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Those are the primary and trusted performance metrics that indicate whether the call was successful, the issue was resolved, and whether customers will remain loyal to the business. But these metrics often miss the emotions beneath the conversation, like anger, frustration, and confusion that drive customer behavior, loyalty, and ultimately revenue. Sentiment analysis bridges that gap.&nbsp;&nbsp;Well, business owners may argue that at the end of the day, if the customer stays with the business and the issue is solved, the emotions aren’t really important. Not really, because when analyzing customer churn, it is not always easy to find a root cause. Moreover, sentiment analysis is critical for constant service improvement. With this in mind, let’s talk about sentiment analysis from the business perspective.&nbsp;<h2 style="line-height:1.38;margin-bottom:0pt;margin-top:0pt;" dir="ltr">The Hidden Cost of Missed Emotions</h2>Let’s admit, today customers are picky, and it’s not because their behavior has changed. It is all because of the market, where customers have infinite choices. When they stick to one brand, one product, or one service, they expect it to be flawless. Fair enough, that’s why today's businesses should compete not only with product quality but also with customer service.&nbsp;&nbsp;<h3 style="line-height:1.38;margin-bottom:0pt;margin-top:0pt;" dir="ltr">Emotions have real costs</h3>&nbsp;In a<a style="text-decoration:none;" target="_blank" rel="noopener noreferrer" href="https://experiencematters.wordpress.com/2016/10/21/exploring-multiple-emotions-during-contact-center-interactions/?utm_source=chatgpt.com"> study</a> of 118,000 calls across 11 major brands, customers who expressed fear during the call had call durations up to 87% higher than average and were 3.5× more likely to be transferred to a supervisor. One interesting observation is that during longer calls, customers experience all four emotions: joy, anger, fear, and sadness.&nbsp;&nbsp;When anger is detected, Net Promoter Scores (NPS) drop significantly: ~19% below average, and those calls are 40% longer than typical calls.&nbsp;&nbsp;Another<a style="text-decoration:none;" target="_blank" rel="noopener noreferrer" href="https://www.researchgate.net/publication/391441927_Sentiment_Analysis_to_Detect_Churn_Signals"> NLP-based analysis</a> of 10,000 anonymized customer records showed that negative sentiment led to churn within 6 months. On the other hand, with real-time sentiment analysis, businesses managed to improve CSAT accuracy by 23% and get a better picture of customer experience.&nbsp;&nbsp;<h3 style="line-height:1.38;margin-bottom:0pt;margin-top:0pt;" dir="ltr">Why traditional metrics fall short</h3>Traditional approach: random QA of &lt;10% of calls, post-mortems, supervisor coaching, and agent training. These are valuable, but:&nbsp;<ul><li>They detect emotion too late, often after damage is done. And when we say too late, we mean in the next month(s), customer engagement report, and churn statistics.&nbsp;</li><li>They rely on subjective evaluation or memory. Agents work in high-stress environments, and it is absolutely impossible to remember all the conversations.</li><li>They cannot scale in real time. Even the most advanced post-call analysis won’t deliver precise information about customer emotions at the very moment.</li></ul>&nbsp;Business consequence: frustrated customers, lower retention, service escalations, and agents under stress.&nbsp;<h2 style="line-height:1.38;margin-bottom:0pt;margin-top:0pt;" dir="ltr">What Sentiment Analysis Really Does</h2>Sentiment analysis turns emotional cues in the voice into measurable, actionable business intelligence. Both post-call and real-time analytics are efficient, each having its unique value for call center performance management and customer experience improvement.&nbsp;<ul><li>Real-Time Layer: Monitors live calls for emotional signals; triggers alerts to the agent and/or supervisor when frustration or dissatisfaction is detected.</li><li>Post-Call Analytics: Every call is indexed for sentiment: percent of time negative vs. positive, escalation points, and emotional arcs. Supervisors can filter by agent, team, or emotion type.</li></ul>Core value: detecting emotionally risky interactions early, measuring what truly matters (customer perception), and enabling proactive correction before it hurts loyalty or metrics. By correction, it can be agent replacement.&nbsp;<h3 style="line-height:1.38;margin-bottom:4pt;margin-top:16pt;" dir="ltr">From Pain Points to Solutions: How Sentiment Analysis Fixes CX Gaps</h3>&nbsp;<div style="margin-left:0pt;" dir="ltr" align="left"><figure class="table"><table class="ck-table-resized" style="border-collapse:collapse;border-style:none;"><colgroup><col style="width:50%;" width="214"><col style="width:50%;" width="394"></colgroup><tbody><tr style="height:14.25pt;"><td style="border-color:#9a9a9a;border-width:0.75pt;overflow-wrap:break-word;overflow:hidden;padding:1pt 4pt;vertical-align:top;">Pain Point</td><td style="border-color:#9a9a9a;border-width:0.75pt;overflow-wrap:break-word;overflow:hidden;padding:1pt 4pt;vertical-align:top;">How Sentiment Analysis Helps</td></tr><tr style="height:26.25pt;"><td style="border-color:#9a9a9a;border-width:0.75pt;overflow-wrap:break-word;overflow:hidden;padding:1pt 4pt;vertical-align:top;">Undetected customer frustration → repeated calls, churn</td><td style="border-color:#9a9a9a;border-width:0.75pt;overflow-wrap:break-word;overflow:hidden;padding:1pt 4pt;vertical-align:top;">Real-time detection lets agents adjust or escalate immediately.</td></tr><tr style="height:26.25pt;"><td style="border-color:#9a9a9a;border-width:0.75pt;overflow-wrap:break-word;overflow:hidden;padding:1pt 4pt;vertical-align:top;">Long AHT because of clarifications or repeats</td><td style="border-color:#9a9a9a;border-width:0.75pt;overflow-wrap:break-word;overflow:hidden;padding:1pt 4pt;vertical-align:top;">Faster de-escalation; less wasted time.</td></tr><tr style="height:26.25pt;"><td style="border-color:#9a9a9a;border-width:0.75pt;overflow-wrap:break-word;overflow:hidden;padding:1pt 4pt;vertical-align:top;">Low first-call resolution rates</td><td style="border-color:#9a9a9a;border-width:0.75pt;overflow-wrap:break-word;overflow:hidden;padding:1pt 4pt;vertical-align:top;">Agents can better understand hidden dissatisfaction and address issues fully.