Unlock the Hidden Value of Transcripts and Voice Data

We delve into how developers can improve their automatic transcriptions to draw even more value from each conversation

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Unlock the Hidden Value of Transcripts and Voice Data
Contact CentreInsights

Published: December 7, 2023

Robbie Pleasant

It’s no secret that AI can bring tremendous value to businesses and their contact centers. Organizations of all shapes and sizes are working AI into their solutions and business processes and perhaps no feature is more ubiquitous than automatic transcriptions. AI-powered tools built using speech recognition and natural language processing can create accurate transcripts of calls and meetings asynchronously and in real time, but what comes next?

Each transcript is a figurative treasure trove of information that can help businesses, but AI solutions that only include transcription tools are not enough to help users unlock the most value from their voice data. Transcribing conversations is insufficient. Users also need to understand the meaning behind each conversation and conversations at scale.

With that in mind, let’s look at how developers can improve their automatic transcriptions to draw even more value from each conversation.

Enable Users to Extract Insights

One major benefit of AI-powered tools is the ability to search large quantities of data and create reports from the data. This allows users to ask questions about transcripts and contact center records in simple, natural language, and quickly receive accurate answers.

We can see this in action with LeMUR (which stands for “Leveraging Large Language Models to Understand Recognized Speech”), a framework for applying Large Language Models (LLMs) to speech data from AssemblyAI that developers can integrate into their AI-powered platforms. Users can prompt LeMUR to understand and pull data from conversations, and then give feedback and extract information, insights, summaries, and more, from the transcripts.

For instance, LeMUR’s Question and Answer feature enables users to ask sophisticated questions like “What was the most common issue customers called about today?” LeMUR can then search through every recorded conversation, analyze the data, and provide accurate answers, complete with citations.

Features like this allow businesses to find the information they need from hours of conversations, without needing to manually search through transcripts. LeMUR’s Question and Answer feature  provides instant, actionable insights into everything from customer history to call statistics (for both individual calls and bulk conversations for wider trends), which adds even more value to the calls.

Improve and Advance Outputs with Questions and Action Items

Transcripts are filled with important questions, agreements, and decisions, but searching through them manually can be time-consuming and unreliable. AI-generated transcription tools need to do more than just copy the conversations word-for-word—they need to also identify and capture key points from each conversation.

Adding the ability to capture action items, important questions, and other conversational topics can bring immense value to an AI-powered transcription tool. This capitalizes on the AI’s understanding of language to let it instantly identify everything from agreements made during a meeting to tasks contact center agents will need to address after a call. So if, for instance, a customer has a question the agent needs to look up, the question will be noted and saved for easy reference.

With the right AI model, the system can capture these items, summarize each one, and sort them by person. For example, LeMUR can be added to an AI platform so it can easily recap action items, mark the assignee of a task, and note which project or department they fall under.  This ensures each task is given to the right person with clear instructions and goals.

Consider Sentiment Analysis

How do organizations know how their customers are feeling? In the past, they had to rely on post-call surveys, which only a small percentage of customers would answer. Now, by adding AI with natural language processing to contact center applications, it’s easier than ever to understand customer sentiment from 100% of calls.

Using sentiment analysis features enables organizations to understand and support their customers better. These use either keywords from transcripts or the customer’s tone during the call (depending on how the application is built) to identify positive and negative sentiment, which helps contact center agents and supervisors know when they’re providing great assistance, where supervisors need to provide more guidance or training, and what customers are happy or frustrated about.

Integrate Advanced AI Technology

Every call and conference can provide organizations with helpful insights and data, but we’ve only recently been able to see its full value thanks to AI with natural language processing. Adding a framework like LeMUR to an AI platform helps organizations unlock the value from those conversations, providing instant access to action items, conversation analytics, transcript quotes, and more.

“Our customers have been very excited by the extra analytical capabilities a framework like LeMUR unlocks for them, especially since they can customize the endpoint to fit the specific needs of their platform,” explains Prachie Banthia, VP of Product at AssemblyAI.

So, the next time you look at a call transcript, think about all the useful insights and data hidden in that conversation. Then know that with the right technology, it doesn’t have to be hidden any more. To see this AI in action, try the AssemblyAI playground to test LeMUR’s abilities for analyzing phone calls.

Artificial IntelligenceCall RecordingConversational AI

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