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Overview Of Voice Conversational Intelligence

With companies today engaging in a multitude of voice and video conversations with customers across many stages of the customer lifecycle, it has become crucial to be able to analyze the content of these conversations in a way that provides actionable insights into the business. This article provides an overview of how voice analytics works, the many benefits to applying voice conversational analytics in your business, and what to look for when selecting a provider.

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By Elliot
August 20th, 2021

We're communicating with our customers via voice and video calls and meetings more and more these days. The rise of voice and video calls has driven a greater demand for analytics that can help us understand our customers better, improve our support and sales process, and enable us to gain a competitive advantage.

Voice conversational intelligence refer to the extraction of actionable information from voice conversations. With companies today engaging in a multitude of voice and video conversations with customers across many stages of the customer lifecycle, it has become crucial to be able to analyze the content of these conversations in a way that provides actionable insights into the business.

Voice Of The Customer

Companies have been gathering customer feedback via surveys for decades, but much of that data was qualitative in nature. One would ask the customer to rate their support experience on a scale, or to rank three features in order of importance. While this sort of information can be valuable, it does not necessarily provide context or insights into what is actually happening with the customer.

As companies have begun to leverage voice and video calls to improve support, sales, and other areas of the business, they have encountered a new challenge: how to use this raw unstructured data for business intelligence. With traditional call centers, businesses could analyze structured metrics such as the number of calls, the top issues customers were reporting, and the average length of a support call. However, these sort of metrics don't actually look at what the customer is actually saying.

The challenge is how to extract actionable insights from inside voice and video conversations. Organizations that do not have a way to extract value from the content of their calls and meetings are missing out on a key opportunity to improve their business and better serve their customers.

Roots Of Conversational Intelligence: Text

In the past, conversational intelligence has largely been confined to text analysis. While the voice conversational analytics space is relatively new, it has its roots in analytics that have been performed on traditional text-based customer feedback for many years. Text analytics providers like AlchemyAPI, Lexalytics, and IBM have provided natural language processing solutions used by companies to mine their text-based customer feedback in order to improve their products and customer experience.

Text analytics relies on the analysis of raw unstructured text from customer support tickets, survey responses, chat bots and other systems. Using natural language processing algorithms, text analytics aims to understand the key elements of a piece of text and summarize it in a machine readable format. These systems often employ sentiment analysis to discern how the customer is feeling about the problem they are describing, as well as keyword and concept analysis to detect other themes of the text.

Enabled by recent advances in speech recognition technology, businesses are now finding ways to integrate voice and video data into their conversational intelligence efforts. While the world of voice conversational intelligence is relatively new, it has generated a great deal of buzz in the tech industry, and a number of companies have emerged to find ways to improve how we leverage voice conversations for business intelligence.

Voice analytics is still in its infancy, but early adopters are already beginning to see the many benefits that can be generated by capturing and analyzing voice calls and meetings. While early adopters have been largely confined to leveraging voice analytics in call centers, companies are now deploying these systems in the realm of sales calls and regular business meetings.

From Call Centers To Calls And Meetings

Spurred by early successes in the call center spaces, businesses began to explore how they can leverage voice analytics for more than just customer support. Many have begun to integrate call recording and analysis into their sales and marketing processes, using this capability to improve lead qualification and conversion. Calls can be recorded and transcribed, and key moments in the call can be tagged and associated with each customer. Using this call data, sales teams can provide more tailored follow-up, send personalized emails and other communication, and prioritize leads for the sales force. Sales can be accelerated, as sales reps are able to spend more time with high-value leads, and productivity can be improved as conversations can be better organized.

The move from call centers to voice conversations and meetings has been driven in part by the transition of the workforce from traditional office workers to employees who work remotely. As more employees work from home or from an office outside of the main office, they are having more calls and video conferences vs meeting in person. It is critical that businesses be able to capture these meetings and conversations, analyze them, and use this data to improve their business process.

Benefits Of Voice Conversational Intelligence

Voice analytics can provide a number of benefits to companies who are capturing their calls and meetings. While some of these benefits are similar to those that can be generated by leveraging text-based customer feedback, the quality, depth, and breadth of data is enhanced by the analysis of voice conversations. Customers are often willing to convey more information verbally than over other mediums and will provide candid and detailed context to their problems, issues, and suggestions.

Here are a few examples of how voice analytics can help your business:

1. Customer Support

Voice conversational intelligence enables companies to gain a better understanding of the customer experience at each stage of the customer lifecycle. They can see where in the process customers are running into problems, what features or products they are struggling with, and how support reps are performing.

Companies often wait until a customer files a support ticket before being aware of a problem, when this information may in fact be sitting in interactions the customer has had with their sales and account success reps. Did they mention running into trouble getting setup? Challenges with particular aspects of your product and/or service? By mining customer conversations, support issues can often be discovered before they escalate into significant problems.

2. Sales And Marketing

Companies can improve their sales and marketing process by leveraging voice analytics applied to sales calls. They can gather better insights into the needs of each customer, and can streamline the lead-management process.

Sales calls contain a wealth of information about the deal process: which competitors a company is up against, key decision criteria or "must have" components of the deal, compliance or regulatory concerns, and so on. These sort of factors can have massive impact on the win/loss rate for a company's sales team, and by mining sales conversations for this data, a company can gain a better understanding of why some deals are closing and others are not.

3. Product Development

Voice and video calls can provide valuable insights into emerging trends, issues, and ideas for improving a company's products and services. Businesses can gather insights into features or products that customers are struggling with, and they can proactively ensure that these problems are resolved before they turn into negative feedback.

Product managers in many organizations struggle to get first-hand access to customer opinions, feedback, wants, and needs. Voice conversational analytics enables feedback to be pulled from large numbers of customer interactions to drive data-driven decisions in product roadmap planning and development.

