Speech analytics is an automated process of converting human speech into organized text and then analyzing its content to identify valuable customer trends, sentiment, and important patterns. Call centers rely on speech analytics to phrase thousands of conversations daily. Without the technology, call centers are hard to improve customer experience (CX), monitor agent performance, and make data-driven decisions.
Want to learn more valuable information about speech analytics? This complete guide uncovers its definition, key benefits, work mechanism, the difference between related technologies, and real-life use cases. It also shares several best speech analytics solutions for call centers.
What is Speech Analytics
Definition
Speech analytics refers to an AI-powered technology that can automatically understand, process, and analyze human speech. It uses AI technologies, like automatic speech recognition (ASR), natural language processing (NLP), to convert spoken language into text first, and then extract insights related to customer preferences, sentiment, trends, pain points, and more.
The technology is commonly used for contact or call centers. Call center speech analytics software can analyze 100% of customer interactions around the clock, making your team more proactive and having deep understanding of customer interactions.
Benefits of Speech Analytics
Speech analytics delivers measurable business value by transforming raw voice data into actionable intelligence. Here are the key benefits:
Making Data-Driven Decisions: Speech analytics can analyze 100% of customer calls and extract valuable insights, which lets your team make clear, data-driven decisions instead of guessing. This directly boosts long-term operational success.
Automated Quality Assurance: Automates tasks like call scoring and after-call work using standardized criteria, ensuring fair evaluation of all customer service agents and reducing the supervisors’ manual reviewing time. These free agents are to focus on team development and personalized service.
Cross-Departmental Value: Shares insights beyond customer service team: marketing teams use customer feedback to refine messaging, product teams address pain points (e.g., “faulty ZX model screens”), and sales identify upsell opportunities (e.g., “callers asking about premium plans”).
Voice Analytics vs Speech Analytics
While both voice analytics and speech analytics are often used interchangeably, they serve different analytical purposes.
Speech analytics focuses on what is said: the actual words, phrases, and semantic meaning of the conversation. It works by converting the voice into text and analyzing the keywords, sentiment, intents, preferences, and more.
Voice analytics, by contrast, primarily analyzes how it is said: the speaker’s vocal characteristics such as tone, pitch, volume, pace, and pauses. One of the most significant differences is that it can detect a customer’s emotion (e.g., frustration or happiness) through the tone.
Feature | Speech analytics | Voice analytics |
Data type used | Structured text (via ASR transcription) | Audio/acoustic (tone, pitch, pace) |
Best for | Customer compliance, root cause analysis | Emotion and stress detection |
Call center value | CX, compliance, quality assurance | Emotional analysis |
Real-Time vs Post-Call Speech Analytics for Call Center
There are two models of speech analytics: real-time and post-call, and each serves different operational goals in call centers.
What is Real Time Speech Analytics
Real-time speech analytics processes the audio as the conversation happens, which analyzes content and emotion with minimal latency (often seconds). The biggest advantage is immediacy, delivering real-time sentiment analysis, instant alerts, and on-screen prompts to empower agents.
What is Post Call Speech Analytics
Post-call speech analytics mainly analyze recorded calls after the conversation ends, which conducts deeper analysis of content, sentiment, and long-term trends. It offers comprehensive insights, like negative feedback about a product feature (e.g., “confusing online order tracking”) or long-term agent performance monitoring (e.g., “improve overall CSAT up to 20%”).
Feature | Real-Time Speech Analytics | Post-Call Speech Analytics |
Timing | During the call | After the call |
Primary use case | Immediate intervention (de-escalation, compliance alerts) | Strategic insights (trend analysis, process improvement) |
Analysis depth | Lower (Speed-optimized) | Higher (Data-intensive) |
How Does Speech Analytics Work
This section explores how speech analytics work from the four key aspects.
Speech-to-Text (ASR) Layer
As discussed above, speech analytics software converts spoken human words into written text first-this is the role of Automatic Speech Recognition (ASR). Modern ASR systems, such as those powering Siri and Alexa, use deep neural networks to handle accents, background noise, and conversational speech, achieving human-like accuracy for clear audio. Ultimately, it serves as the foundation for all further analysis.
Natural Language Processing and Sentiment Analysis
After transcribing, Natural Language Processing (NLP) parses the text to extract its meaning. It identifies keywords (e.g., “refund,” “product issue”) to understand the customer’s actual intent. Sentiment Analysis is a subset of NLP, which analyzes language cues and categorizes them (e.g., “this is frustrating” = negative). This combination transforms raw text into actionable insights about customer needs.
Machine Learning Models and Training Data
Machine Learning (ML) improves the accuracy and adaptability of speech analytics. ML models are trained on large datasets of call recordings to improve accuracy in customer intent detection and emotion recognition over time. For example, if a new customer complaint emerges (e.g., “delayed shipping for holiday orders”), the model updates to flag it automatically.
Integration with Call Center Systems
To maximize its benefits, the speech analytics system is often integrated with other call center software, such as CRM, IVR, HelpDesk system, and QA software. These integrations enable automated workflows, sentiment analysis, agent performance tracking, and actionable dashboards, directly impacting call center efficiency and decision-making.
Speech Analytics Call Center Explained
What is Call Center Speech Analytics
Call center speech analytics, sometimes called contact center speech analytics, is a specialized application tailored to the needs of call/contact centers. It automatically analyzes 100% of customer calls and digital interactions (like chats) to give insights into customer experience, agent performance, patterns, and compliance issues.