</td></tr><tr style="height:26.25pt;"><td style="border-color:#9a9a9a;border-width:0.75pt;overflow-wrap:break-word;overflow:hidden;padding:1pt 4pt;vertical-align:top;">Poor CSAT / negative word-of-mouth</td><td style="border-color:#9a9a9a;border-width:0.75pt;overflow-wrap:break-word;overflow:hidden;padding:1pt 4pt;vertical-align:top;">Catching emotion and responding improves perception and makes customers feel heard.</td></tr><tr style="height:26.25pt;"><td style="border-color:#9a9a9a;border-width:0.75pt;overflow-wrap:break-word;overflow:hidden;padding:1pt 4pt;vertical-align:top;">Agent burnout &amp; turnover</td><td style="border-color:#9a9a9a;border-width:0.75pt;overflow-wrap:break-word;overflow:hidden;padding:1pt 4pt;vertical-align:top;">Helping agents know when calls are emotionally charged gives them a chance for support, rather than constant overwhelm.</td></tr></tbody></table></figure></div>&nbsp;<h2 style="line-height:1.38;margin-bottom:6pt;margin-top:18pt;" dir="ltr">Real-World Scenarios Where Sentiment Analysis Makes the Difference</h2>The adoption of sentiment analysis may, at first, be negatively accepted by agents as another performance tracking layer. Every negative emotion may add to the agent’s stress as a false indicator of a failed call. However, sentiment analysis is not created to frustrate or stress agents.&nbsp;&nbsp;In reality, its value lies in surfacing the hidden emotional layer of conversations, the cues that traditional KPIs never capture. Properly implemented, it empowers agents, informs managers, and helps organizations see patterns they would otherwise miss.&nbsp;<h3>Silent Attrition Signals</h3>A customer who sounds calm but carries subtle resignation in their tone, sentiment analysis flags negative sentiment across calls. Management realizes this is not about today’s call but about a slow churn pattern. Without emotional insight, this customer might leave quietly after months of dissatisfaction.&nbsp;<h3>Preventing Escalation Loops</h3>A customer grows increasingly frustrated during a technical walkthrough. Instead of waiting for the inevitable “Let me speak to your supervisor” sentiment, detection triggers real-time prompts: adjust pace, reframe explanations, or offer a pause. With this single adjustment, the loop of repeat calls and escalations is avoided.&nbsp;<h3>Hidden Stress in “Neutral” Calls</h3>Some calls sound uneventful, but agents report high fatigue afterward. Post-call sentiment analysis shows frequent low-level negative signals (confusion or impatience) that don’t spike loud enough to cause escalations but accumulate over time. This insight helps managers redesign scripts to reduce agent load.&nbsp;<h3>Spotting Training Gaps Early</h3>An agent handling complex billing explanations consistently generates low sentiment scores from customers, even though the metrics (AHT, FCR) look fine. The data shows customers understood less than they admitted in real time. Sentiment analysis exposes the training gap faster than surveys or QA sampling ever could.&nbsp;<h3>Cultural and Accent Sensitivity</h3>In multinational centers, non-native speakers sometimes sound abrupt or “cold” unintentionally. Sentiment analysis detects repeated misalignments in perceived tone. Instead of blaming agents or customers, the system highlights where real-time adjustments (like clearer pacing or boosting) can prevent misunderstandings across accents and cultures.&nbsp;<h3>Executive-Level Insights</h3>Beyond individual calls, sentiment trends reveal systemic issues: product features that customers consistently react negatively to or processes that show hidden anxiety (e.g., refund policies). This data shapes strategic decisions, not just agent coaching.&nbsp;<h2 style="line-height:1.38;margin-bottom:0pt;margin-top:0pt;" dir="ltr">The Business Value of Sentiment Analysis</h2>When evaluating the effectiveness of a particular tool or feature, first of all, we bring in its ROI.<ul><li>Retention &amp; Loyalty:&nbsp;Emotions are important whether customers stay or leave. Unaddressed emotions can cost far more than investing in detection tools.</li><li>Operational Efficiency:&nbsp;Reduced escalations, fewer repeat calls, and tighter QA; improved operations eventually lead to better performance and higher revenue.</li><li>Brand Reputation: Customers tell 15 others about negative service. One bad emotional experience spreads. Keeping loyal customers is far better than chasing new leads. </li><li>Employee Retention: Supporting agents with emotional awareness tools lowers burnout, improving performance and reducing recruitment/turnover costs.</li></ul>&nbsp;<h2 style="line-height:1.38;margin-bottom:0pt;margin-top:0pt;" dir="ltr">Why Hecttor’s Approach Is Different</h2>Today, call center SaaS solutions include all kinds of sentiment analysis tools, but most offer post-call analysis, which is no less critical. What makes Hecttor’s sentiment analysis unique is its real-time operation in synergy with other features. When Hecttor’s voice boost, speed adjustment, and noise cancellation. When all features are turned on and only pure speech is heard, the tool can capture any slight changes in the customer’s voice tone and alert agents and supervisors.<ul><li>It operates on voice (not just text), capturing tone, pace, and speech patterns in real time.</li><li>Sentiment tools are integrated into the same tool agents use; there are no extra apps or workflows.</li><li>Alerts are immediate and context-aware; post-call reports give actionable insights rather than raw data.</li><li>Scalable: every agent and every call, without overwhelming supervisors or QA teams.</li></ul>&nbsp;<h2 style="line-height:1.38;margin-bottom:0pt;margin-top:0pt;" dir="ltr">Conclusion: Turning Every Call Into an Opportunity</h2>Sentiment analysis isn’t just a nice-to-have metric. It’s a strategic lever. A call center is one of those environments where competition is tight, customer expectations are high, and margins are under pressure. That’s why the ability to sense emotion, respond in real time, and measure effects can be the difference between growth and stagnation.&nbsp;If you treat sentiment as a noise metric, you’ll miss what customers really feel. But once you treat it as one of the central components of your CX operations, as Hecttor does, you gain clarity, trust, and measurable returns.

