{"id":5608,"date":"2026-07-01T11:42:33","date_gmt":"2026-07-01T11:42:33","guid":{"rendered":"https:\/\/verbix.ai\/blog\/?p=5608"},"modified":"2026-07-03T07:56:46","modified_gmt":"2026-07-03T07:56:46","slug":"voicebot-performance-metrics","status":"publish","type":"post","link":"https:\/\/verbix.ai\/blog\/voicebot-performance-metrics\/","title":{"rendered":"Voicebot Performance Metrics: What Should You Track?"},"content":{"rendered":"\n<p>Voicebots\u2002are no longer a futuristic novelty \u2014 they\u2019re taking customer calls, qualifying leads, booking appointments, and solving support tickets 24\/7. But\u2002launching a voicebot is just half the battle. The real question is: <strong>how can you tell whether\u2002it\u2019s working?<\/strong><strong>&nbsp;<\/strong><\/p>\n\n\n\n<p>Knowing which performance metrics to track is what differentiates voicebot deployments that provide ROI from those that leave customers silently\u2002frustrated and organizations resource-drained. Whether you are working with a IVR-replacement bot, an outbound sales dialer or a multilingual support agent,\u2002the metrics you are tracking will tell you how quickly you can gather data, change, grow and trust your AI voice solution.&nbsp;<\/p>\n\n\n\n<p>In this guide, we examine the most important\u2002voicebot performance metrics \u2014 what they are, why they matter, and what you can do about them.&nbsp;<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><a href=\"https:\/\/verbix.ai\/blog\/wp-content\/uploads\/2026\/07\/voicebot-performance-metrics-key-kpis.png\"><img loading=\"lazy\" decoding=\"async\" width=\"1456\" height=\"720\" src=\"https:\/\/verbix.ai\/blog\/wp-content\/uploads\/2026\/07\/voicebot-performance-metrics-key-kpis.png\" alt=\"Voicebot performance metrics and key KPIs\" class=\"wp-image-5617\" srcset=\"https:\/\/verbix.ai\/blog\/wp-content\/uploads\/2026\/07\/voicebot-performance-metrics-key-kpis.png 1456w, https:\/\/verbix.ai\/blog\/wp-content\/uploads\/2026\/07\/voicebot-performance-metrics-key-kpis-300x148.png 300w, https:\/\/verbix.ai\/blog\/wp-content\/uploads\/2026\/07\/voicebot-performance-metrics-key-kpis-1024x506.png 1024w, https:\/\/verbix.ai\/blog\/wp-content\/uploads\/2026\/07\/voicebot-performance-metrics-key-kpis-768x380.png 768w\" sizes=\"auto, (max-width: 1456px) 100vw, 1456px\" \/><\/a><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Why Voicebot Metrics Are Different from Chatbot Metrics<\/strong><\/h2>\n\n\n\n<p>Prior to getting started, it is important to understand that voice is a very different medium than text. Voice is real-time, linear and emotionally charged (very\u2002rarely is text-based chat), while these are strong endearments of gloom in the mood of any RTA gamer). A user who is confused or frustrated on a call has\u2002much less room to recover than a user typing in a chat window.&nbsp;<\/p>\n\n\n\n<p>Which means\u2002that your metrics have to be adapted for:&nbsp;<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Speech recognition errors<\/strong> (not only intent mismatches)<\/li>\n\n\n\n<li><strong>Latency and\u2002audio quality<\/strong> (pauses and delays are excruciating on calls)<\/li>\n\n\n\n<li><strong>The emotional tone of the caller and\u2002caller patience<\/strong><\/li>\n\n\n\n<li><strong>Mid-conversation drop-offs<\/strong> (hang up is instantaneous and\u2002irreversible)&nbsp;<\/li>\n<\/ul>\n\n\n\n<p>With that framing in mind, here are the metrics that matter most.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>1. Call Completion Rate (CCR)<\/strong><\/h2>\n\n\n\n<p><strong>What it is:<\/strong> The Ratio of calls where the voicebot achieved a pre-defined end state (e.g. completing a transaction, answering a query or transferring the caller to an agent).&nbsp;&nbsp;<\/p>\n\n\n\n<p><strong>Why it matters:<\/strong> Your\u2002CCR is your headline metric. A poor completion\u2002rate is indicative of customers leaving, becoming further confused then stuck in a loop, or saying, \u201cAgent, agent, agent!