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2020 Call Center Metrics: 6 Key Metrics for Your Call Center Dashboard

Callminer

At the heart of most technological optimizations implemented within a successful call center are fine-tuned metrics. Keeping tabs on the right metrics can make consistent improvement notably simpler over the long term. However, not all metrics make sense for a growing call center to monitor. Peak Hour Traffic.

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Using Training Analytics to Improve Agent Retention

Vistio

As the workplace becomes more data-driven, advanced analytics is emerging as a key tool in understanding and improving agent retention. In this blog post, we’ll explore how companies can leverage analytics to not only reduce agent attrition but also foster a more engaged, successful workforce. This is where data comes in.

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How Analytics and Smart Routing Help to Maximize Inbound Call Efficiency

NobelBiz

Fortunately, contact centers can make full use of analytics and smart routing capabilities to maximize inbound call capabilities. Leverage Analytics to Track, Adapt, and Succeed The analytics coming from call centers present the necessary data that enables firms to interpret their performance and customer behavior.

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Congratulations to the 2018 LISTEN Awards Winners

Callminer

At last month’s LISTEN event, we were excited to award three customers with LISTEN Awards for their achievements in speech analytics success. The LISTEN awards were presented to customer engagement analytics users whose efforts had a direct impact on improving business results for their companies.

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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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Build generative AI applications quickly with Amazon Bedrock IDE in Amazon SageMaker Unified Studio

AWS Machine Learning

Building generative AI applications presents significant challenges for organizations: they require specialized ML expertise, complex infrastructure management, and careful orchestration of multiple services. This will provision the backend infrastructure and services that the sales analytics application will rely on.

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

AWS Machine Learning

In this post, we present a framework to customize the use of Amazon SageMaker Model Monitor for handling multi-payload inference requests for near real-time inference scenarios. 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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