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Conversational Analytics: Powerful Data Behind Business Transformation

InMoment XI

When you analyze the natural language interactions between customers and an organization, conversational analytics unlocks a wealth of insights that can be used to resolve issues faster, enhance agent performance, reduce costs, and demonstrate the value of customer service investments. What is Conversational Analytics?

Analytics 195
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InMoment named Leader in Forrester Wave™: Text Mining and Analytics, Q2 2024: A Comprehensive Breakdown

InMoment XI

InMoment is excited to announce its recognition as a Leader in the Forrester Wave : Text Mining and Analytics, Q2 2024. For some context , The Forrester Wave Text Mining and Analytics, Q2 2024 report is a rigorous evaluation of the top text mining and analytics providers.

Analytics 195
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Use LangChain with PySpark to process documents at massive scale with Amazon SageMaker Studio and Amazon EMR Serverless

AWS Machine Learning

This allows SageMaker Studio users to perform petabyte-scale interactive data preparation, exploration, and machine learning (ML) directly within their familiar Studio notebooks, without the need to manage the underlying compute infrastructure. Prerequisites Before you get started, complete the prerequisite steps in this section. python3.11-pip

Policies 111
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How Deltek uses Amazon Bedrock for question and answering on government solicitation documents

AWS Machine Learning

Question and answering (Q&A) using documents is a commonly used application in various use cases like customer support chatbots, legal research assistants, and healthcare advisors. In this collaboration, the AWS GenAIIC team created a RAG-based solution for Deltek to enable Q&A on single and multiple government solicitation documents.

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The Health of the Contact Center: Are You Ready for 2019?

A survey of 1,000 contact center professionals reveals what it takes to improve agent well-being in a customer-centric era. This report is a must-read for contact center leaders preparing to engage agents and improve customer experience in 2019.

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Boosting RAG-based intelligent document assistants using entity extraction, SQL querying, and agents with Amazon Bedrock

AWS Machine Learning

Such data often lacks the specialized knowledge contained in internal documents available in modern businesses, which is typically needed to get accurate answers in domains such as pharmaceutical research, financial investigation, and customer support. This task involves answering analytical reasoning questions.

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Boost post-call analytics with Amazon Q in QuickSight

AWS Machine Learning

Contact centers play a vital role in shaping customer experiences, and analyzing post-call interactions can provide valuable insights to improve agent performance, identify areas for improvement, and enhance overall customer satisfaction. The following figure shows the available roles and their capabilities.