy for a non-sale call can include:
Agent summarizes and sets the next action (transfer to service, schedule a callback, or end the call).
Agent applies a disposition that triggers follow-up actions like putting leads on do-not-contact lists, different campaigns, or e-mail journeys.
System routes salvageable cases away from licensed sellers (e.g., to a service queue or specialist) so licensed capacity stays focused on sales.
Lead intake and routing are usually the fastest way to reduce non-sales AHT. If you want to see how this is commonly automated, review Onyx Platform’s lead management features.
Shorter sales AHT comes from fewer repeats, not faster talking
With non-sales waste reduced, focus on sales calls that must convert. In practice, shorter successful sales calls come from removing repetition and decision friction—not from asking agents to talk faster or skip required disclosures.
Sales calls usually bloat for operational reasons:
Repeated data entry across separate systems.
Re-asking questions because information didn’t persist between screens or calls.
Plan comparisons too early (benefits talk before basic qualification is finished).
Long holds while agents search for plan details, provider/network info, or prior call notes.
A tight structure reduces pauses and repeats. A simple sequence:
Qualification first. Confirm eligibility and core needs in 2–3 targeted questions.
Product fit second. Match one likely product/category to the need rather than comparing everything immediately.
Enrollment steps last. Gather remaining data and complete consent/signature steps with a single, repeatable flow.
Use call reviews to coach micro-skills. Don’t coach “reduce AHT”; coach the exact step where calls stall (medication capture, provider lookup, SOA/consent timing, etc.). Then make one targeted change: a script tweak plus a focused drill.
When training closes those micro-gaps, AHT falls because agents repeat fewer actions and spend less time searching for missing information. The next bottleneck is usually the underlying workflow that forces agents to hunt across tools.
AHT improves when your workflow removes lookup work and dead-end calls
Durable AHT improvement typically comes from workflow alignment: the right information is available at the right time, and the call flow keeps agents out of dead ends.
Concrete levers you can pull:
Contextual record display. Surface prior interactions, key data, and dispositions at connect so agents don’t restart the conversation. A unified person record is the foundation—see CRM capabilities.
Script and disclosure alignment. Match script steps to data fields and compliance checks so required items happen in a predictable order. For regulated lines, call recording and monitoring should be built into the flow—see compliance tools.
Routing and pre-qualification rules. Route by state license, product eligibility, and intent; send service/renewals to non-licensed queues; block leads missing consent or critical fields.
Reduce avoidable holds. The faster agents can access the right queue, script, and caller context, the less time they spend placing callers on hold. Dialing operations (including inbound/outbound controls) also affect this—see dialer features.
Marketing also affects handle time. When ad messaging matches the script—and the lead form captures what you need—calls start in a qualified state and avoid long “orientation” segments.
Fixing workflow reduces lookup time and shrinks the number of dead-end conversations that pull licensed agents away from revenue-generating work. That sets up a measurement approach that protects conversion and compliance as you optimize AHT.
Use a simple measurement loop that protects conversion and compliance
Adopt a minimal cadence that prevents “chasing AHT” at the expense of outcomes or regulatory risk.
Track four numbers for sales and non-sales separately:
AHT for the bucket.
% of calls reaching a desirable outcome (booked appointment, verified consent, qualified transfer, etc.).
Progression rate (transfer rate, appointment set rate, or enrollment initiation rate—whatever represents the next revenue-producing step for your model).
Top dispositions by volume and by total minutes (volume × AHT) so you know which categories contain the most time.
If you need a place to operationalize those views, use reporting that can segment by campaign, script/intent, and disposition (see reporting and analytics).
Run a weekly 30-minute review with this agenda:
Pick the single disposition bucket with the highest total minutes (not just the highest AHT).
Listen to five representative calls and timestamp stall points (hold segments, repeated questions, long explanations, repeated verification).
Agree on one change: a script tweak, a routing rule, or a two-minute coaching action.
Monitor impact the following week and repeat with the next bucket.
Add guardrails:
If AHT falls but desirable outcomes fall too, revert and test a more conservative change.
If outcomes hold but compliance flags increase, freeze the change and run focused training on the steps involved.
These checks keep AHT improvements honest and ensure you’re not simply making calls shorter at the cost of revenue or regulatory exposure.
Conclusion
Average Handle Time is useful when you treat it as a diagnostic ratio rather than a scoreboard. Split AHT into sales and non-sales, stop non-sales capacity leaks, shorten sales calls by removing repeats, and remove lookup work with aligned workflow and routing.