Compared to general speech analytics tools, it prioritizes call/contact center-specific use cases, like reducing repeat inquiries, scaling quality assurance, and meeting industry regulations, and it integrates with other call center solutions.
Why Speech Analytics is Important for Call Center
1. Gain Actionable Insights from Conversations
“The voice of the customer is the most important voice in your business,” as marketing experts Abbie Griffin and John R. Hauser said. Speech analytics understands customer voice and analyzes them to deliver actionable insights into customer needs, preferences, and unmet expectations.
2. Improve Customer Experience and Satisfaction
Customer experience (CX) has a significant impact on customer loyalty. 71% of customers will switch to other brands after just one bad experience, according to Yotpo’s research. Speech analytics help call centers avoid this by identifying CX pain points, compliance issues, and emotional triggers in real time.
3. Boost Agent Performance and Efficiency
Agents are the backbone of any call center, but manual QA impacts agent efficiency and performance. Speech analytics solves this by reviewing 100% of calls, providing consistent feedback, targeted training data, and real-time guidance to agents.
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Call Center Speech Analytics Use Cases
Speech analytics can be used in multiple scenarios of a call center. Below are real-world use cases that demonstrate how this technology empowers teams to solve problems and drive measurable performance improvements.
Use Cases | Core Business Value | Industry Application |
Quality Assurance (QA) Automation | Reduce manual QA workload and ensure consistent service standards across agents. | All industries |
Customer Sentiment & Churn Prevention | Identify customer emotions, like frustration and confusion, and proactively address negative experiences. | E-commerce, retail, finance |
Agent Coaching & Performance Optimization | Highlight best practices and common mistakes for targeted training and higher agent productivity | E-commerce, logistics |
Sales & Conversion Optimization | Identify buying signals and analyze top-performing calls to uncover best scripts. | Retail, insurance |
Compliance Monitoring | Monitor calls and trigger real-time alerts for non-compliant language. | Finance, healthcare |
How to Choose the Best Speech Analytics Software
Choosing the best speech analytics software isn’t just a purchasing decision but a critical strategy that impacts your team’s ROI, operational efficiency, and customer experience. The following key questions can help you narrow the choice:
- Does it support real-time and post-call analytics?
- How accurate is its ASR and sentiment analysis?
- Can it integrate with your existing call center tools?
- Does it provide actionable dashboards, not just data?
Best Speech Analytics Software for Call Center
1.CallMiner
What is it: CallMiner is a leading AI-powered deep speech and text analytics tool that analyzes customer interactions across voice calls, chat, and digital channels. It focuses on advanced sentiment tracking and root cause analysis, especially suitable for scaling QA and CX programs.
Standout Features:
- Omnichannel conversation analytics
- Advanced ASR, sentiment, intent, and emotion detection
- Real-time and post-call automation
- Automated redaction for compliance
Benefits for Call Center:
- Analyze 100% of voice interactions for compliance and root-cause insights.
- Identify inefficiencies to reduce handle time and improve CSAT.
2. Verint
What it is: Verint is a comprehensive customer engagement platform that offers advanced speech analysis capabilities, seamlessly integrated with workforce optimization tools. The platform excels in robust risk detection and extensive multilingual support, making it a standout choice in financial services, healthcare, and regulated environments.
Standout Features:
- Keyword spotting and topic extraction
- Real-time compliance alerting
- Multilingual support
- Integrations with WFM and engagement tools
Benefits for Call Center:
- Reduce compliance risks and avoid regulatory penalties.
- Deliver seamless service for global client bases.
3. Balto
What it is: Balto offers a real-time speech analytics solution that aligns with agent guidance, specifically engineered to support live conversations in call centers. It empowers agents to address customer needs promptly and provides automatic QA scoring to ensure consistent service quality.
Standout Features:
- Real-time agent assistance
- Proactive QA and compliance tracking
- Embedded coaching workflows
- Seamless integration with leading CCaaS platforms
Benefits for Call Center:
- Help agents respond effectively and stay compliant during live calls.
- Improve overall customer experience by ensuring high-quality interactions.
Software | Best for | Integrations | Pricing |
CallMiner | Enterprise-level, omnichannel insights analysis | CRM, BI, CCaaS APIs | Custom/enterprise pricing |
Verint | Large enterprises seeking WFO and robust compliance capabilities | Unified Verint suite, CRM, | Custom pricing |
Balto | Real-time agent coaching and live assistance | CCaaS platforms, dashboards | Subscription, tiered |
FAQs
1. What is contact center speech analytics?
Contact center speech analytics is a solution that uses AI technologies to automatically analyze customer calls and interactions. With the solution, the customer service team can get insights into customer intent, agent performance, compliance, and emerging trends.
2. What is speech to text analytics?
Speech-to-text analytics is a technology that automatically transcribes spoken words from audio (like calls) into text, and then analyzes that text for sentiment, keywords, and topics. It’s often used by businesses to understand customer needs at scale and ensure compliance.
3. What is cloud based speech analytics?
Cloud-based speech analytics refers to a deployment model where the technology runs on remote cloud servers instead of on local hardware. For a call center, this model scales analysis capabilities instantly with minimal upfront costs.
4. How to use speech analytics effectively?
To use speech analytics effectively, you need to define clear goals first (e.g., “reduce churn by 15%”), choose the best tool that can integrate with your existing systems (e.g., CRM, WFM), then train agents to act on real-time prompts, and finally measure the KPIs and refine your strategy.
5. What are the categories of speech analytics?
The two main categories are Real-Time Speech Analytics, which provides live feedback during a call, and Post-Call Speech Analytics, which analyzes recordings later for deep trends.