\u201d &nbsp;<\/p>\n\n\n\n<p><strong>What to aim for:<\/strong> Use\u2002case-specific industry average varies. Appointment scheduling bots typically result in 80\u201390% CCR; more complex support queries can be as low as 60\u201370%. Get a baseline of\u2002your own and measure progress.&nbsp;&nbsp;<\/p>\n\n\n\n<p><strong>How to improve it:<\/strong> Audit most\u2002common exit points. Are callers getting\u2002tired of a certain prompt? Is there particular\u2002intent that always fails? Use these drop-off visualizations to re-structure conversation flows.&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>2. Containment Rate<\/strong><\/h2>\n\n\n\n<p><strong>What it is:<\/strong> The proportion of calls that are fully addressed by the voicebot without transferring to\u2002a live human agent.&nbsp;<\/p>\n\n\n\n<p><strong>Why it matters:<\/strong> Containment rate\u2002is directly related to cost of operation.Each call\u2002a voicebot resolve without an agent takes less of an agent\u2019s time. It&#8217;s one of the\u2002primary financial reasons for implementing a voicebot in the first place.&nbsp;<\/p>\n\n\n\n<p><strong>Containment vs. Completion:<\/strong> Both related\u2002and different. A call can be &#8220;completed&#8221; by the bot after it transfers to a human (if\u2002transfer was the appropriate resolution). To the contrary, containment is completely about calls\u2002with no humans at all.&nbsp;<\/p>\n\n\n\n<p><strong>Healthy benchmarks: <\/strong>A well-tuned customer service voicebot should contain 70\u201385% of calls within its scope\u2002for incoming calls. Anything\u2002less than 50% usually denotes trust issues with the bot or gaps of coverage on intents.<strong>&nbsp;<\/strong><\/p>\n\n\n\n<p><strong>How to improve it:<\/strong> Next, plot the\u2002escalation reasons. If &#8220;agent,&#8221; &#8220;representative&#8221; or similar terms are being repeated by callers, it might mean they not only perceive the\u2002bot lacks coverage, but maybe the bot isn\u2019t really helping. Run sentiment analysis on\u2002those moments.&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>3. Intent Recognition Accuracy<\/strong><\/h2>\n\n\n\n<p><strong>What it is:<\/strong> The percent of caller utterances in which the voicebot accurately predicts the\u2002intent of what the caller is trying to do.&nbsp;<\/p>\n\n\n\n<p><strong>Why it matters:<\/strong> Everything rests on\u2002that foundation. If your bot doesn&#8217;t even understand what callers\u2002are asking for, no amount of conversational design will help it. Intent recognition accuracy is what determines if the bot leads the caller down the correct path, or\u2002round and round in \u201cI\u2019m sorry, I didn\u2019t catch that.\u201c<\/p>\n\n\n\n<p><strong>Measurement approach:<\/strong> Tag\u2002N real calls and check manually whether the detected intent actually corresponds to the real intent of the call. Seek\u2002to maintain the same evaluation pace \u2014 weekly or biweekly.&nbsp;<\/p>\n\n\n\n<p><strong>Target range:<\/strong> 90%+ accuracy for high\u2002volume, well-defined intents. For open-ended or long-tail queries, 80\u201385% is achievable\u2002in the absence of continual training. &nbsp;<\/p>\n\n\n\n<p><strong>How to improve it:<\/strong> Regularly monitor the low confidence intent matches and\u2002utterances that were classified as a &#8220;fallback&#8221; or &#8220;no match&#8221;. Use\u2002these to increase your training data and improve your NLU model.&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>4. Speech Recognition Rate (Word Error Rate)<\/strong><\/h2>\n\n\n\n<p><strong>What it is:<\/strong> The accuracy of the ASR engine \u2014 in other words, how often the transcribed\u2002text is what the caller actually said. This is\u2002usually calculated as <strong>Word Error Rate (WER)<\/strong>, the lower the better.&nbsp;<\/p>\n\n\n\n<p><strong>Why it matters:<\/strong> You can&#8217;t save a call with bad words, even if they&#8217;re from the best NLU model. ASR mistakes are also particularly painful\u2002with proper nouns (names, addresses, account numbers), accented speech and noisy call centers.&nbsp;<\/p>\n\n\n\n<p><strong>What to monitor:<\/strong> Monitor how WER changes with caller demographics, call types (mobile versus landline, quiet versus noisy), and\u2002languages if you have a multilingual bot.