Measure four focused metrics by bucket, run a weekly 30-minute review, and keep guardrails that protect conversion and compliance. Those steps turn AHT from a vanity number into an operational control you can actually change.
If you want help setting up AHT reporting by campaign and disposition—or aligning scripts, routing, and compliance checks so AHT drops without hurting conversion—contact Onyx Platform for a walkthrough: request a demo.
Read Article
More Articles
Compliance by Design: How AI Helps Agencies Stay on the Right Side of Regulation
Compliance “by design” means building compliance checks into your daily call workflow. This allows you to catch issues early, document outcomes automatically, and prove what happened when a carrier or regulator asks. In this guide, you’ll learn a practical, four-step process for using AI to scale call reviews, reduce risk, and keep humans in control of final judgment.
At Onyx Platform, we’ve seen agencies improve speed and consistency by combining experienced compliance leadership with AI-assisted review. Our Compliance Suite is designed to support Compliance Officers and QA teams by automatically reviewing calls against an agency-defined scorecard, surfacing key moments with timestamps, and summarizing the evidence that supports each score—across thousands of sales calls.
Guiding principle: AI doesn’t remove judgment—it changes where you spend your time
Used well, AI doesn’t replace your compliance lead. It shifts their time from listening to hours of calls and assembling documentation to higher-value work such as:
reviewing calls flagged by AI and jumping directly to the most relevant snippets,
finding patterns across agents, campaigns, and scorecards (not just one-off misses), and
coaching the behaviors that cause repeat failures to improve compliance performance.
This shift matters most during peak volume periods, when manual review often collapses into sampling and your risk concentrates in the calls nobody had time to fully review.
The 4-step process for “compliance by design” with AI
Step 1: Define (and maintain) your compliance scorecard
Whether your reviewer is human or AI, you need structured guidelines. Most agencies already have some version of this in scripts, SOPs, carrier requirements, or regulatory guidance. Turning those requirements into a clear scorecard is what makes consistent review possible. A strong scorecard is:
unambiguous (pass/fail criteria are clear),
auditable (each item can be supported by call evidence), and
actionable (coaching and process updates naturally follow from results).
Example requirements many agencies include: stating the agent’s full name, confirming licensure in the caller’s state, and disclosing that the call is being recorded early in the conversation (timing and exact language depend on your rules).
Step 2: Automatically record and transcribe calls
Once your scorecard exists, you need reliable documentation. That typically means call recordings plus accurate transcriptions so reviewers can search, quote, and verify what occurred.
Storage and retention requirements vary by industry and carrier. In regulated programs (for example, Medicare-related sales), recordings often need to be retained for multiple years and be retrievable quickly. Onyx Platform automates recording and transcription as part of the workflow so teams aren’t stuck managing files manually.
If you’re evaluating tooling, start with the fundamentals—consistent capture, secure storage, and easy retrieval. Learn more about the platform’s capabilities on our dialer and compliance features pages.
Step 3: Have AI review, score, and cite evidence
AI review works best when it does more than output a number. The goal is to produce a score and the supporting trail: what was said, when it was said, and which scorecard item it maps to. In practice, an effective AI scoring workflow should provide:
a score against each scorecard requirement,
clear reasoning (why it passed/failed), and
timestamp-backed references so a reviewer can jump straight to the relevant moment in the call.
This approach speeds review while improving defensibility—because you’re not just claiming compliance; you’re tying it to the specific evidence in the recording and transcript.
Step 4: Review exceptions, override when needed, and coach for prevention
At Onyx Platform we believe that humans should remain in control of outcomes. Your compliance team reviews the AI results, adds notes, and applies overrides when context requires it. The real operational win is what happens next: pattern-based coaching and process changes that prevent repeat issues.
With an exception-first workflow, compliance leaders spend less time on end-to-end listening and more time on:
coaching agents on recurring misses,
updating scorecards and playbooks when requirements shift, and
responding to carrier or complaint requests with a fast, timestamped evidence packet.
Why compliance by design works for small and midsize agencies
Small and midsize agencies don’t have unlimited QA headcount—and they can’t pause production every time requirements change. When call volume spikes, manual review becomes a throughput problem: reviewers either fall behind or narrow their sampling.
An AI-powered first pass reduces the number of calls that require a full listen-through, so your compliance lead can focus on work that actually lowers risk over time.
