AWS Announces Amazon Transcribe Call Analytics for Customer Conversation Insight Extraction

Aug. 6, 2021 — In 2017, AWS launched Amazon Transcribe, an automatic speech recognition (ASR) service that makes it easy to add speech-to-text capabilities to any application. Now, AWS announces the availability of Amazon Transcribe Call Analytics, a new feature that lets you easily extract valuable insights from customer conversations with a single API call.

Each discussion with potential or existing customers is an opportunity to learn about their needs and expectations. For example, it’s important for customer service teams to figure out the main reasons why customers are calling them, and measure customer satisfaction during these calls. Likewise, salespeople try to gauge customer interest, and their reaction to a particular sales pitch.

Thus, many customers and partners would like to add call analytics capabilities in different applications, regardless of their contact center provider. They often need to analyze more than phone calls, for example web-based audio and video calls. So far, they’ve typically done this by stitching AI services and dedicated ML models together, and they’ve asked us for a simpler solution.

We got to work and built Amazon Transcribe Call Analytics, a new addition to Transcribe and a key enhancement to AWS Contact Center Intelligence. If you can’t wait to try it, feel free to jump now to the AWS console. If you’d like to learn more, read on!

Introducing Amazon Transcribe Call Analytics

Based on ASR implemented in Transcribe, Transcribe Call Analytics adds natural language processing (NLP) capabilities specifically trained on customer calls, and optimized to provide highly accurate call transcripts and actionable insights. With a simple API call, developers can now easily add call analytics to any application, and extract customer insights from conversations without having to build AI pipelines and train custom ML models.

Key features of Transcribe Call Analytics include:

• Timestamped turn-by-turn call transcription in 21 languages.
• Issue detection, which picks up the shortest set of contiguous words in a conversation turn that represents the reason why the customer is calling. This works out of the box without any configuration or training.
• Call categorization based on conversational characteristics:
  • Matching specific words and phrases,
  • Detecting non-talk time,
  • Detecting interruptions,
  • Analyzing sentiment for the customer and the agent.
• Call characteristics such as:
  • How quickly and loudly a customer or agent are speaking,
  • Detecting non-talk time,
  • Detecting interruptions.
• Redaction of sensitive data from the text transcript and the corresponding audio file.

For example, you can create rules to flag calls where customers interrupt the agent, exhibit negative sentiment, and say “I want to speak with the manager”. These calls certainly did not go well, and are worth analyzing in detail! You can also look for calls where agents don’t use pre-defined greetings (“Welcome to ACME Support, how can I help you today?”) within the first 15 seconds, to measure script compliance and help supervisors identify agent coaching opportunities. Another popular scenario is to create rules that flag mentions of your specific products and services (“Your ACME Turbo 2000 vacuum cleaner isn’t working like it should”), in order to pick up any emerging trends you’d need to be aware of.

Last but not least, you can further process the text transcript with other AI services such as Amazon Translate, or with custom NLP models built with Amazon SageMaker.

Click here for the full announcement.

Source: AWS