Remove Analytics Remove Data Remove Scripts
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The Power of Data Analytics in Contact Centers: Driving Insights and Improving Performance

CCNG

With the advent of data analytics, these centers are not just handling customer inquiries; they are also becoming a goldmine of information that can revolutionize decision-making processes and enhance overall performance. The Impact of Data Analytics in Contact Centers: 1.

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How Dynamic Scripting Can Improve Your Agents’ Performance

Calltools

This is where dynamic scripting comes in. It customizes call scripts in real time, ensuring every single conversation is more relevant and personal. Dynamic scripting lets you cater scripts for different customers, demographics, and campaigns. What Is Dynamic Scripting? Dynamic scripting can help with all this.

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Governing the ML lifecycle at scale, Part 3: Setting up data governance at scale

AWS Machine Learning

This post dives deep into how to set up data governance at scale using Amazon DataZone for the data mesh. The data mesh is a modern approach to data management that decentralizes data ownership and treats data as a product.

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Generate training data and cost-effectively train categorical models with Amazon Bedrock

AWS Machine Learning

In this post, we explore how you can use Amazon Bedrock to generate high-quality categorical ground truth data, which is crucial for training machine learning (ML) models in a cost-sensitive environment. For the multiclass classification problem to label support case data, synthetic data generation can quickly result in overfitting.

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6 Killer Applications for Artificial Intelligence in the Customer Engagement Contact Center

If Artificial Intelligence for businesses is a red-hot topic in C-suites, AI for customer engagement and contact center customer service is white hot. This white paper covers specific areas in this domain that offer potential for transformational ROI, and a fast, zero-risk way to innovate with AI.

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Unlocking insights and enhancing customer service: Intact’s transformative AI journey with AWS

AWS Machine Learning

In this post, we demonstrate how the CQ solution used Amazon Transcribe and other AWS services to improve critical KPIs with AI-powered contact center call auditing and analytics. The goal was to refine customer service scripts, provide coaching opportunities for agents, and improve call handling processes.

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Customized model monitoring for near real-time batch inference with Amazon SageMaker

AWS Machine Learning

Examples include financial systems processing transaction data streams, recommendation engines processing user activity data, and computer vision models processing video frames. A preprocessor script is a capability of SageMaker Model Monitor to preprocess SageMaker endpoint data capture before creating metrics for model quality.

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