&nbsp;<\/p>\n\n\n\n<p><strong>Acceptable WER:<\/strong> For voicebot applications a WER of less than\u200210% is usually considered acceptable in tightly controlled environments. Anything over 15-20 percent is going to have a\u2002really bad effect on user experience.&nbsp;<\/p>\n\n\n\n<p><strong>How to improve it:<\/strong> Collaborate with your\u2002ASR provider on the possibility of using custom language models for your domain vocabulary. Verbix.ai&#8217;s voice engine, for instance, provides domain specific acoustic and language model adaptation to minimize WER substantially in\u2002domain applications.&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>5. Average Handle Time (AHT)<\/strong><\/h2>\n\n\n\n<p><strong>What it is:<\/strong> What it is: How long the average\u2002call lasts from the time the call starts to close, including bot interactions and any human interaction time.&nbsp;<\/p>\n\n\n\n<p><strong>Why it matters:<\/strong> AHT is a quintessential contact center metric and still holds true in the voicebot world. Bot efficiency: A shorter AHT (along with\u2002high containment and completion rates) indicates that your bot is resolving calls effectively. Increasing AHT\u2002suggests users are getting more confused, are being reprompted more than they should, or are talking too long.&nbsp;&nbsp;<\/p>\n\n\n\n<p><strong>What to watch for:<\/strong> Don\u2019t optimize AHT alone. A terse\u2002and unhelpful bot, that hastens resolution and leads to shorter calls, will hurt satisfaction scores. Always monitor AHT in\u2002conjunction with completion rate and CSAT.&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>6. Transfer Rate and Transfer Reason Codes<\/strong><\/h2>\n\n\n\n<p><strong>What it is:<\/strong> The proportion of calls transferred\u2002to a live agent, along with the reasons for transfer classified.&nbsp;<\/p>\n\n\n\n<p><strong>Why it matters:<\/strong> Bad transfers are the bane of mobile and remote call center support \u2014 but it doesn\u2019t mean all transfers are bad, some just need a different handle. But monitoring the reasons a\u2002call is transferred is absolute gold for product teams. Reason codes illustrate the difference between what your bot is able to handle and what your callers actually\u2002do need.&nbsp;&nbsp;<\/p>\n\n\n\n<p><strong>Transfer reason categories to track:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Caller requested\u2002an agent.&nbsp;<\/li>\n\n\n\n<li>Unrecognized intent&nbsp;<\/li>\n\n\n\n<li>Caller frustration detected&nbsp;<\/li>\n\n\n\n<li>Task complexity beyond scope\u2002of bot&nbsp;<\/li>\n\n\n\n<li>System\/API\u2002error call&nbsp;<\/li>\n\n\n\n<li>Explicit\u2002escalation keyword triggered&nbsp;<\/li>\n<\/ul>\n\n\n\n<p><strong>How to act on it:<\/strong> High \u201ccaller requested agent\u201d rates along with low \u201ctask complexity\u201d transfers are generally a sign of a trust or experience issue \u2013 not a problem with capabilities. That&#8217;s UX\u2002work, not more training data.&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>7. First Call Resolution (FCR)<\/strong><\/h2>\n\n\n\n<p><strong>What it is:<\/strong> The\u2002percentage of calls on which the caller\u2019s issue is completely resolved during the initial interaction \u2014 no callbacks, no follow-up calls, no escalations.&nbsp;<\/p>\n\n\n\n<p><strong>Why it matters:<\/strong> FCR can be considered the most important\u2002metric impacting the customer experience in any contact center. It indicates whether the caller\u2019s real problem \u2014 the reason they placed a call\u2002in the first place \u2014 was resolved.&nbsp;<\/p>\n\n\n\n<p><strong>How to measure it in a voicebot context:<\/strong> FCR is difficult\u2002to track, given that it involves post-call information (did the caller reach out again within X days with the same issue?). Integrate your voicebot analytics with your CRM or call\u2002recording repository for this view.