Conversational analytics
Customer conversations are a treasure chest of valuable insights. With built-in conversational analytics capabilities and AI Quality Management, RingCentral CX makes it easy to extract the data you need.
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.
When you combine conversational analytics with RingCX AI Quality Management, you make every insight count by transforming it into action. You’ll be able to score agents automatically, analyze every call, coach in real time, see trends and competitor mentions, and build custom training plans that help your team deliver the best customer experience.
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.
Start turning conversations into conversions
Turn every customer interaction into a growth opportunity with RingCentral’s conversational analytics. Transform calls into actionable insights that boost sales and elevate customer satisfaction.
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.
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% uptime reliability.