Next step: see how it fits your workflow
If you want a concrete example of what this looks like inside a call workflow, explore our Compliance Suite. To connect compliance outcomes to operational visibility (who is failing what, in which campaign, and whether it’s improving), review our reporting and analytics.
If you’d like to map your current process to an AI-assisted review workflow (including scorecard design, recording/transcription, exception handling, and reporting), contact our team for a walkthrough: request a demo.
Jan 6, 2026
4 min read
What Actually Happens When You Make or Receive a Call on a Cloud-Based Dialer
For an insurance agent, it feels effortless: Click to call and within moments, you're connecting with a prospect. Or you receive an inbound call, instantly routed to you with full prospect context already on screen.
But behind the scenes, a cloud-based dialer executes a complex choreography of technical processes, all in a fraction of a second. Understanding what happens under the hood reveals why modern dialers are so powerful, and how the right technology can dramatically improve both performance and compliance outcomes.
1. Lead Data Verification and Preparation
Before any call begins, an intelligent dialer validates every detail in your CRM:
Phone number formatting: Is the number callable and properly structured?
Compliance screening: Are there Do Not Call (DNC) flags or regulatory restrictions?
Contact history: Has this person been reached before, and what was the outcome?
Time zone validation: Is it legal calling hours for this prospect's location?
This pre-call verification ensures your agents aren't wasting time on bad numbers or risking costly regulatory violations. Quality dialers catch these issues before they become problems.
2. Smart Call Routing
Next, the dialer determines the optimal path to connect your call:
For outbound calls, the system selects the best outbound line and caller ID to maximize answer rates. Here's where platform quality makes a massive difference: Not every dialer maintains the same level of trust with telephone carriers. Poor carrier relationships result in calls being flagged as "spam likely," devastating your connect rates.
The call is then transmitted over the internet to the carrier, which connects to the public phone network and begins ringing the prospect's line.
Using a platform with proven experience in outbound dialing is critical. They understand the regulatory and technical methods needed to maintain carrier trust. This expertise is often the difference between a 5% and a 35% connect rate, directly impacting your marketing ROI.
3. Answering Machine Detection (AMD)
As the call connects, the dialer must instantly determine: Is this a live person or an answering machine?
Poor AMD creates terrible user experiences. Have you ever answered a call only to hear silence for several seconds? That's likely a dialer with slow, inaccurate answering machine detection. When detection takes too long, prospects hang up in frustration, resulting in lost marketing spend and damaged brand perception.
Advanced AMD systems use sophisticated audio analysis to make this determination in under a second, ensuring live prospects hear your agent immediately. This technical precision directly protects your marketing investment and prospect relationships.
4. Real-Time Logging and Analytics
From the moment dialing begins, the system captures critical performance data:
Ring duration: How long before pickup, voicemail, or abandonment
Connection quality: Audio clarity, latency, and technical metrics
Call outcome: Connected, dropped, busy, or rejected
Agent performance: Talk time, conversion rates, and productivity metrics
All this data flows into real-time dashboards, giving sales managers instant visibility into team performance and campaign effectiveness. This isn't just logging, it's intelligence that drives better decisions.
5. Compliance and Security Monitoring
Throughout every call, the cloud dialer silently enforces regulatory safeguards:
TCPA compliance: Monitoring calling hours, consent requirements, and frequency caps
DNC list management: Real-time checking against multiple suppression lists
CMS requirements: Applying Medicare-specific regulations when applicable
Call recording: Secure capture and storage for quality assurance and audits
Audit trails: Comprehensive logging for regulatory compliance
Your agents focus on conversations while the system automatically manages complex compliance requirements, reducing risk and protecting your business.
Why It Matters for Insurance Agencies
In high-volume environments like telephonic insurance sales, these behind-the-scenes processes directly impact your bottom line:
Higher Connect Rates: Advanced carrier relationships and spam prevention can triple your connection rates, maximizing marketing spend effectiveness.
Better Prospect Experience: Instant AMD and seamless call handling create professional first impressions that improve conversion rates.
Reduced Compliance Risk: Automated safeguards prevent costly violations while maintaining detailed audit trails.
Improved Agent Productivity: When technology handles complexity seamlessly, agents spend more time building relationships and closing sales.
Real-Time Optimization: Instant analytics enable managers to coach teams and adjust campaigns for maximum performance.
