Remove Analysis Remove Analytics Remove APIs Remove Metrics
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5 Capabilities of Business Intelligence for Social Media Monitoring and Analytics

CSM Magazine

In this article, we’ll explore five key capabilities of BI that empower businesses to monitor social media conversations, analyze sentiment, conduct competitor analysis, create customized dashboards and reports, and integrate social media data with other sources for comprehensive analytics.

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Real-time analysis of customer sentiment using AWS

AWS Machine Learning

Traditionally, this data is collected via a batch process and sent to a data warehouse for storage, analysis, and reporting, and is made available to decision-makers after several hours, if not days. Use cases for real-time sentiment analysis. The Amazon Comprehend sentiment API identifies the overall sentiment for a text document.

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Facebook’s Conversion API – what marketers need to know

Infinity

So, in autumn 2021, when Facebook partnered up with Amazon and launched the Conversion API Gateway, it was a very exciting day for Facebook advertisers. When talking Facebook and data, you’re likely to come across two key models – the Conversion API Gateway and the Facebook Pixel, but what’s the difference?

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The Comprehensive Guide to NICE CXone’s Latest AI-Driven Release

Expivia

Enlighten Actions: Beyond Analytics Enlighten Actions represents a significant advancement in AI-driven analytics, providing unprecedented insights into customer interactions and agent performance. No more looking at dashboards, you prompt it and see the data you want to see.

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How to Cut Down Time on LMS Reporting

CSM Magazine

One efficient method involves automating data collection through the use of APIs (Application Programming Interfaces) or connections with systems, like Human Resource Information Systems (HRIS) or Customer Relationship Management (CRM) software. This eliminates data entry and decreases the likelihood of errors.

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

AWS Machine Learning

We also look into how to further use the extracted structured information from claims data to get insights using AWS Analytics and visualization services. We highlight on how extracted structured data from IDP can help against fraudulent claims using AWS Analytics services. Amazon Redshift is another service in the Analytics stack.

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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning

Query training results: This step calls the Lambda function to fetch the metrics of the completed training job from the earlier model training step. RMSE threshold: This step verifies the trained model metric (RMSE) against a defined threshold to decide whether to proceed towards endpoint deployment or reject this model.