Sales Intelligence AI for sales insights and conversation intelligence

Using AI to unlock the hidden value of conversational data


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As often as they wish they could see customers’ thoughts, sales representatives and customer support agents are not mind readers.

And yet, every conversation is filled with valuable information that can help organizations better understand their customers, gaining unprecedented insights into consumer behavior, sentiment, and needs. It’s just a matter of deploying the right tools and technologies to harness the data that can be mined from conversations.

One reason organizations struggle to do this today is because this information – conversational data – is spread out across disparate modes of communication (voice, video, SMS) and can span millions of conversations over time.

A second challenge arises from each communication mode being siloed. What businesses really need today is a holistic understanding of customers and their experiences across multiple modalities. This requires an integrated approach where all communications modes can be analyzed together, which is why multimodal conversation intelligence is essential.

What is Multimodal Conversation Intelligence?

Conversation intelligence data comes from all forms of communications – both externally with customers, and internally from your workers. The common thread is using artificial intelligence (AI) to glean insights from each conversation. By bringing the voice of the customer to your organization, you can garner a better understanding of your strengths, areas for growth, and new opportunities to delight your customers.

Gathering conversation intelligence works like this: AI technology transcribes and analyzes conversations (frequently in real-time), factoring in everything from what was said and the sentiment behind it to the overall context of the conversation. This technology relies on natural language processing (NLP) and automatic speech recognition (ASR) to understand spoken and written conversations, machine learning (ML) to detect patterns and interpret them, and data mining to uncover information and patterns from vast amounts of conversations.

  • The data and insights from conversation intelligence can then be used to:
  • Understand how teams are performing
  • Help sales teams identify trends and opportunities
  • Uncover new coaching opportunities
  • Streamline workflows to increase productivity
  • Ensure compliance via saved and analyzed call recordings
  • Gain new understanding of consumer sentiment, behavior, and needs
  • Improve customer relationships
  • Enhance sales strategies and performances

These are just a few examples of use cases, both for internal operations and teams, as well as customer-facing communication. With these new layers of insight, you should expect to see more effective sales teams, more productive working teams, and happier customers.

To some extent, you can get these outcomes when conversation intelligence is drawn from just one communication channel, such as phone calls or messaging. The information may be accurate, but it provides an incomplete picture. To try and gain a full understanding of customer sentiment, sales opportunities, rep performance, and more from looking at a single channel is like looking at a photograph of a car to understand how to drive—you’re missing out on some important information.

On the other hand, multimodal conversation intelligence draws data from conversations across channels and analyzes them within the combined context. This means that all calls, messages, emails, and so on are considered together, rather than using individual tools to analyze each one individually. Doing so creates better data and analytics for organizations to leverage, in turn leading to improved employee productivity and customer satisfaction.

How Multimodal Conversation Intelligence with RingSense is Adding New Business Value

With that in mind, what happens when you bring the power of multimodal conversation intelligence to your teams? Customers can see it in action with the RingSense AI platform. RingSense leverages generative AI to gain new insights from conversation data.

In our RingSense for Sales solution, AI is used to:
Track and score deals throughout the pipeline to identify which ones are progressing and which ones are at risk of falling through
Report on the details of deals that are won or lost
Coach sales agents

With RingSense for Sales, organizations are able to overcome the challenges presented by large quantities of disparate data. Companies such as InsuranceHub Leavitt Agency and Total Security Solutions have been using RingSense for Sales leading up to its wider release, and the impact of multimodal conversation intelligence has been revolutionary for them. These are just two of many use cases where RingSense is providing new forms of value that only an AI-driven solution can provide.

“When I recognized a growing need for conversation intelligence, my initial thought was to seek help from a third-party AI,” said Lee LaBaigue, Senior Vice President of InsuranceHub Leavitt Agency. “However, when RingCentral introduced RingSense for Sales, it was a clear choice for us.”

Before using RingSense, Lee knew that a solution capable of automatically synchronizing notes and interactions would “bring significant enhancement to our workflow, bring about consistency, and also save an enormous amount of time.”

“This is precisely where RingSense AI is making a transformative impact, bringing structure and consistency to data management,” Lee says. “By leveraging RingSense conversation intelligence, we are significantly streamlining our operations and improving the overall efficiency and accuracy of our record-keeping, ultimately enhancing our customer service and legal compliance.”

Additionally, the team at InsuranceHub can quickly look up information from across conversations, significantly saving time and helping improve sales management.

“With a simple keyword, managers can find specific types of calls, whether they want to review ‘cancellation calls’ one day or focus on ‘new quote calls’ the next,” Lee explains. “This feature surfaces all relevant calls at once, allowing us to either listen to specific segments or read concise summaries. From the perspective of sales and customer-facing rep training, this technology is revolutionizing our approach to sales training, quality control and enhancing our customer experiences in promising ways.”

These sentiments are shared by Total Security Solutions’ Chad Mobley, Sales Manager, who stated: “What truly stands out about RingSense is its AI powered call and deal scoring capabilities. It goes beyond mere tracking of keywords, bringing a nuanced focus on the soft skills that truly make a salesperson exceptional. It accurately evaluates factors like relatability, conversational fluency, energy, balance in assertiveness, and the customer sentiment’s all key indicators of a good salesperson.”

It’s no secret that customer conversations contain a wealth of data that can help businesses and sales reps make better decisions. The challenge has been capturing those conversations in a way that the data can be properly analyzed and shared – not just in a timely manner, but also cost-effectively. These needs play to the strengths of AI, and with multimodal conversation intelligence, businesses finally have a way to get the full picture.

To learn more, read our recently published paper, Navigating AI With RingCentral.

Originally published Feb 15, 2024, updated Mar 22, 2024

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