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Transitioning off Amazon Lookout for Metrics 

AWS Machine Learning

Amazon Lookout for Metrics is a fully managed service that uses machine learning (ML) to detect anomalies in virtually any time-series business or operational metrics—such as revenue performance, purchase transactions, and customer acquisition and retention rates—with no ML experience required. To learn more, see the documentation.

Metrics 90
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Your Customer Feedback Dashboard: More Than Just a Pretty Face

Interaction Metrics

Your customer feedback dashboard is a powerful tool when used strategically. Here’s our quick guide to getting the most out of your customer feedback dashboard. When Setting Up a Customer Feedback Dashboard Of course, not all customer feedback dashboards are created equal.

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Improve LLM performance with human and AI feedback on Amazon SageMaker for Amazon Engineering

AWS Machine Learning

The team navigates a large volume of documents and locates the right information to make sure the warehouse design meets the highest standards. The Amazon D&C team implemented the solution in a pilot for Amazon engineers and collected user feedback. During the pilot, users provided 118 feedback responses.

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Ground truth curation and metric interpretation best practices for evaluating generative AI question answering using FMEval

AWS Machine Learning

This post focuses on evaluating and interpreting metrics using FMEval for question answering in a generative AI application. FMEval is a comprehensive evaluation suite from Amazon SageMaker Clarify , providing standardized implementations of metrics to assess quality and responsibility. Question Answer Fact Who is Andrew R.

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Study: The Health of the Contact Center

What does it take to engage agents in this customer-centric era? Download our study of 1,000 contact center agents in the US and UK to find out what major challenges are facing contact center agents today – and what your company can do about it.

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Three Letter Acronyms – Metrics

Education Services Group

I’m not going to waste time trying to document how to correctly (mathematically) calculate all the three letter acronyms—but feel free to check out our Customer Success Definitions, Calculations, and Lingo…Oh My! Instead, I want to do some level setting on some specific metrics and flaws I see in the industry.

Metrics 98
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Boosting RAG-based intelligent document assistants using entity extraction, SQL querying, and agents with Amazon Bedrock

AWS Machine Learning

Conversational AI has come a long way in recent years thanks to the rapid developments in generative AI, especially the performance improvements of large language models (LLMs) introduced by training techniques such as instruction fine-tuning and reinforcement learning from human feedback.

Analytics 127
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Executive Report: The Customer Data Too Often Overlooked by the C-Suite

A recent Calabrio research study of more than 1,000 C-Suite executives has revealed leaders are missing a key data stream – voice of the customer data. Download the report to learn how executives can find and use VoC data to make more informed business decisions.

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The Challenges of Omnichannel: Why so Many Contact Centers Struggle with Digital Self-Service

To find how contact centers are navigating the transition to omnichannel customer service, Calabrio surveyed more than 1,000 marketing and customer experience leaders in the U.S. about their digital customer communication strategies. Read the report to find out what was uncovered.

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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.