Speech analytics: Everything you need to know
Sometimes, running a contact center can feel like you’re stuck on a hamster wheel. Constantly reacting to issues and events as they appear. Firefighting becomes your default position and you have a reactive mindset rather than proactive.
In reality, it’s simply a lack of data and insights that is creating this environment. And there is technology readily available today that can fill this gap and help you get ahead of the game.
Speech analytics software can help you proactively identify issues, trends or behaviors that can inform decision making and process change. Enabling you to plan, resource, and modernize your contact center far more effectively.
Let’s take a look at everything you need to know about speech analytics, from the common use cases for its deployment to how it will benefit your contact center.
Speech analytics unlocks insights from your customer interactions so you can proactively improve your contact center operations. Speech analytics automatically listens to (or can read) every call, email and social message that occurs in your contact center and enables you to understand the true voice of your customers and your team. Speech Analytics lets you understand how your customers are feeling and identifies opportunities for you to increase the performance of your contact center.
There are three core components that make up speech analytics technology; Speech-to-text transcription, natural language processing (NLP), and conversation analytics. Let’s take a closer look at each one.
Speech-to-text technology converts spoken words into text so they can be analyzed and interpreted.
Natural language processing uses AI and machine learning algorithms to understand the meaning of the text transcript (be that an original digital format or converted from a customer interaction).
Conversation analytics uses the data generated from the speech-to-text and NLP engines to deliver insights and metrics that can be used to understand the needs of your customers and agents so you can better plan and prepare for ongoing performance improvement.
By combining these technologies you can fundamentally improve business outcomes such as customer churn, sales revenues and operating costs.
Machine learning—a branch of AI—is foundational to speech analytics. Machine learning models are designed to find mathematical relationships in large data sets and solve for unknowns. With conversation data (i.e., the text transcripts generated from voice recordings), machine learning models can be trained to identify collections of phrases that have a specific meaning. For example, a machine learning model can be trained to identify the thousands of ways customers express frustration on a contact center call and label those points of frustration in a transcript.
Machine learning is especially useful for tracking objective QA criteria and customer or agent behaviors based on utterances. Speech analytics software that uses machine learning helps you monitor compliance criteria, provide targeted coaching to agents, and track large-scale trends in contact center performance.
Some speech analytics platforms (including Creovai) also incorporate generative AI, a type of AI that generates new content by learning from and emulating patterns in existing data. For instance, Creovai uses generative AI to:
Combining machine learning and generative AI gives contact centers a holistic view of what is happening in their customer interactions. AI-powered speech analytics helps contact center leaders make informed decisions that improve business outcomes.
Speech analytics can support you by uncovering hidden insights about your customers. Here are a few common uses cases for speech analytics:
Speech analytics automates quality assurance by analyzing every customer interaction, ensuring consistent monitoring and reducing the need for manual reviews. This allows supervisors to focus on coaching agents with targeted feedback, leading to improved customer interactions and higher agent efficiency.
Real-world example: NRTC, a utility and telecom call center, automated their 11-point QA checklist with Creovai Conversation Intelligence. This lets them see how every agent is performing across all of their calls—and surfaces each agent’s biggest opportunities for improvement. NRTC has developed training modules for each of the items in their checklist, and when managers see an agent struggling in one area, they direct them to the relevant module. This targeted approach to coaching has led to a 12-point increase in NRTC’s average QA score.
By analyzing customer conversations, speech analytics can identify recurring mentions of product issues, feature requests, or service pain points. This helps businesses make data-driven decisions to refine their offerings and improve overall customer satisfaction.
Real-world example: Consumer goods company Thrasio uses Creovai to uncover trending product feedback and issues across their large brand portfolio. They have configured over 1000 categories—collections of phrases related to specific products and issues—and track mentions of these across customer service phone calls, chat conversations, emails, Amazon reviews, and negative customer experience data sources. This lets their Global Services team quickly identify new issues and pass the feedback on to the appropriate team, leading to better brand experiences for their customers.
Speech analytics detects patterns in customer complaints, long call durations, or frequent call transfers that indicate points of frustration. Addressing these issues proactively enhances the overall customer experience and streamlines service processes.
By monitoring sentiment, tone, and keywords in customer conversations, speech analytics helps identify customers who may be considering leaving. Early intervention through targeted retention efforts can improve loyalty and reduce churn rates.
Speech analytics ensures that agents follow regulatory guidelines by detecting potential compliance violations. This proactive approach reduces legal and financial risks while reinforcing best practices among agents.
Real-world example: Utility provider Spark Energy must follow certain protocols, such as disclosing fees, when enrolling new customers. If their agents don’t follow the proper steps, the sale must be invalidated—and when errors occur across hundreds of calls, the cost can be high. Spark Energy uses Creovai to track fee disclosure language (and other compliance criteria) in their sales calls, enabling them to quickly catch and correct compliance issues and improve their sales processes.
Analyzing call patterns and recurring issues allows businesses to pinpoint inefficiencies, such as excessive call handling times or repeated customer inquiries. Addressing these inefficiencies can lead to cost savings and improved resource allocation.
By analyzing successful sales interactions, speech analytics helps determine which offers, scripts, or rebuttals lead to the highest conversion rates. This enables teams to refine their sales strategies and maximize revenue opportunities.
Real-time speech analytics provides immediate insights into successful interactions, allowing teams to adapt workflows and scripts dynamically. This ensures agents have the best possible guidance during live calls, leading to improved outcomes and efficiency.
