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What is voice analytics? Definition, tips and best practices


The Team at CallMiner

February 28, 2024

What is voice analytics? Definition, tips and challenges
What is voice analytics? Definition, tips and challenges

Updated February 28, 2024

Voice analytics is a valuable tool enabling businesses to gain valuable insights from conversations and interactions with customers. As artificial intelligence (AI) capabilities have rapidly advanced, and technologies such as machine learning and natural language processing (NLP) have become commonplace in everyday life, AI-driven voice analytics solutions are increasingly powerful for extracting actionable insights from what was once unstructured, difficult-to-analyze data.

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This blog offers insights into what voice analytics is, as well as tips and challenges associated with this technology, including:

  • What is voice analytics?
  • Differences between voice analytics, speech analytics, and text analytics
  • The importance of voice analytics
  • The benefits of voice analytics
  • Voice analytics: Transcription or phonetics
  • Voice analytics tips and best practices
  • Frequently asked questions

What is voice analytics?

Voice analytics refers to the use of a voice recognition tool to record and analyze a spoken conversation, such as those that happen in the contact or customer service center. Not only does voice analytics software translate speech to text, but it can also identify the speaker’s emotion and intent by analyzing audio patterns.

This software was first leveraged for commercial purposes in the enterprise in the early 2000s. Since then, it has grown in importance, with more and more companies investing in voice analytics technology.

In fact, according to Allied Market Research, the global speech analytics market is expected to grow from $1.7 billion in 2021 to $4.9 billion in 2031, growing at a compound annual growth rate (CAGR) of 11.2%.

The first solutions in this space concentrated on converting speech to text, which certainly had its uses. For one, it was quick to produce a transcript or a report on an agent’s call. However, data analysis was still a manual and time-consuming task left to team members. Leading voice analytics solutions today go further and leverage conversation intelligence technology, which applies large language models (LLMs), sentiment analysis, and other artificial intelligence techniques and machine learning algorithms to automatically convert unstructured information from customer interactions into structured data, which can then be searched and analyzed.

Differences between voice analytics, speech analytics, and text analytics

The terms voice analytics, speech analytics, and text analytics are often used in the same context, and speech analytics and voice analytics are often used interchangeably. However, each term refers to a specific function:

  • Speech analytics: A technology that identifies what is said in a conversation, focusing on the actual words spoken. Speech analytics solutions convert speech to text to organize and analyze the content, identifying patterns and commonly used terms and phrases.
  • Voice analytics: A technology that identifies how things are said in a conversation, focusing on the tone and pitch of spoken language, the tempo, rhythm, and stress. Voice analytics uses sentiment analysis to reveal the intent, sentiment, and emotion behind the spoken words, so these technologies are often called sentiment analysis solutions.
  • Text analytics: A technology that analyzes the meaning, intent, and emotion in written language such as SMS, email, chat, and social media.

Comprehensive analysis of customer interactions requires all three technologies, which collectively are referred to as conversation analytics.

The importance of voice analytics

Voice analytics software brings with it enormous benefits. Companies across f various industries, such as insurance, technology, financial services, and healthcare, are leveraging this technology to generate insights into customer needs and drive business performance improvement.

Business areas such as customer service and customer experience can particularly benefit from voice analytics. By using voice analytics solutions to analyze huge volumes of customer conversation data, your company can identify vital and previously overlooked information that allows you to better meet your customers’ needs, predict future behavior, and create highly targeted messaging that resonates with your customers.

The benefits of voice analytics

Voice analytics can boost customer service and call center performance levels in the following ways:

  • Enhancing customer satisfaction: Voice analytics software identifies recurring themes, trends, and hot topics that can rapidly highlight customer satisfaction levels.
  • Improving customer experience and loyalty: By analyzing the sentiments and emotions in a customer’s voice, companies can tailor their services to meet customer needs and expectations.
  • Preventing customer churn: Early identification of dissatisfied customers helps in proactive management and issue resolution. By addressing issues promptly, before your customer takes their business to your competition, there’s a better chance of retaining customers.
  • Creating personalized experiences: Recognizing customer sentiments helps in personalizing interactions and recommendations, which can lead to enhanced user satisfaction.
  • Ensuring regulatory compliance: Detecting specific phrases and keywords in customer conversations enables you to monitor regulatory compliance, detect potential compliance breaches quickly, and take immediate corrective action.
  • Gaining competitive intelligence: You can identify competitive intelligence in conversation data with new and at-risk customers who may ask for features and particular benefits your competitors provide.
  • Improving products and services: Gathering and analyzing vocal feedback can help in recognizing common issues and complaints that inform product and service areas that need enhancement or improvement.
  • Improving employee training and development: Sentiment analysis can be used in training programs to enhance employee communication skills. Ongoing analysis helps in identifying underperforming agents and areas where employees need further training or development.
  • Sharing best practices and messaging that works: Help your B performers turn into A performers by identifying the messaging and conversation techniques that bring results and boost customer satisfaction.
  • Building a positive brand reputation: Voice analytics provides insights into brand satisfaction. Your team can address negative sentiments to improve your brand’s image.
  • Improving operational efficiency: Voice analytics can help in automating some aspects of customer interaction, such as query resolution. Additionally, voice analytics can help to reduce average handling time by identifying customer issues promptly so your agents can resolve them quickly.
  • Making data-driven decisions: Insights derived from voice analytics can drive strategic business decisions. Ongoing analysis helps in monitoring performance and identifying areas that require attention.