Beyond Words: Why Sentiment Analysis Is Reshaping Customer Experience

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Beyond Words: Why Sentiment Analysis Is Reshaping Customer Experience



Traditional metrics miss emotions that drive loyalty and churn. Learn how sentiment analysis reveals hidden insights and improves customer experience.

Why Sentiment Analysis Matters in Contact Centers

Contact center success is measured by metrics such as average handling time (AHT), first-call resolution (FCR), and customer satisfaction (CSAT).

Before the Cognitive Age (AI Era), humans had significant advantages based on their abilities. Today, the gap is smaller than ever, and sometimes they share the same weaknesses. One such aspect we are investigating is speech recognition and comprehension.&nbsp;The Illusion of “Perfect Listening”&nbsp;Call centers invest in high-end headsets, noise cancellation, and a big budget for audio clarity. All good. But clarity alone doesn’t equal understanding. Research shows that even with clean audio, both humans and machines make predictable mistakes. That gap is what business leaders need to solve.<h2 style="line-height:1.38;margin-bottom:6pt;margin-top:18pt;" dir="ltr">The Human-Machine Gap in Speech Recognition</h2>Why do we say that comprehension is a weakness? Isn’t it a basic ability? In fact, comprehension may fail for many reasons, including external noise, poor audio quality, cognitive overload, language barriers, etc. Humans and machines process speech in different ways; while they may both fail at recognizing particular speech patterns, studies consistently show that humans and machines fail in different ways. For example,<a style="text-decoration:none;" target="_blank" rel="noopener noreferrer" href="https://pubmed.ncbi.nlm.nih.gov/21303019/"> one study</a> on phoneme recognition compared human listeners with automatic speech recognition (ASR) systems under various conditions, including speaking rate, pitch variation, effort, dialect, and background noise. Though the study is not new, the major findings are applicable even today.&nbsp;Key findings:<ul><li>Humans outperform machines in noise. The ASR system needed a 15 dB higher signal-to-noise ratio (SNR) to reach the same performance as humans.</li><li>Feature extraction is the weak link. Much of the gap comes from how machines extract features. Even when researchers resynthesized ASR features for humans, listeners still performed better at just a 10 dB advantage.</li><li>Intrinsic variability is punishing. Changes in pitch, speaking rate, or accent increased error rates for both humans and machines, sometimes by up to 120%. But humans were more resilient to these shifts.</li><li>Temporal cues matter. Humans are better at using timing information (like phoneme duration) to decode speech in noisy environments, something current ASR systems miss. Still, in most cases, humans fail in speech comprehension due to prolonged noise or fast speech.&nbsp;</li></ul>&nbsp;For business owners, this matters because the errors your customers and agents notice aren’t just random; they’re predictable and systematic. Machines drop the ball under acoustic stress. Humans slip when cognitive load rises. Both problems cost businesses money.&nbsp;<h2 style="line-height:1.38;margin-bottom:6pt;margin-top:18pt;" dir="ltr">Why People Mishear Even in Quiet Environments</h2>We have all experienced moments when we are present physically but our minds are somewhere else. In constant stress, humans tend to lose focus unconsciously, which directly impacts comprehension. At the same time, the brain is the most powerful “machine” that can even make predictions.&nbsp;<h3 style="line-height:1.38;margin-bottom:4pt;margin-top:16pt;" dir="ltr">Predictive Processing in the Brain</h3>Our brain doesn’t passively wait for every word. It anticipates what comes next using context. MEG/EEG (electroencephalography and magnetoencephalography) studies show that word predictability (from prior context) changes neural signals before the actual word arrives. (<a style="text-decoration:none;" target="_blank" rel="noopener noreferrer" href="https://pubmed.ncbi.nlm.nih.gov/32474080/?utm_source=chatgpt.com">PubMed</a>)&nbsp;&nbsp;For example, “Brain activity reflects the predictability of word sequences in listened continuous speech” (MEG data) found that certain brain regions track the probability of upcoming words based on context, not just the sounds. (<a style="text-decoration:none;" target="_blank" rel="noopener noreferrer" href="https://pubmed.ncbi.nlm.nih.gov/32474080/?utm_source=chatgpt.com">PubMed</a>)<h3 style="line-height:1.38;margin-bottom:4pt;margin-top:16pt;" dir="ltr">Mishearing Despite Good Signal</h3>Temporal processing limits: a PNAS study showed that during fast speech comprehension drops, even though audio is still intelligible, because the auditory cortex can’t follow fast changes (phase-locking). (<a style="text-decoration:none;" target="_blank" rel="noopener noreferrer" href="https://www.pnas.org/doi/full/10.1073/pnas.201400998?utm_source=chatgpt.com">PNAS</a>) The comprehension levels differ based on language proficiency and fluency, but generally, the challenge of understanding fast speech is essentially perpetual, rooted in the brain’s temporal processing limits rather than just linguistic knowledge.<h2 style="line-height:1.38;margin-bottom:6pt;margin-top:18pt;" dir="ltr">How Machines Mishear</h2>When it comes to machines, there are no emotions, no focus problems, or cognitive loads. It’s all about algorithms that succeed in decoding or fail.&nbsp;<h3 style="line-height:1.38;margin-bottom:4pt;margin-top:16pt;" dir="ltr">Error Patterns Under Adverse Conditions</h3>One of the comparative<a style="text-decoration:none;" target="_blank" rel="noopener noreferrer" href="https://pubs.aip.org/asa/jel/article/4/11/115204/3320022"> studies</a> of humans and machines showed a consistent pattern: once speech is distorted by background noise, error rates rise sharply for both.&nbsp;&nbsp;But humans and machines failed differently. Human listeners, even under strain, usually preserve grammaticality, filling in gaps with plausible constructions. Machines, on the other hand, degrade into ungrammatical or nonsensical sequences. For business, that difference matters: a slightly misheard but grammatical human response keeps conversations flowing, while a meaningless machine output risks confusion and mistrust.&nbsp;<h3 style="line-height:1.38;margin-bottom:4pt;margin-top:16pt;" dir="ltr">Accents, Rare Words, and Variability</h3>Accented speech remains another major challenge for humans and machines. One of the techniques for processing accented speech with automatic speech recognition systems is training models with accented speech. This can be a solution if we take one or two accents. But this technique is not scalable and fails when cross-testing accents.&nbsp;<h3 style="line-height:1.38;margin-bottom:4pt;margin-top:16pt;" dir="ltr">Why Machine Mishearing Matters in the Customer Service Domain</h3>It’s tempting for non-industry experts to say, “Why not just use speech-to-text? Problem solved.” But the research tells a different story. Machines don’t simply transcribe; they interpret under stress, and interpretation breaks down in ways that look different from human failure.<ul><li>Noise, accents, and speech variability push error rates up.</li><li>Machines may outperform humans in narrow scenarios, but their mistakes become misleading when scaling.</li><li>Unlike people, they don’t repair errors using context, tone, or shared meaning.</li></ul>That’s why the conversation isn’t about whether machines can listen. It’s about whether they can understand in the way business-critical conversations demand.&nbsp;And this is the gap Hecttor addresses. Instead of treating speech-to-text as a silver bullet, Hecttor recognizes that machines mishear just as humans do and builds systems designed to support human comprehension.<h2 style="line-height:1.38;margin-bottom:6pt;margin-top:18pt;" dir="ltr">Business Implications: Where Misrecognition Hurts the Most</h2>Research on speech recognition in noisy conditions shows that recognition accuracy drops significantly for both humans and machines. In a contact center, that drop isn’t abstract. It shows up as repeated questions, frustrated customers, and agents who lose track of the conversation flow. Each misunderstanding forces both sides to work harder to repair communication.&nbsp;The danger isn’t just about accuracy percentages. It’s about the type of errors. Machines under stress often misrecognize words and produce unnatural speech patterns, while humans stay grammatically consistent even if they miss certain details. At this point, one thing is clear: in real-time conversations, machines are not applicable. Businesses need solutions designed to actively support comprehension through voice speed adjustment, boosting muffled speech, and suppressing background noise so agents can focus on meaning, not decoding speech.<h2 style="line-height:1.38;margin-bottom:6pt;margin-top:18pt;" dir="ltr">The Open Question for Business Leaders</h2>If research shows that comprehension errors are systematic and predictable, then the question isn’t if they’ll happen; it’s how you prepare for them.<ul><li>Do you measure the cost of misheard words in your customer journey?</li><li>Do you design your speech systems around noisy, real-world conversations?&nbsp;</li><li>Do you give your agents tools that reduce cognitive overload, or do you add to it with more dashboards and alerts?</li></ul>These are the questions that define whether speech technology protects or destroys your customer relationships.<h2 style="line-height:1.38;margin-bottom:6pt;margin-top:18pt;" dir="ltr">Where Hecttor Comes In</h2>Both people and machines have blind spots. Humans mishear under cognitive strain. Machines misinterpret under acoustic stress. The contact center is one of the few places where these weaknesses escalate and cost businesses directly.&nbsp;That’s where Hecttor makes the difference. Unlike traditional ASR tools, Hecttor is built to support humans and human comprehension.<ul><li>It reduces cognitive load by slowing down speech to a comfortable speed in real time.</li><li>It adapts to both agent and customer needs instantly, without extra steps.</li><li>It ensures agents can actually hear and respond to customers’ emotions with empathy.</li></ul>For business owners, the takeaway is simple: you don’t need machines that only listen. You need machines that help humans understand.&nbsp;