We’ve discussed the practical applications of using speech analytics in the contact center. Next, we’ll look at the benefits of using this technology.
Speech analytics automates the evaluation of every customer interaction, eliminating the need for manual sampling and significantly reducing QA workload. This ensures a more comprehensive assessment of agent performance, allowing managers to provide precise, data-driven coaching that enhances service quality.
By identifying common issues that lead to repeat calls and extended handle times, speech analytics helps pinpoint inefficiencies in processes and agent responses. Addressing these problem areas not only improves operational efficiency but also reduces overall costs by minimizing unnecessary customer interactions.
Speech analytics detects early signs of dissatisfaction, such as frustrated language, negative sentiment, or repeated complaints, enabling proactive retention efforts. By equipping agents with insights to address concerns before customers leave, businesses can enhance loyalty and reduce churn rates.
Analyzing customer conversations helps businesses identify gaps in their self-service tools, such as unclear IVR menus or insufficient knowledge base articles. By enhancing these resources based on real customer feedback, companies can increase self-service adoption, reducing agent workload and improving customer convenience.
Speech analytics reveals which sales tactics, scripts, and rebuttals lead to the highest success rates, allowing teams to refine their approach. By equipping agents with proven strategies and real-time guidance, businesses can drive higher conversion rates and maximize revenue opportunities.
Understanding sentiment and key customer pain points through speech analytics allows businesses to proactively address frustrations and improve service quality. Faster resolutions, personalized interactions, and a better overall experience lead to higher customer satisfaction and stronger brand loyalty.
Of couse, not all speech analytics technologies are created equal. Here at Creovai we have spent the last ten years perfecting our blend of deep insights, rapid reporting and enterprise-grade security. Here are our top things to look for when picking a speech analytics vendor:
Seamless integration with CCaaS, CRM, and other platforms ensures that speech analytics can leverage all relevant customer interaction data for more comprehensive insights. This connectivity enables businesses to track customer journeys across multiple touchpoints, improving personalization and service efficiency.
Robust encryption, access controls, and compliance with industry standards ensure that sensitive customer data remains protected. By implementing strict security measures, businesses can maintain customer trust and avoid legal or regulatory risks associated with data breaches.
Advanced speech recognition technology ensures that call transcriptions are highly accurate, reducing errors and improving the reliability of insights. Higher accuracy leads to better analysis of customer sentiment, agent performance, and compliance adherence.
AI and machine learning enables automatic categorization of calls based on topics, sentiment, and behavioral patterns, reducing manual tagging efforts. This helps businesses quickly identify trends, track customer concerns, and optimize responses for different interaction types.
By analyzing tone, keywords, and past interactions, predictive analytics can forecast customer sentiment, satisfaction, and effort levels. This allows businesses to proactively address potential dissatisfaction and enhance customer experiences before issues escalate.
Speech analytics helps uncover underlying reasons for high repeat contacts, long average handle time (AHT), and other inefficiencies by identifying recurring patterns in conversations. Understanding these root causes enables businesses to implement targeted process improvements and optimize operational performance.
Automated QA scoring ensures that every call is evaluated against consistent, objective criteria without the limitations of manual reviews. This allows businesses to identify coaching opportunities faster, improve agent performance, and maintain compliance more efficiently.
Real-time integration enables speech analytics to provide instant guidance to agents during live interactions, helping them adjust responses based on customer sentiment and compliance needs. This leads to better outcomes, improved customer satisfaction, and more effective issue resolution in the moment.
There’s a lot you can do with speech analytics software, but it’s best to define 1-2 top goals to start with. For example, if improving first-call resolution is one of your contact center’s top priorities, you might set a goal to use speech analytics software to identify the top three agent behaviors associated with repeat contacts and coach your agents to avoid those behaviors. Aligning your analytics strategy with business objectives will help your team stay focused and generate faster ROI.
Speech analytics affects multiple teams—quality assurance, compliance, operations, and training (not to mention the departments outside the contact center that can benefit from conversation insights). Involve cross-functional leaders early to ensure the platform addresses everyone’s needs and integrates smoothly into existing workflows.
Some agents might initially feel wary when they hear that AI-powered technology is going to listen to and analyze all their conversations. When talking to agents about new speech analytics software, make sure they understand how it benefits them. Emphasize that it will help make coaching more objective and targeted, allowing them to make meaningful improvements. Showing agents how speech analytics improves their daily work will help earn their buy-in.
Speech analytics can surface powerful data, but it’s only useful if teams know how to act on it. Invest time in training frontline managers and QA analysts to interpret trends, spot coaching opportunities, and translate insights into action.
Speech analytics enables you to keep optimizing contact center performance. After achieving success with your initial goals, look for ways to expand your use of the software and achieve even greater benefits. For example, while you might initially implement the software to automate your QA process and improve agent coaching, you could also begin tracking product issues and delivering those insights to relevant decision-makers.
93% of consumers are likely to make repeat purchases from businesses that offer excellent customer service. Insights from speech analytics software that utilizes AI and machine learning (ML) can help contact centers defuse tricky situations, reduce customer churn and enhance the customer experience. Using a combination of language and behavioral analytics, such as topic modeling, natural language processing (NLP) and vocal emotion detection, speech analytics provides these insights.
In short, ensuring your contact center is compliant and has the capacity to identify and serve vulnerable or dissatisfied customers holds the key to transforming the customer experience. As a result, it’s no surprise the speech analytics market is expected to reach $5,460 million by 2026.