The Bottom Line
The next time you click "Dial" and hear a prospect say "Hello," remember: Your cloud dialer just executed dozens of complex technical processes invisibly and instantly. Those processes weren't just background tasks, they were critical success factors determining whether that call would connect, comply, and convert.
At Onyx Platform, our cloud dialer and unified CRM are engineered to make these behind-the-scenes processes seamless, fast, and compliant. When technology handles complexity perfectly, your agency can focus on what drives growth: building relationships and closing more sales.
Dec 30, 2025
5 min read
Would You Trust a 20-Year-Old Computer to Run Your Business? Why Do It with Your CRM and Dialer?
No one would seriously expect a computer from 2005 to keep up with today’s demands. The same goes for your phone. You probably upgrade every few years without a second thought.
Yet inside many insurance agencies, carriers, and call centers, the CRM and dialer powering day-to-day operations were built more than 20 years ago (and many even longer). Tools designed for another era are still being asked to carry the weight of compliance-heavy and high-volume operations.
So why are you surprised that your platform has issues?
The cracks are hard to ignore
Agents lose valuable time switching systems. Studies have shown that nearly 70% of contact center agents report being slowed down by toggling between platforms like CRM and telephony tools. Multiply that across a 500-seat call center, and the productivity loss is staggering.
Data is fragmented, reporting is reactive. Integrated systems significantly increase data accuracy, reducing reporting lags and freeing up teams from manual work. Without tools that are seamlessly integrated, leaders are left piecing together spreadsheets on a significant time delay.
Compliance is an afterthought. With regulators increasing scrutiny, relying on bolt-on QA tools and after-the-fact call reviews isn’t enough. The cost of a missed disclosure can be enormous.
The future looks different
Modern platforms are showing what’s possible:
Integrated, not stitched together. One platform providing all of your CRM, dialer, QA, and analytics needs.
Compliance-first, not compliance-bolted-on. Real-time data and immediate call reviews reduce risk instead of documenting it after the fact.
Native AI tools and assistants built right into the platform. Stop adding yet another tool to your system, and start buying fewer things that do more for your business.
The question to ask
It’s not whether legacy systems can still function, it’s whether you would accept that level of performance anywhere else.
If you wouldn’t trust a 20-year-old computer or a flip phone to run your business today, why keep relying on a CRM and dialer built for another decade?
The agencies and carriers that reevaluate now will find themselves leaner, faster, and better equipped for the next decade.
So ask yourself: how old is your core tech, and what’s it really costing you?
Dec 30, 2025
3 min read
How Real-Time Dashboards Work
Have you ever wondered what really goes on behind the scenes when you look at a live data dashboard? At Onyx Platform, our tools give agencies a real-time (or near-real-time) view into their most important metrics. But that “instant insight” is powered by a carefully orchestrated flow of data. Here’s a look under the hood.
1. Capturing the Raw Data
Every time someone in your agency takes an action — placing a call, enrolling a customer, following up on an appointment — that event is logged as a data point. Our platform captures these data points continuously and registers updates the moment they happen.
2. Streaming the Information
When we say “real-time,” we mean that data is transmitted continuously, often using a data streaming service that can ingest tens of thousands of updates per second. This is like a high-speed conveyor belt, delivering all the small pieces of data into our central data platform as quickly as they come in.
3. Processing and Aggregating
Before raw data can go into a dashboard, it must be cleaned, organized, and aggregated into meaningful metrics. For example, “sales per hour per agent” or “average wait time per call.” Our data pipeline handles this transformation efficiently. Under the hood, this can involve caching data in-memory so queries return results in milliseconds, and using incremental updates so you can see the new information together with old information without reloading everything.
4. Feeding the Dashboard
That cleaned, aggregated data is then sent to the front end of our dashboarding system, typically a web application built to refresh charts and tables automatically as new data streams in. Modern dashboarding tools use technologies like WebSockets and reactive frameworks, which allow your screen to update without you clicking “refresh.” Filters like date range, team, or region simply add a query layer that lets you zoom into the data that matters most.
5. Turning Data Into Action
When you put this all together, you get a dynamic window into your business. Real-time dashboarding enables managers to spot issues as they happen, adjust tactics instantly, and recognize team successes the moment they occur.
Ideally, no matter which platform you use, real-time data and visualizations should feel effortless. But under the surface, they’re powered by a combination of smart integrations, fast data processing, and intuitive interface design, all working together to help you make decisions faster and with more confidence.
Dec 30, 2025
3 min read