Conversational analytics

Customer conversations are a treasure trove of valuable insights. With built-in conversational analytics capabilities, RingCentral CX makes it easy to extract the data you need.

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A male contact center agent with an inlay of conversational analytics

Every conversation with your customers and prospects contains valuable nuggets of data, revealing what they really feel, want, and need. With thousands of customer interactions taking place across multiple channels, how can you possibly extract the relevant insights?

That’s where conversational analytics comes in—using AI to collect and process conversational data at scale. No wonder the global conversational AI market size— estimated at $11,576.4 million in 2024—is expected to reach $41,393.2 million by 2030.

So, what is conversation analytics, and how do you harness it in your contact center?

What is conversational analytics?

Conversational analytics is the process of analyzing human interactions—typically those between a business and its customers—to extract actionable insights. It involves collecting data from interactions and processing it using AI algorithms and natural language processing (NLP).

Sometimes called conversation analytics, this method is applicable to both spoken and written language. It covers interactions including voice conversations, chatbots and live chat, email, social media, and customer reviews.

The aim is to increase your understanding of customer behavior and sentiment, using the analyzed data to provide better service and support, make informed decisions, and generally improve customer experiences.

A female home-based contact center agent in front of laptop with an inlay saying analyzing call and question detected

How does customer conversation analytics work?

Conversation analytics uses a variety of technologies and techniques to understand the nuances of human interactions, such as their content, context, intent, and sentiment. It’s able to evaluate unstructured and unsolicited data, not just feedback requested by the business.

So, precisely how do conversation analytics work? Here are the steps involved:

Data collection: The conversational analytics process begins with data-gathering from sources like recorded phone calls, email exchanges, social media messages, and customer interactions with voice assistants and chatbots. You’ll need to convert spoken words into text for the tools to analyze them.
Pre-processing: Now, the tools simplify the text, removing any background noise and splitting text into recognizable words or phrases with a technique called tokenization. Pre-processing also takes out irrelevant wording (such as stop words), plus punctuation and special characters.
Processing: At this stage, artificial intelligence (AI) and machine learning (ML) algorithms process the text using natural language processing (NLP). This enables computers to understand and interpret human language, picking up on sentiment and tone. These tools become smarter over time as they feed on more data.
Reporting: Your analytics platform will report back on these findings, typically displaying the data in a visual format to highlight conversational patterns and trends and even predict likely outcomes. You can identify customer preferences and common keywords.

Everything you need from conversation analytics software

To make the most of your data, you’ll need the best conversational analytics software. RingCentral RingCX, when combined with RingSense AI, gives you a raft of smart features, including:

Call recording and AI transcription

RingCentral RingCX can record your phone calls and video meetings, either to play back later or to use for training and compliance purposes. But that’s not all—AI transcription also captures the spoken words as text, with no manual work required.

As well as converting recordings from various file formats into text, AI speech recognition technology delivers a full transcript of every phone call, plus a summary that highlights the main points, action items, decisions, and items for follow-up.

AI also provides live transcription during video calls (This serves as closed captions, too) and transcribes your voicemails.

Sentiment analysis

RingSense AI listens to what your customers say, but it also listens to the way they say it. Sentiment analysis, which uses NLP and machine learning, can detect positive, negative, or neutral sentiments from speech during phone calls.

The machine learning models are trained using datasets of positive and negative words, and they also pick up on vocal tone to identify customer emotions. This helps businesses to assess customer satisfaction and get ahead of any serious issues.

The best part is that you don’t have to wait for the results of a feedback survey to discover how people really feel about your product or service—you can see it live.

More AI-powered insights

RingSense AI brings plenty of other AI capabilities to the RingCentral platform. For example, it features call scoring for agents and reps with AI-generated scorecards after each interaction for managers to view at a glance or dive into the details.

If an agent is handling a tricky query, the Agent Assist tool grabs helpful information from your existing knowledge base and CRM. It pops up on the screen with details and suggested answers, improving agent confidence, performance, and response times.

At the end of each interaction, AI automatically updates your CRM with customer information and notes, eliminating manual data entry.

Contact center reporting and analytics

Alongside conversational analytics tools, RingCentral RingCX analytics help you collect actionable insights into contact center performance and customer behavior. You can measure metrics such as first response times and first contact resolution rates and predict future outcomes.

Real-time dashboards contain individual widgets to display your data in numbers, charts, and graphs. You can add prebuilt or custom widgets for different categories of live data, and historical dashboards let you compare past and present.

Contact center reporting and analytics lets you monitor compliance and adherence to sales scripts, and you can set up rules and alert systems to notify managers when a specific event occurs.

Benefits of using conversation analytics

Automatically analyzing customer interactions lets you gain insights at scale. Here are some more advantages that demonstrate the importance of conversation analytics:

Improved customer experience

Conversation analytics gives you the chance to deliver a stellar customer experience, increase loyalty, and reduce churn. With insights into the context, purpose, and sentiment of queries and requests, you’ll have a better understanding of customers’ needs and preferences.

You can pair conversational analytics with KPIs to identify ways to improve response times and resolution rates. Plus, you can track the performance of communications tools, such as self-service, and learn how to make them more effective for users.

None of this requires any effort on the customers’ part, as the data is gathered automatically without the need to fill in post-call surveys. You can also use the information to personalize responses and recommendations.

Better conversion rates and more sales

Conversational analytics gives you deeper insights into what drives (or hinders) sales. You’ll be able to identify common pain points, reasons for objection, and potential sales bottlenecks.

For example, you’ll find out which competitors keep coming up in conversations with prospects. When you learn which products or features are most appealing to prospects, you can use this intel to inform product development.

