Amazon Transcribe Call Analytics
Amazon Transcribe Call Analytics is a machine learning (ML)-powered API for generating highly accurate call transcripts and extracting real-time conversation insights to improve customer experience and agent productivity. The API combines powerful speech-to-text and natural language processing (NLP) models that are trained specifically to understand customer service and sales calls. Developers can use Amazon Transcribe Call Analytics to generate real-time transcriptions and insights to remove note taking requirements, address detected issues, and mitigate escalations due to declining sentiment, as they happen. The API can also be used to analyze the audio files post-call.
With Amazon Transcribe Call Analytics, you get valuable intelligence such as customer and agent sentiment, call drivers, non-talk time, interruptions, sentiment, talk speed, and conversation characteristics based on specific phrases like “not happy,” “poor quality,” and “cancel my subscription.” The call categorization capability automatically tags conversations based on phrases in real time. For post-call analysis, conversations can be classified based on phrases, sentiment, non-talk time, and interruptions. Further, the API can help you detect and redact sensitive information such as names, addresses, and credit card information from both the audio and the text in real time or post-call.
Common use cases include agent assist, supervisor alerts, agent scoring, call intent tracking, and post-call analytics.
How it works
Reduce implementation complexity
Transcribe Call Analytics makes it easier to put together a pipeline of multiple AI services and create dedicated ML models. You can add Transcribe Call Analytics as a single API output to any contact center or sales call application quickly, reducing implementation time.
Gain ML-powered insights
Transcribe Call Analytics comes with natural language processing (NLP) models that are pretrained on conversational data and optimized to provide accurate call transcripts and actionable insights that can improve customer experience and agent productivity. No ML expertise is needed to build, train, and maintain these models.
Use your existing contact center
You can use Transcribe Call Analytics to analyze and gain valuable real-time and post-call insights from the contact center of your choice. As an API, Transcribe Call Analytics gives you the flexibility to add these capabilities into call applications such as customer service, sales, and more.
Improve contact center productivity with call summarization
Generate call summaries to help agents focus on providing excellent customer experiences and increase productivity post-call by automatically capturing key parts of the customer conversation (such as issues, outcomes, or action items). Managers can quickly review these summaries during a live call or after a call, without reviewing the entire transcript to understand the context of an interaction and investigate any customer issues.
Extract detailed call analytics and conversation insights
Using the power of ML, you can quickly apply speech-to-text and NLP capabilities during live calls and uncover valuable conversation insights. You can then integrate insights such as customer and agent sentiment, detected issues, and speech characteristics like non-talk time, interruptions, and talk speed into your inbound and outbound call analytics applications. This can help your supervisors more readily identify potential customer issues, agent coaching opportunities, product feedback, and call trends.
Improve compliance and monitoring with automated call categorization
Monitor your calls at scale to track compliance with company policies or regulatory requirements. Build and train your own custom categories based on your specified criteria (such as words/phrases or conversation characteristics). For example, you can set up category labels to see what percentage of calls are upsells or account cancellation.
Protect sensitive customer data
Conversations often contain sensitive customer data such as names, addresses, credit card numbers, and social security numbers. Transcribe Call Analytics helps you identify and redact this information from both the audio and the text.
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