Voice analytics: Transcription or phonetics

There are two different approaches to voice analytics – phonetics and transcription. They both begin the same way by identifying the sounds and audio and converting them to phonemes – the basic units of communication.

However, a phonetics-based conversation is limited hereafter. These solutions scan an extensive list of phonemes and identify phonetic patterns for words and phrases. This technique is inefficient, can be slow, and – because there is an average of 4 phonemes per word – there is a high chance of errors.

Transcription or speech-to-text technology goes one step further than phonetics and applies a language model of hundreds of thousands of words to the phonetic index, enabling the analytics software to automatically piece together accurate conversations using the same logic and context found in the human brain.

As you weigh up which voice analytics solution is most suitable to your business needs, you should prioritize transcription-based technology.

Voice analytics tips and best practices

There are a number of best practices you should bear in mind as you figure out the solution most suited to your company.

  • Identify your precise business needs: Consider things like the size of your company and the volume of calls you want to analyze. This will help you figure out whether you need an on-premise solution or if you need a more flexible option like a desktop solution.
  • Outline your project goals: What do you hope to gain from voice analytics? You should be clear on your project goals at the start to avoid scope creep. Figure out your use case and develop your project accordingly.
  • Real-time analytics: How long does the solution take to work? The best technology in this space provides real-time analytics. Real-time call monitoring allows managers to review and analyze customer conversation data quickly and eliminate any issues as they arise.
  • Self-manage agent performance: The best solutions enable agents to self-manage their individual performance. With quick access to and insights on call performance, voice analytics can foster employee self-motivation and enhance employee experience. Agents can track and analyze their performance and drive self-improvement.

Voice analytics is an exciting and rapidly growing area in business today. It offers enormous benefits to your organization by improving agent performance and boosting customer satisfaction.

If your business wants to tap into the power of voice analytics, invest in an advanced voice analytics solution that leverages multiple AI techniques for deeper, actionable insights from customer conversations. The CallMiner platform combines voice and text analytics to analyze all customer interactions at scale across multiple channels such as phone, email, chat, and more, providing more comprehensive conversation intelligence insights and connecting those insights with actions that drive business improvement.

Frequently asked questions

How does voice analytics work?

Voice analytics works by capturing and analyzing audio data from voice interactions such as phone calls, voice assistants, and other voice-enabled technologies. Advanced algorithms and artificial intelligence (AI) are used to transcribe the audio into text, identify speech patterns, emotions, and various vocal qualities, and extract meaningful insights from the conversation.

These insights can be used for various purposes like enhancing customer service, improving products, and making informed business decisions.

What can voice data be used for?

Voice data can be used for a multitude of purposes such as:

  • Customer service: Understanding customer needs, pain points, and satisfaction levels to enhance service quality.
  • Market research: Gathering insights into customer preferences, purchasing behavior, and reactions to products or services.
  • Operational efficiency: Identifying areas for operational improvement and automating certain customer interaction processes.
  • Employee training: Using voice data to improve the training of customer service representatives, call center agents, and other client-facing employees.
  • Fraud detection: Analyzing voice interactions to detect unusual or suspicious activities.

What is the difference between speech analytics and voice analytics?

Speech analytics focuses primarily on the words spoken during a voice interaction. It involves transcribing audio, analyzing the choice of words, phrases, and the structure of conversations to extract meaningful information and insights. It is used mainly to understand the content of the conversation.

Voice analytics, on the other hand, goes beyond the words to analyze the speaker's tone, pitch, speed, and emotional state during the interaction. It gives context to the spoken words and offers insights into the speaker's sentiments and emotional state. Voice analytics is used to glean deeper insights from voice interactions.

What is an example of sentiment analysis?

One example of sentiment analysis is analyzing online product reviews. A customer might leave a review saying, "The product is good, but the customer service was terrible!"

Sentiment analysis software would process this text, identifying the positive sentiment towards the product ("The product is good") and the negative sentiment towards customer service ("customer service was terrible"). This allows the company to understand and categorize customer sentiments, helping them improve specific aspects of their business.

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