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A company is building a contact center application and wants insights from customer conversations. Which solution meets these requirements?

Build a conversational chatbot by using Amazon Lex

Transcribe call recordings by using Amazon Transcribe

The solution to transcribe call recordings using Amazon Transcribe is particularly effective for this scenario because it directly addresses the need for insights from customer conversations. Amazon Transcribe is a speech recognition service that automatically converts spoken language into written text. By transcribing call recordings, the company can analyze the text for trends, customer sentiment, and key discussion points, effectively gaining deeper insights into customer interactions.

Transcription serves as a foundational step in understanding customer conversations, enabling further analysis through additional AWS services, like Amazon Comprehend, for natural language understanding, or other analytics tools for measuring performance and improving customer service.

While building a conversational chatbot with Amazon Lex would facilitate interaction with customers through automated responses, it doesn't provide insights from existing conversations. Extracting information from call recordings using Amazon SageMaker Model Monitor is not the primary purpose of this service, as it focuses more on monitoring machine learning models rather than analyzing speech data. Lastly, creating classification labels with Amazon Comprehend is valuable for text analysis but might follow the transcription process, as it would rely on having the conversation content in a text format first. Thus, using Amazon Transcribe is the most appropriate choice to directly obtain insights from customer conversations.

Extract information from call recordings by using Amazon SageMaker Model Monitor

Create classification labels by using Amazon Comprehend

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