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Speech Comprehension Errors: How People and Machines Interpret Words

Explore how humans and AI mishear speech, why clarity doesn’t mean comprehension, and how Hecttor bridges the human–machine understanding gap.

Speech Comprehension Errors: Why People and Machines Mishear

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We’ve been comparing humans and machines for decades. 

We are now solving one of the biggest gaps in agent-customer interaction, the comprehension gap, where customers feel they were not heard and understood. The successful implementation of speech adjustment and voice boost technology showed us how the conversations should flow. That’s critical, but it leaves out the other half of the conversation: customers often struggle just as much to understand agents, especially when calls move quickly, accents differ, or background noise intrudes.&nbsp;Hecttor’s new Bidirectional Speech Adjustment solves this gap by improving clarity in both directions of the call. Agents hear customers more clearly. Customers hear agents more clearly. The result? Conversations that flow naturally, without repetition or confusion.<h2 style="line-height:1.38;margin-bottom:6pt;margin-top:18pt;" dir="ltr">The Limitations of One-Way Comprehension</h2>One-way adjustment definitely makes a huge difference, but it only addresses the agent’s pinpoint. Consider these real-world challenges:&nbsp;<ul><li>Agents struggle with fast talkers or accented speech</li><li>Customers misunderstand agents speaking at contact-center speed</li><li>Both sides repeat themselves, lengthening calls and lowering satisfaction.</li></ul>&nbsp;With only one side optimized, the cycle of misheard words and clarifications continues in one way. One call may last longer than it could have. Businesses see the impact in longer Average Handle Time (AHT) and lower First-Call Resolution (FCR).<h2 style="line-height:1.38;margin-bottom:6pt;margin-top:18pt;" dir="ltr">Customer’s Challenge: When Agents Speak Too Fast</h2>Customers regularly express frustration with agents speaking rapidly or unclearly. One of the most common pieces of feedback we hear from customers:&nbsp;<blockquote>“It’s just frustrating when the person thinks that they know English well enough… they just sound like gibberish, and I still can’t understand half of what they tell me.”&nbsp;</blockquote>&nbsp;Traditional strategies to slow down agent speech, like listening to recorded calls, supervisor coaching, deliberate pauses, or personal breathing techniques, offer limited results and require constant self-regulation from agents. Is it possible to control everything in real time? Hardly.&nbsp;&nbsp;During agent training, supervisors constantly repeat the same idea as a mantra: “Modulate your speech… speak slowly and clearly,” but this relies on manual effort and attention that can easily fail during long, high-pressure shifts.&nbsp;Why those approaches fall short:&nbsp;<ul><li>Inconsistency:&nbsp;Without real-time guidance, agents revert to natural fast speech and it is done unconsciously.&nbsp;</li><li>Coach-dependent:&nbsp;Reliance on supervisors or recordings delays improvement. Often feedback is outdated, which doesn’t create urgency for agents.&nbsp;</li><li>Stress-sensitive:&nbsp;Under pressure, agents speed up instinctively, increasing miscommunication.</li></ul><h2 style="line-height:1.38;margin-bottom:6pt;margin-top:18pt;" dir="ltr">Bidirectional Clarity: Inside the Technology</h2>Hecttor now processes audio on both inbound and outbound streams in real time:<ul><li>Customer → Agent: Speech is slowed/adjusted, boosted, and cleaned of noise for better agent comprehension.</li><li>Agent → Customer:&nbsp;Speech is adjusted to a clearer, steadier pace, with consistent volume and reduced background interference.</li></ul>Because Hecttor works on audio frequencies, not language-specific models, the technology adapts seamlessly to any language, accent, or speaking style.<h2 style="line-height:1.38;margin-bottom:6pt;margin-top:18pt;" dir="ltr">Real-World Scenarios Where Two-Way Matters</h2><ul><li>Non-native speakers:&nbsp;Customers calling from abroad hear agents at a steady, clear pace, while agents hear unfamiliar accents with boosted intelligibility.</li><li>Fast-paced native speech: Rapid talkers are slowed for agents; equally, customers receive steadier agent responses, and they won’t need to ask to repeat.</li><li>Technical explanations:&nbsp;When calls involve complex products or troubleshooting steps, clarity in both directions ensures nothing gets lost in translation.</li><li>Accessibility:&nbsp;Hearing-impaired customers are the most vulnerable when dealing with call centers and fast-speaking agents. Enhanced clarity and stability is not a funky perk but a life-changing necessity.&nbsp;</li></ul><h2 style="line-height:1.38;margin-bottom:6pt;margin-top:18pt;" dir="ltr">The Human Impact</h2>Bidirectional comprehension is more than an audio feature. It’s a human feature.<ul><li>For agents: No more pretending they understood, no more strained listening. They engage with confidence.</li><li>For customers: A smoother, more respectful experience where they feel heard and understood.</li><li>For both: Less cognitive load, less frustration, and more authentic conversations.</li></ul><h2 style="line-height:1.38;margin-bottom:6pt;margin-top:18pt;" dir="ltr">The Benefits for Agents, Customers, and Businesses</h2><ul><li>Higher FCR: Clearer conversations reduce back-and-forth and eliminate misunderstandings.</li><li>Lower AHT:&nbsp;Calls move faster without unnecessary repetition.</li><li>Better CSAT &amp; NPS: Customers walk away feeling the service was effortless and professional.</li><li>Reduced Agent Burnout: Clarity means less stress and fatigue, improving retention.</li></ul><h2 style="line-height:1.38;margin-bottom:6pt;margin-top:18pt;" dir="ltr">Beyond Clarity: Strategic CX Implications</h2>Contact centers often struggle with being seen as cost centers rather than value drivers. With Bidirectional Speech Adjustment, every call becomes an opportunity to improve:<ul><li>Operational efficiency through faster resolution.</li><li>Customer experience through comprehension.</li><li>Brand reputation through consistent, clear interactions.</li></ul>&nbsp;Hecttor’s bidirectional speed adjustment model is more than just a technology upgrade. It’s a shift in how conversations work.<h2 style="line-height:1.38;margin-bottom:6pt;margin-top:18pt;" dir="ltr">Conclusion: See It in Action</h2>With Bidirectional Speech Adjustment, comprehension finally flows both ways and naturally. Agents and customers meet at the same pace, with the same clarity, in real time.<h3 style="line-height:1.38;margin-bottom:4pt;margin-top:16pt;" dir="ltr">Ready to experience it? Contact our team at Hecttor for a demo and see how two-way comprehension transforms your calls.</h3>