How Meeting Capture Works

While some companies are familiar with call-center voice analytics, they may not be as familiar with AI meeting capture in a B2B Zoom/Google Meet/Teams context. With AI meeting capture, a system automatically joins meetings on your calendar, records them, and uses speech-to-text technology to create machine-readable transcripts. Natural language processing and computer vision technologies analyze the transcript and any slides or shared screens from the recorded call.

Call and meeting capture such as Hyperia work with many of the most popular video collaboration solutions in the industry, such as Zoom, Google Meet, and Microsoft Teams. These systems can also integrate with your company's existing sales prospecting tools (dialers) and VOIP providers such as Zoom Phone and RingCentral.

Voice Analytics Requires More Than Transcription

While transcription is an essential first step to extracting value from voice calls and meetings, it is not sufficient on its own. When choosing a voice conversational intelligence solution, companies should expect more than basic transcription functionality.

Speech transcription should be paired with advanced natural language processing and screen analytics capabilities that provide additional context to what happened in calls and meetings. This allows a company to gain insights into what happened in a conversation, what needs to be improved, and how they can better engage with their customers.

Creating Searchable Knowledge

Conversational intelligence requires more than just transcribed words; mining insights requires a deeper knowledge-based view of conversation data. This knowledge can be broken down into multiple categories, including actionable, structural, and contextual:

  • Actionable knowledge includes specific insights that you can apply in your business. For example, "John Smith will not be able to attend the meeting on July 14." This is a piece of actionable knowledge that you can apply in your business. You can look up John Smith in your company's CRM, and determine if you should reschedule the meeting. Or, if you have him tagged in the meeting, you can send him an email.

  • Structural knowledge includes information about the structure of the conversation, such as attendees, the topics that came up, or the overarching context of the meeting. This knowledge is critical for understanding how different pieces of conversation fit together. For example, you may have a meeting where you need to discuss a product idea with a sales rep, a marketing director, and a customer who's using your product. This is structural knowledge. It tells you the context of the conversation.

  • Contextual knowledge describes aspects of the conversation in the broader context of previous interactions with the same customer. For example, if you have a customer who is always calling about a specific issue, then you can leverage this conversation for the next time they call about this issue. Knowing how the customer feels about the issue, knowing if they are frustrated with your product, and what product they are using can all be contextual elements that can be leveraged for the next interaction.

It is important to understand the role that machine-readable knowledge (versus simply words) plays in mining insights from conversational data. If a call and meeting capture solution stops analysis after transcribing the audio to text, it has not yet created the searchable knowledge required for enabling voice conversational intelligence.

Automated Tagging Of Key Events

Natural language processing and automated event tagging are critical elements of any voice conversational intelligence solution. When a customer mentions a concern in the conversation, it is important to have an system that can detect this event, so it can be extracted and analyzed by your business. Key event tagging requires more than simple keyword analysis, needing the system to be able to understand the context of a conversation.

Business conversations can contain information about a customer's plans, needs, concerns, budget, and other important factors. A company should expect their voice conversational analytics system to be able to tag all of these events in a meaningful way. This capability can provide insights into how a business can better interact with their customers, help sales staff qualify leads, and identify product needs.

Reports And Aggregate Insights

One of the benefits of leveraging voice conversational analytics is the ability to see aggregate insights that can be applied in your business. Systems such as Hyperia translate recorded conversations into a Wikipedia-style structured knowledge base that can be searched and explored. Conversations around key topics and themes are summarized across many dimensions, including the customer, product, sales or support rep, and more. This allows businesses to gain valuable insights into how they can improve their sales and support processes, and to identify trends that they wouldn't be able to see from basic transcription.

Searching Calls, Customers, and Topics

Searching conversations is an important capability in any voice conversational intelligence solution. In order to enable your business to extract value from call and meeting data, it is critical that you find a solution that allows you to easily search and explore. If you have a customer who called about an issue, you need to be able to quickly search for previous conversations where this problem came up, as well as the customer's interaction history with your company.

Similarly, if you have a customer that you want to reach out to, it can be helpful to be able to search for previous conversations first, so you can better understand who they have already talked to in your company, and how you can best interact with them.

Value Of Conversational Alerts

Voice conversational analytics can provide a number of data-driven insights to companies. It enables a business to better understand their customers and improve the overall customer experience. One valuable thing to leverage to streamline the insights delivery process is proper usage of conversational alerts.

Alerts enable real-time notification when specific things occur inside a customer conversation. For example, if a high-value client raises a problem or concern on the phone, you should be able to set up an alert that triggers notifying key people in your company. This allows your support team to more quickly identify potential issues, and to resolve problems before they turn into a negative customer experience.

Where The Industry Is Going

The biggest trend that we see is an increased reliance on data-driven insights and process automation using voice and meeting data. As companies deploy more voice conversational intelligence solutions, we expect their sales and support process to become more streamlined, and their customer relationships to improve. Another trend that we see is increased adoption of the technology among smaller companies. Historically, voice conversational analytics was largely used exclusively by large enterprises. However, with the advent of modern AI call and meeting capture solutions, the cost and complexity of deploying this technology has decreased significantly, and as a result more companies are leveraging it within their businesses.


When it comes to deploying voice conversational intelligence, it is critical that you find a solution that provides comprehensive insights from what's occurring inside your customer conversations, and not just a call recording and capture tool. Hyperia's solution is an easy way to deploy voice conversational analytics in your company. With advanced natural language processing and computer vision analysis of captured calls and meetings, it goes beyond simple call transcription, and provides you with the insights and automation capabilities that can help your business gain a competitive advantage.

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