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Elevating Customer Service with Contact Center Solutions

InMoment XI

In a world where customer service and support are crucial to business success, the importance of an efficient and effective contact center cannot be overstated. They serve as centralized hubs where businesses manage customer interactions. These solutions are versatile and can be tailored to meet various use cases.

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Intelligent document processing with AWS AI and Analytics services in the insurance industry: Part 2

AWS Machine Learning

In Part 1 of this series, we discussed intelligent document processing (IDP), and how IDP can accelerate claims processing use cases in the insurance industry. We discussed how we can use AWS AI services to accurately categorize claims documents along with supporting documents. Part 2: Data enrichment and insights.

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10 Best Data Entry Tools for Workflow Automation and Analytics

SurveySparrow

SurveySparrow : For Simplified Data and Advanced Analytics With SurveySparrow, collecting and organizing data is as easy as pie! Once that’s down, analyze it with the advanced analytics tool! Once that’s down, analyze it with the advanced analytics tool! Now, it’s time to look at the tools.

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Intelligent document processing with AWS AI services: Part 1

AWS Machine Learning

Organizations across industries such as healthcare, finance and lending, legal, retail, and manufacturing often have to deal with a lot of documents in their day-to-day business processes. There is limited automation available today to process and extract information from these documents.

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Intelligent document processing with AWS AI services: Part 2

AWS Machine Learning

Amazon’s intelligent document processing (IDP) helps you speed up your business decision cycles and reduce costs. Across multiple industries, customers need to process millions of documents per year in the course of their business. The following figure shows the stages that are typically part of an IDP workflow.

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Customer Journey Mapping Examples for Beginners

InMoment XI

“When a manager takes the lead to form a cohesive, customer-centric, interdepartmental team, it not only facilitates learning and accountability throughout the whole company, it can even change company culture for the better.” ” – Jessica Pfeifer, VP & General Manager, InMoment. Define Customer Segments.

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Pre-training genomic language models using AWS HealthOmics and Amazon SageMaker

AWS Machine Learning

Here, we use AWS HealthOmics storage as a convenient and cost-effective omic data store and Amazon Sagemaker as a fully managed machine learning (ML) service to train and deploy the model. It supports large-scale analysis and collaborative research through HealthOmics storage, analytics, and workflow capabilities.