Announcing Bidirectional Speech Adjustment feature

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Announcing Bidirectional Speech Adjustment

When Comprehension Flows Both Ways: Announcing Bidirectional Speech Adjustment

Hecttor’s new Bidirectional Speech Adjustment has come to fix comprehension for both agents and customers, boosting clarity, speed, and customer satisfaction.

Two-Way Clarity: Hecttor’s Bidirectional Speech Tech

Until now, most speech-processing technologies have focused on helping agents understand customers. 

Voice Boost Tech vs. Better Headsets: Which Solves Clarity Better?

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Discover the best call center headsets with noise cancellation and clarity features, and learn how Hecttor’s Voice Boost technology enhances speech intelligibility.

Best Call Center Headsets vs. Voice Boost Technology

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The success of each call in contact centers depends on many factors, but everything starts with audio clarity. 

There’s a reason companies can’t quit Net Promoter Score (NPS).&nbsp;“Would you recommend us?” is one simple question that generates a “sleek” number and gets reported to boards, tied to executive bonuses, and used as the measure of customer loyalty.&nbsp;<a style="text-decoration:none;" target="_blank" rel="noopener noreferrer" href="https://hecttor.ai/blog/10-contact-center-metrics-that-matter-most">NPS</a> is designed to measure customer loyalty and show whether the customer would continue to purchase from the business.&nbsp;&nbsp;<blockquote>This KPI is easy to understand but the number is so simple that it hardly conveys any additional information about the call.</blockquote>&nbsp;And it’s everywhere, especially in contact centers. Every interaction seems to hinge on NPS, as though that one number reflects the full spectrum of what happened in the call. But as many managers and agents will tell you,<a style="text-decoration:none;" target="_blank" rel="noopener noreferrer" href="https://cambridgeservicealliance.eng.cam.ac.uk/system/files/documents/2016OctoberPaper_FallacyoftheNetPromoterScore.pdf"> NPS often</a> oversimplifies and hides deeper flaws. Imagine you are a customer taking part in the survey. You are in a good mood, rating high; you are frustrated (and not even because of the agent or the call), and you will rate lower. Here is another inconsistency. Businesses can have high NPS scores but high churn rates, which is a fundamental error because NPS is supposed to show loyalty.&nbsp;&nbsp;<blockquote>While NPS is one of the major CX metrics, it is subjective and does not always reveal the true picture of contact center performance.&nbsp;</blockquote><h2 style="line-height:1.38;margin-bottom:6pt;margin-top:18pt;" dir="ltr">The Disconnect Between NPS and Contact Center Reality</h2>If you’ve ever sat on the contact center frontline, you know customers rarely call because they are happy or just to say “thanks.” By the time they reach an agent, their experience has already been shaped by product issues, shipping delays, billing errors, or unclear policies.NPS is not an open-ended question but a yes or no or 1 to 10 score that tells not much about the quality of the experience.&nbsp;<blockquote>When the call is ended, whether customers are fully satisfied or still frustrated, they will rate the call with emotions. NPS ends up as a grade on the agent.</blockquote>&nbsp;The issue is systemic: agents have minimal influence over these problems but take the blame anyway. Customer in a bad mood? Bad NPS rate. NPS doesn’t separate a poor process from a helpful agent; it just scores the interaction. And finally, through frustration, agents need to get higher scores ("Please give me a 9 or 10!") rather than focus on genuine problem-solving.&nbsp;&nbsp;<blockquote>NPS could be more effective and informative if each rate was connected to the customer interaction data, but it is about tons of material and enormous resources to process.&nbsp;</blockquote><h3 style="line-height:1.38;margin-bottom:4pt;margin-top:16pt;" dir="ltr">Why NPS Data Lacks Context</h3>We are not trying to disqualify NPS as a KPI but the obsession with NPS scores has created a culture of measurement without understanding. Agents get data points but lose the nuance of human interaction.&nbsp;For example, a score of 6 will tell the manager that a customer wasn't satisfied. But it doesn't tell<ul><li>Was the issue with the product or the service?</li><li>Did language barriers contribute to frustration?</li><li>What friction points drove the low score?&nbsp;</li></ul>Without having the answers to these questions, organizations can make incorrect or not timely decisions. They might conclude that agents need more training when the real issue is more complex. They might blame attitude when the problem is the lack of comprehension.&nbsp;<blockquote>NPS becomes a diagnostic tool without diagnostic capabilities—measuring symptoms while the root causes remain invisible.</blockquote><h2 style="line-height:1.38;margin-bottom:6pt;margin-top:18pt;" dir="ltr">The Real Cost of Misunderstanding</h2>That disconnect has real-world business costs.&nbsp;Like most contact center performance metrics, NPS captures only the outcome without digging into the reasons for the friction, which is basic comprehension.&nbsp;&nbsp;When an agent can’t fully understand the caller because of a poor connection or they’re a non-native speaker hearing fast native speech, a single misunderstanding can add minutes to every call. Longer calls mean higher Average Handling Time (AHT), lower First Call Resolution (FCR), and more frustrated customers.&nbsp;And it scales. Repeating information multiple times burns out agents and annoys customers. Even small cracks like this may affect NPS. But when analyzing scores, they won’t see what happened and will not definitely know how to fix it. This issue scales when you look at the issue holistically: over 10 million non-native-speaking agents in global contact centers are experiencing comprehension issues and silently driving down scores.