By analyzing interactions across multiple touchpoints (including non-direct feedback on social media), you can map customer journeys and identify obstacles, and then work to improve your sales processes for better results.

Enhanced agent performance

Key conversation analytics benefits also include improved performance for agents and reps. That’s because it shows you who’s doing well and who could use a little extra help. Post-call summaries and scorecards provide data after interactions, but real-time analysis is even more useful.

With RingCentral’s AI sales coaching, supervisors can monitor live calls and customer sentiment on a dashboard. If anything flags as negative, they can scan the real-time transcript for more context, then help the agent by whispering instructions, joining the conversation, or taking over the call.

Conversational analytics helps you to understand how and why your top performers do what they do and use those best practices to coach others. Don’t forget that you can apply sentiment analysis to contact center staff as well as customers.

2 layers of mobile interface with the top layer showing a interaction diagram and the 2nd layer showing Prompts

Use cases for conversational AI analytics in the contact center

So, we’ve seen the benefits, but how do you actually apply conversational AI in your contact center? Let’s check out some conversational analytics examples:

Proactive customer service

Conversation analytics isn’t just about using data to improve CX in real time or at a later date. It also alerts you to potential problems so that you can get ahead of them. For instance, if a customer raises a fault with a product, you can work to fix it before there are any further complaints.

By analyzing customer intent and sentiment, you can also forecast future behavior. Advanced analytics determines who’s at risk of churn, who’s likely to make a purchase, and who would be delighted with a certain offer. Then you can reach out to them proactively.

You also have the opportunity to tweak your support offering to prevent future customers from churning. Add new FAQs based on common customer issues or prioritize upgrading your chatbots if users are getting frustrated.

Sales funnel optimization

A better understanding of customer behavior during sales interactions will help you to maximize conversions and revenue. It’s also a good way to optimize your sales funnel so that your reps are giving the right nudges at every stage.

With conversational analytics, you’ll know exactly where and why prospects are losing interest or raising objections to a sale. You can then provide extra coaching to reps, optimize your sales scripts, or figure out the right frequency of follow-ups.

For existing customers, analysis shows you opportunities for upselling or cross-selling based on sentiment and preferences. Reps will spot the perfect time to recommend relevant products or services to increase order value.

A woman holding her mobile phone in front of a laptop with inlay of sentiment pie chart

Voice of the Customer (VoC) programs

VoC programs are dedicated to capturing customer feedback about their experiences with your products, as well as the customers’ expectations and preferences. Instead of relying solely on traditional methods like surveys, focus groups, and 1:1 interviews, you can now use automated analysis.

Conversation analytics tools listen in on all your channels, from phone calls to chatbot interactions and social media. They reveal customer preferences, pain points, and overall sentiment toward your company. Topic extraction shows you what’s most relevant and identifies trends and patterns.

How to choose the best conversational analytics software for your business

As more businesses embrace AI and machine learning, there’s a growing number of software options for conversation analysis. So, how do you pick the right one for your needs?

It’s important to take advantage of any free trials or demos offered by software vendors so that you can determine whether the tools are user-friendly. A steep learning curve will mean it takes longer for reps and agents to get up to speed with the new system.

In terms of features, look out for real-time monitoring of all channels, plus customizable dashboards and reports. You’ll want AI transcription and auto-recording for voice and video calls, and the ability to analyze multiple languages is a must for global businesses.

It’s super-useful to have other AI tools like screen pops and live coaching, so check out conversational analytics as part of a wider contact center or business communications solution. Choose a tool that integrates easily with your existing tech stack, especially your CRM.

You can’t let the customer data you’ve collected fall into the wrong hands—which means choosing software that’s super-secure and compliant with data privacy regulations like CCPA and GDPR, too.

A solution that checks all those boxes? RingCentral RingCX. Along with built-in RingSense AI for conversational analytics and more, you’ll find enterprise-grade compliance, security, and 99.999% availability reliability.

Conversational analytics FAQs

What are the main differences between traditional and conversational analytics?

Traditional analytics involves a certain amount of effort from you and your customers. It relies on manually transcribing every phone conversation or listening to recordings to pick out key words and phrases. You’d also need to ask customers for feedback in the form of surveys.

This method only lets you analyze and report on interactions after they’ve finished, so you’re looking at historical data to find patterns. It gives you a broad overview of performance.

With , you can analyze interactions in real time, giving you the opportunity to address customers’ needs instantly. Plus, calls are transcribed automatically, saving everyone time.

Are there any challenges to overcome when implementing conversation analytics?

Like any new technology, there are a few challenges involved in implementing conversational analytics. For one thing, when you convert speech to text, you can miss cues like tone of voice and nonverbal actions. Machines can find it difficult to maintain context in multi-turn interactions.

Customers may use slang, colloquialisms, or poor grammar, and the data may contain misspellings or mistranslations—especially given that different languages have different characteristics and structures. It’s also harder for AI to pick up on sarcasm and jokes.

Low-quality data can affect the accuracy of NLP models. Historical data may also contain harmful biases, which, in turn, lead to biased responses. You’ll need to update and improve the system continually based on the latest data.

It’s crucial to have the right infrastructure to handle a large volume of real-time conversations. And, of course, you have to consider the challenges of privacy and data protection.

What’s the difference between conversational analytics and sentiment analysis?

Sentiment analysis is about understanding the emotional tone of an interaction based on whether the words and phrases used are positive, negative, or neutral.

Conversational analytics analyzes the whole conversation, including context, intent, and topics—as well as sentiment. So, sentiment analysis is just one element of conversational analytics.