Blog

Developer
Concepts
Use Cases
Product
Spotlight

The Developer’s Introduction to Real-Time Passive Conversation Intelligence

Real-time passive conversation intelligence analyzes human conversations as they happen and extracts meaningful insights – like topics, action items, intents, and sentiments – without having to use a wake word or give commands. Businesses can use this natively in their workflows or products to improve their sales, productivity, and customer experiences.

What’s real-time passive conversation intelligence?

Let’s start from the top: Conversation Intelligence (CI) uses AI and machine learning to transcribe and analyze human conversations — either with other humans or with bots – to pull valuable, actionable insights from it. These insights can be anything from the most important topics discussed in a company meeting, to what phrases convert the most customers on sales calls.

Next: a conversation intelligence system can either be active or passive:

  • Active CI: Has to interact with a human and receive specific instructions before it can take action. For example, you have to say “Hey Google” for the AI to wake up and hear you yell for the next song on your playlist. Think of active CI as an obedient (but somewhat simple) droid that only does what you tell it.
  • Passive CI: Doesn’t need to interact with humans or be instructed. It runs quietly in the background, absorbing what’s being said with human-like understanding, and then surfaces relevant information at exactly the right time. Think of passive CI as the smart assistant who’s constantly taking notes and chimes in when they have something important or useful to add. 

Lastly: a passive CI that works in real time means it ingests and analyzes incoming audio/video/text from a conversation and instantly pulls up insights. This is especially useful for customer support calls and e-learning, where the CI can pull up important notes about a customer or show definitions for confusing terms during a lesson. 

So, “real-time passive conversation intelligence” boils down to a software’s ability to extract meaningful data from human speech and text, while the conversation is happening. Voila. 

Where can you use it?

Most developers aren’t aware of real-time passive conversation intelligence, let alone what it can do for them. To be fair, building something that silently listens to your conversations isn’t an easy sell. But, in the right context and especially where human to human conversations are involved, passive CI can be an incredibly powerful tool for just-in-time insights, improved productivity, and effective customer interactions. 

Here are some popular use cases to get your inspiration going:

Customer support 

There’s no better source for what customers really want than the customers themselves. CI can instantly analyze customer care calls and chats, then unlock valuable insights that agents can use to deliver more efficient and relevant customer experiences. Here are a few things CI can do:

  • Use real-time contextual understanding to transcribe the conversation, highlight important topics, and even detect changes in the customer’s emotional state as they happen. 
  • Surface relevant information that the agent can use to steer the call – like if the customer has had this issue before, or their preferred language is French. 
  • Automate real-time actions, like redirecting a customer to the right agent.
  • Instantly update the CRM with useful post-call summaries about the customer to better prepare the next agent they interact with. 

Sales calls

Businesses always want to understand their customers better so they can drive more conversions. CI makes this possible by mining meaningful data and insights from every sales call, then can do useful things like:

  • Identify what phrases work best in a sales call – and which phrases to avoid. 
  • Suggest actions in real-time to help the agent guide the customer towards a sale.
  • Highlight important topics, questions, and phrases to improve sales scripts and coach new agents on best practices. 
  • Analyze sentiment, context, and word placement to measure buyer’s intent so the agent can capitalize on key moments in the conversation.
  • Automate tasks and follow-ups, like scheduling a call with a customer when their trial period ends.

Meetings

Humans aren’t all that great at multitasking, so adding a note-taking AI to the meeting lets them focus on being present in the conversation – increasing participation and engagement. With CI software, you can:

  • Identify important topics, questions, action items, and decisions that can be analyzed for internal insights or surfaced in real-time. 
  • Suggest contextual insights at exactly the right moment, like instantly pulling up the answer to someone’s question on-screen.
  • Create highly-accurate transcripts and add closed-captions during calls.
  • Automate actions and follow-ups, like sending post-meeting summaries or scheduling the next meeting on everyone’s calendars.
  • Compile useful analytics like positive and negative sentiments, silence, and talk ratios (so you know who could probably spend more time on mute).

Example of real-time passive conversation intelligence in a meeting 

For a better idea of CI’s potential, let’s take the case of a team on a video conference to discuss their latest project. As they join the call, their CI platform connects right along with them, then sits quietly in the background, ready to transcribe the conversation and record important topics, questions, and action items.

The CI logs the meeting date and time and identifies each participant on the call. It can also pull up the action items from their previous call for the team to review. If participants join the meeting late, the CI can show them an on-screen summary of what’s been discussed so far so they can catch up without interrupting the  conversation.

The CI is also capable of identifying questions in the conversation and responding in ways that add value. For example, it can quickly search the company’s knowledge base, retrieve the most relevant documentation for a question and then send it in a direct message to the person that asked it. 

When designed for contextual understanding and with access to chat and email conversations, a CI can pick up on vague references, like the phrase “this project,” and know exactly what the speaker is referring to. The CI can also catch little follow-ups that typically fall through the cracks, and then automatically assign a task to the participants involved. 

After the meeting, the CI automatically sends a post-meeting summary to all participants so they can revisit the main takeaways and their to-dos. 

This is just a glimpse at how a highly-accurate CI allows teams to put down their pens and fully focus on the conversation for better, more productive meetings.

Implementing real-time passive conversation intelligence

At this point, you have a good grasp of why passive CI is a valuable addition to any app that deals with human to human conversations. If you’re seriously thinking of implementing real-time passive CI, here a few capabilities to think about:

  • Speech recognition and contextual understanding for accurate transcriptions and closed-captioning.
  • Streaming conversations into your application for real-time insights.
  • Asynchronously updating information from the AI to your products to surface information, either for internal analysis or to act upon in the moment.
  • Capturing sentiment in real time and measuring how it changes throughout the conversation.\
  • Defining what follow-ups or recommended actions to automate and integrate into your existing workflow.

As you can imagine, implementing any of these takes a tremendous amount of time, resources, and caffeine. You’d need to fiddle with things like speaker diarization, communication protocols, and battle with all the common challenges of capturing human to human conversations. Not to mention the hassle of constantly recalibrating your CI system so it can understand conversations in different domains.

This is where APIs, like Symbl, help developers solve these problems faster. 

Symbl provides all the contextual AI capabilities and scalable infrastructure to make real-time passive CI easy to implement. With flexible APIs, SDKs, and out-of-the-box integrations, developers can quickly bring a human-level understanding of voice and text conversations across different domains – without upfront training data, wake words, or custom classifiers.

To see Symbl in action, check this sample app of Symbl for Zoom that lets you invite a CI into your Zoom meetings for real-time transcription. For more goodies, browse the Symbl demo library on GitHub to sample integrating voice intelligence into existing applications.

With a solid understanding of real-time passive CI and the help of done-for-you APIs to make it reality, you’ll find that transcribing conversations and unlocking useful insights are just the tip of the iceberg.

Additional reading

For more information on passive conversation intelligence and the tools you can use, check out these useful links:

Sign up for Symbl updates