<h2 style="line-height:1.38;margin-bottom:6pt;margin-top:18pt;" dir="ltr">Traditional NPS and AI Tools</h2>Today’s contact center AI stack is mostly retrospective. They respond to NPS pressure with sophisticated analytics: sentiment analysis, call transcriptions, keyword spotting, and post-call scoring. All these tools are perfect for telling you what went wrong during the call.But they don’t help agents at the moment when understanding is most critical. They don’t even explain why the customer and agent had comprehension issues.&nbsp;&nbsp;<blockquote>Without real-time tools to improve comprehension during the call, agents remain at a disadvantage, and NPS continues to reflect those struggles.</blockquote><h2 style="line-height:1.38;margin-bottom:6pt;margin-top:18pt;" dir="ltr">Language Barriers: The Most Overlooked Factor in NPS</h2>We gave you hints about communication issues that affect NPS, but here is the explicit explanation.&nbsp;&nbsp;Aren’t working hours about listening fatigue that occurs when they experience mental exhaustion due to sustained auditory effort? In simple words, in call centers, where agents spend 85–95% of their work hours listening to customers, fast speech accelerates fatigue.&nbsp;Add customers’ emotions and frustration to fast speech and you get a massive comprehension gap.&nbsp;&nbsp;<ul><li>Studies show that non-native listeners require up to 40% more cognitive effort to process fast speech compared to native listeners (<a style="text-decoration:none;" target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1016/j.bandl.2014.10.003">Schmidtke et al., 2014</a>).</li><li>After prolonged exposure to rapid speech, cognitive efficiency drops, leading to slower responses, increased customer frustration, and lower call resolution efficiency.</li></ul>&nbsp;And all these are the most silent drivers of poor NPS.&nbsp;Now let’s look at<a style="text-decoration:none;" target="_blank" rel="noopener noreferrer" href="https://hecttor.ai/blog/global-bpo-market-2025"> the geography of the global BPO contact centers</a>: India, the Philippines, and Latin America. Despite the level of language proficiency, there is a constant comprehension gap when dealing with native customers of the US, UK, and Australia.&nbsp;&nbsp;Fast-paced native speech can lead to misunderstanding, repetition, and delays, all before agents can even attempt to resolve the issue. The customer only hears the lag; they don’t see the language challenge. NPS drops, call times rise, and agent morale falls. NPS captures the frustration but not the cause.&nbsp;&nbsp;These language-based friction points are mostly invisible to traditional KPI measurement systems unless there is a person who has experienced comprehension issues and is now a decision-maker.&nbsp;<h2 style="line-height:1.38;margin-bottom:6pt;margin-top:18pt;" dir="ltr">Hecttor AI: Real-Time Speech Speed Adjustment</h2>So we have NPS as one of the major KPIs that suffers from an “unknown” reason. Then we have the core problem revealed. The last component is the solution, a real-time solution for real-time problems.&nbsp;&nbsp;Hecttor doesn’t analyze performance post-factum, and it doesn’t provide training for future communication. This voice AI technology works during the call, slowing down native speech in real-time with no latency, and helps agents understand every word without asking to repeat. We give agents the right to end the call successfully and have real scores.&nbsp;&nbsp;The result isn’t just improved NPS scores but improved reality. Agents perform better because they're enabled to perform better. Customers feel heard because they actually are heard. The metrics improve because the underlying experience improves.<h2 style="line-height:1.38;margin-bottom:6pt;margin-top:18pt;" dir="ltr">What Contact Centers Really Need</h2>We don’t say NPS is useless and we don’t suggest discarding it. But we do need to stop weaponizing it. The metric has value when used appropriately. We are calling for a more nuanced approach to customer experience measurement, which also includes agent experience and recognizes the human factors behind the numbers.&nbsp;A truly effective customer experience measurement system could be<ul><li>Separate controllable from uncontrollable factors. An agent shouldn't be penalized for poor call quality or fast speech.</li><li>Provide tools that truly affect performance in real-time. If speech clarity is causing problems, solve it during the call, not after.</li><li>Measure effort alongside outcome. Customer Effort Score (CES) provides more actionable insights than NPS alone.</li><li>Create feedback loops that actually close. Instead of just reporting problems, make sure the problems are solved.</li></ul><h2 style="line-height:1.38;margin-bottom:6pt;margin-top:18pt;" dir="ltr">Conclusion: Fixing the Friction with Hecttor&nbsp;</h2>Contact center performance metrics are complex. They gather enormous data, and they help build training and simulations but none of them will have an immediate real-time impact on the ongoing call.&nbsp;&nbsp;Hecttor is not a magic wand to fix NPS and not a magic pill to give superpowers. We offer to fix the friction caused by language barriers and misunderstandings. We help call centers solve real problems in real-time, and you’ll see those scores rise naturally.<h3 style="line-height:1.38;margin-bottom:4pt;margin-top:16pt;" dir="ltr">The scores follow the experience, not the other way around.</h3>

Net Promoter Score: The Hidden Pain Point of Contact Centers

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NPS is everywhere in contact centers but often hides the real issues. See why it fails and and how real-time voice tech like Hecttor fixes what NPS can’t measure.