&nbsp;<\/p>\n\n\n\n<p><strong>Target:<\/strong> The\u2002well-deployed voicebot dealing with mundane enquiries should achieve FCR results of tell 75%+. If a bot scores less than 60% for simple tasks, this tells us that\u2002the bot is just making superficial calls without really fulfilling the underlying needs.&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>8. Customer Satisfaction Score (CSAT) and Post-Call Surveys<\/strong><\/h2>\n\n\n\n<p><strong>What it is:<\/strong> A localized indicator of callers&#8217; satisfaction with their\u2002voicebot experience, usually obtained via a post-call IVR survey or SMS follow-up. &nbsp;<\/p>\n\n\n\n<p><strong>Why it matters:<\/strong> These are the results of your quantitative metrics like CCR and AHT in telling you what happened. CSAT tells you how callers felt about it. And these two datasets can sometimes be quite different \u2014 a bot might have a high completion rate but a low CSAT\u2002because it sounds robotic, is moving too quickly, or is delivering answers that are technically correct but practically unhelpful.&nbsp;<\/p>\n\n\n\n<p><strong>Survey design tip:<\/strong> Limit your post-call surveys to one or two\u2002queries. &#8220;How satisfied were you with your call\u2002today?&#8221; on a scale of 1 to 5 is sufficient. If you want to distinguish bot satisfaction from agent\u2002satisfaction, you can ask about the bot specifically.&nbsp;<\/p>\n\n\n\n<p><strong>CSAT targets:<\/strong> Voicebots should deliver a CSAT\u2002score of 3.8\/5 or better for consumer facing engagements. Target 4.0+ for B2B and enterprise voicebots where\u2002expectations are extremely high.&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>9. Fallback Rate<\/strong><\/h2>\n\n\n\n<p><strong>What it is:<\/strong> A turn rate show how\u2002often the exchange takes place between both sides. (Turns are individual exchanges within a call.) For calls with two or more turns: the percentages of calls for which the voicebot\u2002did not know what the user was saying and fell back to an answer such as \u201cI\u2019m sorry, what were you saying?\u201d or \u201cI didn\u2019t catch that,\u201d etc.&nbsp;&nbsp;<\/p>\n\n\n\n<p><strong>Why it matters:<\/strong> The\u2002most telling sign that your bot is having a bad day is a high fallback rate. Every fallback is a point of friction \u2014 and multiple fallbacks during one call nearly always\u2002ends in an angry transfer or a hang-up.&nbsp;<\/p>\n\n\n\n<p><strong>Measurement:<\/strong> Monitor the fallback rate at the global and intent\/flow\u2002level. A high aggregate fallback rate indicates\u2002general NLU coverage problems. High fallout rates at certain nodes indicate these conversation steps are flawed\u2002and need to be reworked.<br><br><strong>Target:<\/strong> It is desirable replenishment rate\u2002to be below 8\u201310% of all turns. A level\u2002above 15% indicates a widespread problem which needs either model re-training or flow restructuring.&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>10. Latency and Response Time<\/strong><\/h2>\n\n\n\n<p><strong>What it is:<\/strong> The time from when the caller stops speaking until the voicebot starts speaking. including ASR processing time, NLU inference time, API calls, TTS rendering, and\u2002audio delivery. &nbsp;<\/p>\n\n\n\n<p><strong>Why it matters:<\/strong> In human\u2002conversation, a response that takes more than 1.5\u20132 seconds to initiate is uncomfortable. When on the phone, you can feel delays of more than 2 seconds and breaks at\u2002more than 3 seconds. Latency\u2002is the single biggest detractor to the perceived quality of your voicebot, even if it\u2019s saying the right thing.<br><br><strong>Components to measure separately:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>ASR latency (time to transcribe speech)<\/li>\n\n\n\n<li>NLU latency (time to detect intent)<\/li>\n\n\n\n<li>API call latency (backend integrations)<\/li>\n\n\n\n<li>TTS latency (text-to-speech rendering)<\/li>\n\n\n\n<li>Network\/audio delivery time<\/li>\n<\/ul>\n\n\n\n<p><strong>Target:<\/strong> End-to-end latency under\u20021.5 seconds for simple intents. Complex API-dependent responses should be under\u20022.5 seconds, with appropriate \u201cthinking\u201d prompts to bridge the gap.