Why NPS Fails Contact Centers And What to Do About It

Some KPIs, including NPS, are critical for contact centers to track, even if the tracking and measuring are not 100% accurate. 

The competition is rough, and the stakes are high. That’s why the demand for speed, clarity, and efficiency is unprecedented.&nbsp;&nbsp;Customer service, as the key touchpoint in CX, now gets more attention from businesses than ever. Organizations are pouring millions into all kinds of post-call analytics platforms, hoping to get granular data on customer experience and finally see root-cause issues. These tools are valuable, but they’re inherently backward-looking. They diagnose the past.&nbsp;But what about the cognitive bottlenecks that happen during the conversation? What about the seconds when an agent mishears a customer’s concern, or struggles with a fast speaker, or fails to defuse frustration because comprehension lags?&nbsp;This gap may not even be reported in post-analytics tools and, subsequently, may not be trained (if there is any training to overcome cognitive load at all). This is the gap real-time processing fills, and it’s time to understand the difference not just at a surface level, but at the level of operational impact, cognitive science, and ROI.<h2 style="line-height:1.38;margin-bottom:6pt;margin-top:18pt;" dir="ltr">The Strategic Value of Post-Call Analytics</h2>Modern post-call analytics platforms are on an AI engine, having wider and smarter implementations. These tools can gather data non-stop, store and analyze it, and give insights into what needs attention.&nbsp;<ul><li>Collecting customer insights: Post-call analytics tools are the only way to track customer experience and discover the pain points and needs. The data is then translated into CSAT drivers and churn risks.</li><li>Compliance monitoring: Post-call analytics tools help automatically identify non-compliant keywords and phrases and flag them for future interactions. This feature ensures agents know and adhere to the regulatory protocols.&nbsp;</li><li>Agent evaluation: Quality checks and structured feedback based on agent performance are one of the most effective ways for agent coaching. The information gathered is often used in simulations and situational training.&nbsp;</li><li>Collecting historical datasets: Post-call analytics tools with AI collect every single interaction, which is then used to model strategies for customer journey and risk mitigation.&nbsp;</li></ul>&nbsp;In essence, post-call analytics platforms are ideal for decision-makers for future improvements. They inform workforce planning, training strategy, and CX roadmaps.<h2 style="line-height:1.38;margin-bottom:6pt;margin-top:18pt;" dir="ltr">The Blind Spots of Post-Call Analytics</h2>Let’s not forget that 99% of interactions in customer service are online, except for emails, and these post-call analytics systems miss critical aspects of live interaction:&nbsp;<ul><li>Delayed feedback loop: Agents get coached days or weeks after mistakes occur, when they may have even forgotten the real issue and challenges they faced.&nbsp;</li><li>No in-the-moment support: Real-time comprehension breakdowns can’t be prevented with post-call analytics tools. The issues like poor connection, comprehension, customers’ fast speech, and cognitive load cannot be supported by any KPI tracking system, sentiment analysis, or analytics.&nbsp;</li><li>Missed micro-events: Frustration spikes, pauses, and misunderstanding cues may not be detected through keyword triggers. Customers’ emotions often cause distress, which leads to lower performance. When analyzing the results, we may not see the real cause of poor performance.&nbsp;</li><li>Agent disempowerment: Supervisors see data, but agents face complexity without live assistance.&nbsp;</li></ul>&nbsp;Cognitive load happens in environments where urgency is “perpetual,” no matter how loud this word may seem. And contact centers are all about urgency. Businesses focus on average handling time because they know the longer the customer is on the line, the more the business pays.&nbsp;<h2 style="line-height:1.38;margin-bottom:6pt;margin-top:18pt;" dir="ltr">The Case for Real-Time Processing</h2>Real-time processing tools are a totally different story for contact centers. They are created to work synchronously with agents for agents. The “for agents” is actually one of the most critical components, because most real-time tools are still working to track agents instead of helping.&nbsp;&nbsp;But let’s talk about real-time tools in general and how they complement situations where reaction time defines the outcome.&nbsp;<ul><li>Prevention Over Important Diagnosis: Real-time tools allow for live error correction, such as clarifying misunderstandings or modulating speech pacing to prevent confusion before it snowballs into escalation.</li><li>In-Call Cognitive Support: These solutions act as a live assistive layer, helping agents manage difficult audio, language, accent, fast speech, or customer frustration as it happens, reducing mental strain and performance dips.</li><li>Operational Efficiency Gains: By smoothing communication and reducing repetition, real-time tools impact AHT, FCR, and customer satisfaction immediately, not after training cycles or coaching reviews.&nbsp;</li></ul>&nbsp;This is the most attractive factor that persuades businesses to go for real-time tools integration. The challenge here is to find solutions that can have a significant impact on KPIs and not just vague promises.&nbsp;&nbsp;<ul><li>Empowered Agents, Not Just Informed Managers: Where post-call analytics serve supervisors and QA teams, real-time systems are FOR agents directly, improving their performance at the moment of need, not retroactively.</li><li>Latency-Tuned for Human Conversation: The best real-time systems operate within a sub-200-ms response window, ensuring interventions are minimal and don't affect call quality. This preserves flow, empathy, and conversational naturalness.</li></ul>&nbsp;These systems shift the whole idea and role of contact center technology. While you may survive without post-call analytics (which is not advised), real-time solutions are crucial to give results here and now.<h2 style="line-height:1.38;margin-bottom:6pt;margin-top:18pt;" dir="ltr">Limitations of Real-Time Systems</h2>Deploying real-time systems is complex and depends on many factors, including the device OS, connection quality, and even the headset type and quality.&nbsp;&nbsp;<ul><li>Latency ceiling: Real-time speech enhancement technology must occur in under ~200 ms to preserve natural dialog flow.&nbsp;</li><li>Edge-device performance: Most call centers don’t have GPU-equipped machines, so real-time models must be optimized for CPU-only inference, which again may be a challenge and affect real-time performance.&nbsp;</li><li>Fear of integration: Many contact centers are overwhelmed by tools, and every new tool, be it real-time or post-call processing, is a stress for an agent.</li></ul>&nbsp;Hecttor’s approach solves this through on-device, signal-level adaptation that never transmits audio externally. Unlike cloud-first models, our voice AI preserves data privacy and ensures sub-200ms response time.&nbsp;<h2 style="line-height:1.38;margin-bottom:6pt;margin-top:18pt;" dir="ltr">Post-Call Analytics vs. Real-Time Processing: A Quick Comparison</h2><div style="margin-left:0pt;" dir="ltr" align="left"><figure class="table"><table class="ck-table-resized" style="border-collapse:collapse;border-style:none;"><colgroup><col style="width:33.33%;" width="134"><col style="width:33.33%;" width="238"><col style="width:33.34%;" width="252"></colgroup><tbody><tr style="height:25.75pt;"><td style="overflow-wrap:break-word;overflow:hidden;padding:5pt;vertical-align:top;">Dimension</td><td style="overflow-wrap:break-word;overflow:hidden;padding:5pt;vertical-align:top;">Post-Call Analytics</td><td style="overflow-wrap:break-word;overflow:hidden;padding:5pt;vertical-align:top;">Real-Time Processing</td></tr><tr style="height:25.75pt;"><td style="overflow-wrap:break-word;overflow:hidden;padding:5pt;vertical-align:top;">Timing</td><td style="overflow-wrap:break-word;overflow:hidden;padding:5pt;vertical-align:top;">After the call is completed</td><td style="overflow-wrap:break-word;overflow:hidden;padding:5pt;vertical-align:top;">During the call — live and dynamic</td></tr><tr style="height:25.75pt;"><td style="overflow-wrap:break-word;overflow:hidden;padding:5pt;vertical-align:top;">Primary User</td><td style="overflow-wrap:break-word;overflow:hidden;padding:5pt;vertical-align:top;">Supervisors, QA, and managers</td><td style="overflow-wrap:break-word;overflow:hidden;padding:5pt;vertical-align:top;">Agents on the front line</td></tr><tr style="height:39.25pt;"><td style="overflow-wrap:break-word;overflow:hidden;padding:5pt;vertical-align:top;">Purpose</td><td style="overflow-wrap:break-word;overflow:hidden;padding:5pt;vertical-align:top;">Performance review, compliance, trend analysis</td><td style="overflow-wrap:break-word;overflow:hidden;padding:5pt;vertical-align:top;">In-call comprehension, responsiveness, prevention</td></tr><tr style="height:25.75pt;"><td style="overflow-wrap:break-word;overflow:hidden;padding:5pt;vertical-align:top;">Support Model</td><td style="overflow-wrap:break-word;overflow:hidden;padding:5pt;vertical-align:top;">Observational &amp; corrective</td><td style="overflow-wrap:break-word;overflow:hidden;padding:5pt;vertical-align:top;">Assistive &amp; preventative</td></tr><tr style="height:39.25pt;"><td style="overflow-wrap:break-word;overflow:hidden;padding:5pt;vertical-align:top;">Agent Benefit</td><td style="overflow-wrap:break-word;overflow:hidden;padding:5pt;vertical-align:top;">Indirect (delayed coaching)</td><td style="overflow-wrap:break-word;overflow:hidden;padding:5pt;vertical-align:top;">Direct (real-time clarity, reduced strain)</td></tr><tr style="height:39.25pt;"><td style="overflow-wrap:break-word;overflow:hidden;padding:5pt;vertical-align:top;">Latency Sensitivity</td><td style="overflow-wrap:break-word;overflow:hidden;padding:5pt;vertical-align:top;">Not time-sensitive</td><td style="overflow-wrap:break-word;overflow:hidden;padding:5pt;vertical-align:top;">Sub-200ms required for conversational flow</td></tr><tr style="height:25.75pt;"><td style="overflow-wrap:break-word;overflow:hidden;padding:5pt;vertical-align:top;">Impact on KPIs</td><td style="overflow-wrap:break-word;overflow:hidden;padding:5pt;vertical-align:top;">Long-term improvements</td><td style="overflow-wrap:break-word;overflow:hidden;padding:5pt;vertical-align:top;">Immediate AHT, FCR, CSAT impact</td></tr><tr style="height:39.25pt;"><td style="overflow-wrap:break-word;overflow:hidden;padding:5pt;vertical-align:top;">Cognitive Load Relief</td><td style="overflow-wrap:break-word;overflow:hidden;padding:5pt;vertical-align:top;">No</td><td style="overflow-wrap:break-word;overflow:hidden;padding:5pt;vertical-align:top;">Yes — reduces fatigue during the call</td></tr><tr style="height:39.25pt;"><td style="overflow-wrap:break-word;overflow:hidden;padding:5pt;vertical-align:top;">Typical Output</td><td style="overflow-wrap:break-word;overflow:hidden;padding:5pt;vertical-align:top;">Dashboards, scorecards, transcripts</td><td style="overflow-wrap:break-word;overflow:hidden;padding:5pt;vertical-align:top;">Dynamic speech adaptation, voice enhancements</td></tr><tr style="height:25.75pt;"><td style="overflow-wrap:break-word;overflow:hidden;padding:5pt;vertical-align:top;">System Behavior</td><td style="overflow-wrap:break-word;overflow:hidden;padding:5pt;vertical-align:top;">Retrospective (what happened?)</td><td style="overflow-wrap:break-word;overflow:hidden;padding:5pt;vertical-align:top;">Active (what's happening now?)</td></tr></tbody></table></figure></div>&nbsp;<h2 style="line-height:1.38;margin-bottom:6pt;margin-top:18pt;" dir="ltr">Why Hecttor Didn’t Build a Post-Call Tool</h2>The market is full of post-call systems, but the core issue we have discovered cannot be solved by any of them. Speech comprehension was still there, causing&nbsp;&nbsp;<ul><li>Misunderstood queries</li><li>Repeated questions (“Can you say that again?”)</li><li>Agent hesitations</li><li>Longer call durations</li><li>Unintended escalations</li></ul>&nbsp;We designed Hecttor from day one to improve live comprehension, not after-the-fact evaluation. By helping agents understand speech as it happens, Hecttor improves productivity without adding dashboards or post-call overhead.<h2 style="line-height:1.38;margin-bottom:6pt;margin-top:18pt;" dir="ltr">From Analytics to Active Assistance</h2>Exploring both post-call and real-time processing solutions, one thing is clear: both are critical for a contact center’s efficiency. Use real-time tools to have an immediate impact on performance and monitor that performance with post-call analytics to understand how your new solution impacted your KPIs. Separate them, and you will again have customer experience suffering.

Why Real-Time Processing Beats Post-Call Analytics

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Post-call analytics help you review the past. Real-time processing changes what happens in the moment. This article breaks down the differences and why both matter.

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Why Real-Time Processing Beats Post-Call Analytics

Let’s slow down the speech and speed up your service