&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>11. Sentiment and Emotion Detection<\/strong><\/h2>\n\n\n\n<p><strong>What it is:<\/strong> Con\u00adsumer-tone analysis in\u2002real-time or following a call in which frustration, confusion, satisfaction or urgency is detected on the part of the consumer during the call. &nbsp;<\/p>\n\n\n\n<p><strong>Why it matters:<\/strong> The sentiment adds an additional intelligence layer that is missed in\u2002pure performance metrics. A caller who grows more\u2002and more agitated over several turns \u2014 even if they have yet to explicitly request an agent \u2014 might deserve a proactive escalation before things get worse.<\/p>\n\n\n\n<p><strong>How to use it:<\/strong> Create\u2002rules that automatically escalate calls when negative sentiment exceeds a threshold. Use post-call sentiment to highlight interactions for\u2002QA review. Combine sentiment trends to\u2002discover which intent\/flow is most likely to trigger a negative emotional reaction.&nbsp;<\/p>\n\n\n\n<p><strong>Advanced application:<\/strong> The voice analytics layer of Verbix.ai allows to perform sentiment scoring in real-time and thus call routing decisions may be altered on the fly during the call, and not only when a predefined escalation trigger is reached.&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>12. Opt-Out and &#8220;Agent Request&#8221; Rate<\/strong><\/h2>\n\n\n\n<p><strong>What it is:<\/strong> Callers who explicitly say they want a human agent, utter trigger words such as &#8220;operator,&#8221; &#8220;representative,&#8221; or &#8220;human&#8221; or tap 0 to leave the bot flow.&nbsp;<\/p>\n\n\n\n<p><strong>Why it matters:<\/strong> &nbsp;This is one\u2002of the most truthful signals that you have in your data set. When callers \u201cvote with their feet\u201d\u2002by actively seeking an exit from the bot, that\u2019s telling. A large number of opt-outs indicate\u2002that your bot is not trusted \u2014 and that degrades both containment and satisfaction in lock-step. &nbsp;<\/p>\n\n\n\n<p><strong>How to investigate:<\/strong> Look at opt-out events against the\u2002exact moment in the dialogue. Is it right at the beginning of the call (general bot mistrust)? Or is at\u2002some specific prompt that\u2019s ambiguous, misleading? &nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Building a Voicebot Metrics Dashboard<\/strong><\/h2>\n\n\n\n<p>You can\u2019t just monitor these\u2002metrics separately \u2014 you need a holistic picture emerge from them. Here&#8217;s a proposed dashboard layout for\u2002the following concourse:&nbsp;<\/p>\n\n\n\n<p><strong>Tier 1 \u2014 Business KPIs (executive view)<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Containment Rate<\/li>\n\n\n\n<li>First Call Resolution<\/li>\n\n\n\n<li>CSAT Score<\/li>\n\n\n\n<li>Cost Per Call<\/li>\n<\/ul>\n\n\n\n<p><strong>Tier 2 \u2014 Operational Health (ops\/product view)<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Call Completion Rate<\/li>\n\n\n\n<li>Transfer Rate by Reason Code<\/li>\n\n\n\n<li>Average Handle Time<\/li>\n\n\n\n<li>Fallback Rate<\/li>\n<\/ul>\n\n\n\n<p><strong>Tier 3 \u2014 Technical Performance (engineering\/AI view)<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Intent Recognition Accuracy<\/li>\n\n\n\n<li>Word Error Rate (ASR)<\/li>\n\n\n\n<li>End-to-End Latency<\/li>\n\n\n\n<li>Sentiment Trend<\/li>\n<\/ul>\n\n\n\n<p>Review Tier 1 on a weekly basis, Tier 2 daily, and Tier 3 in real-time with alerting thresholds on\u2002fallback rate, latency and WER.&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>How Verbix.ai Helps You Track What Matters<\/strong><\/h2>\n\n\n\n<p>At Verbix.ai we\u2002know that great voicebot performance begins with great observability. Our platform\u2002offers:&nbsp;<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Real-time analytics dashboards for all the\u2002metrics mentioned in this Guide<\/li>\n\n\n\n<li>&nbsp;Call-level transcripts and intent maps so you can test individual\u2002interactions<\/li>\n\n\n\n<li>Sentiment\u2002and emotion analysis in every call, not applied after the fact<\/li>\n\n\n\n<li>Automated\u2002QA scoring, which helps identify the poorest-performing conversation flow, without reviewing manually<\/li>\n\n\n\n<li>Tailored benchmark reports\u2002for your industry, use case and call volume&nbsp;<\/li>\n<\/ul>\n\n\n\n<p>Whether you\u2019re building your first voicebot or scaling your million-plus enterprise \u2014 it\u2019s the right metrics framework that\u2019s\u2002what turns a voice AI deployment from a cost center into a competitive advantage.&nbsp;<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><a href=\"https:\/\/verbix.ai\/blog\/wp-content\/uploads\/2026\/07\/voicebot-performance-tracking.png\"><img loading=\"lazy\" decoding=\"async\" width=\"1456\" height=\"720\" src=\"https:\/\/verbix.ai\/blog\/wp-content\/uploads\/2026\/07\/voicebot-performance-tracking.png\" alt=\"Verbix.ai voice analytics features\" class=\"wp-image-5618\" srcset=\"https:\/\/verbix.ai\/blog\/wp-content\/uploads\/2026\/07\/voicebot-performance-tracking.png 1456w, https:\/\/verbix.ai\/blog\/wp-content\/uploads\/2026\/07\/voicebot-performance-tracking-300x148.png 300w, https:\/\/verbix.ai\/blog\/wp-content\/uploads\/2026\/07\/voicebot-performance-tracking-1024x506.png 1024w, https:\/\/verbix.ai\/blog\/wp-content\/uploads\/2026\/07\/voicebot-performance-tracking-768x380.png 768w\" sizes=\"auto, (max-width: 1456px) 100vw, 1456px\" \/><\/a><\/figure>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<h4 class=\"wp-block-heading\"><strong>Final Thoughts<\/strong><\/h4>\n\n\n\n<p>The\u2002best voicebot isn\u2019t necessarily the one with the most advanced NLU or the most human-sounding TTS. This\u2002is the voicebot that is demonstrably solving the problems of callers \u2014 quickly, reliably and in a manner that callers actually find itself useful.<\/p>\n\n\n\n<div style=\"height:9px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p>Focus on contain rate and\u2002CSAT to measure business impact. Explore intent accuracy and fallback rate\u2002to identify NLU gaps. Keep an eye on\u2002latency and WER, to make sure you\u2019re good on the tech side of things. And don\u2019t ever stop closing\u2002the loop \u2014 each and every call can become a data point to help you make your bot smarter. It\u2019s all there, as far\u2002as the indicators go. The question is, are you listening\u2002to them?<\/p>\n<\/blockquote>\n","protected":false},"excerpt":{"rendered":"<p>Voicebots\u2002are no longer a futuristic novelty \u2014 they\u2019re taking customer calls, qualifying leads, booking appointments, and solving support tickets 24\/7. But\u2002launching a voicebot is just half the battle. The real question is: how can you tell whether\u2002it\u2019s working?&nbsp; Knowing which performance metrics to track is what differentiates voicebot deployments that provide ROI from those that [&hellip;]<\/p>\n","protected":false},"author":9,"featured_media":5614,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[45],"tags":[],"class_list":["post-5608","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-call-analytics-software"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/verbix.ai\/blog\/wp-json\/wp\/v2\/posts\/5608","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/verbix.ai\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/verbix.ai\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/verbix.ai\/blog\/wp-json\/wp\/v2\/users\/9"}],"replies":[{"embeddable":true,"href":"https:\/\/verbix.ai\/blog\/wp-json\/wp\/v2\/comments?post=5608"}],"version-history":[{"count":2,"href":"https:\/\/verbix.ai\/blog\/wp-json\/wp\/v2\/posts\/5608\/revisions"}],"predecessor-version":[{"id":5620,"href":"https:\/\/verbix.ai\/blog\/wp-json\/wp\/v2\/posts\/5608\/revisions\/5620"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/verbix.ai\/blog\/wp-json\/wp\/v2\/media\/5614"}],"wp:attachment":[{"href":"https:\/\/verbix.ai\/blog\/wp-json\/wp\/v2\/media?parent=5608"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/verbix.ai\/blog\/wp-json\/wp\/v2\/categories?post=5608"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/verbix.ai\/blog\/wp-json\/wp\/v2\/tags?post=5608"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}