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10 Key Metrics and KPI’s for Contact Centre Performance

Call Design

Understanding how to make a profit on the double bottom line (DBL) involves employing a broad range of KPIs and key metrics to ensure a contact centre meets every need that a business may have in supporting their customers. of the 380 contact centre professionals they asked thought customer satisfaction was one of the most important metrics.

Metrics 148
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Best Practices for Auditing Calls to Maintain High QA Standards

TeleDirect

Call auditing helps ensure that customer interactions meet established quality benchmarks while identifying areas for improvement. Conduct Calibration Sessions for Accuracy Calibration sessions ensure consistency across QA teams. Q5: What metrics are essential for call auditing?

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25 Call Center Leaders Share the Most Effective Ways to Boost Contact Center Efficiency

Callminer

Metrics, Measure, and Monitor – Make sure your metrics and associated goals are clear and concise while aligning with efficiency and effectiveness. Make each metric public and ensure everyone knows why that metric is measured. Jeff Greenfield is the co-founder and chief operating officer of C3 Metrics.

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Introducing Fortuna: A library for uncertainty quantification

AWS Machine Learning

Fortuna provides calibration methods, such as conformal prediction, that can be applied to any trained neural network to obtain calibrated uncertainty estimates. This concept is known as calibration [Guo C. Fortuna: A library for uncertainty quantification. 2020] , temperature scaling [Guo C. 2022] methods.

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Automate the machine learning model approval process with Amazon SageMaker Model Registry and Amazon SageMaker Pipelines

AWS Machine Learning

The SageMaker approval pipeline evaluates the artifacts against predefined benchmarks to determine if they meet the approval criteria. You can either have a manual approver or set up an automated approval workflow based on metrics checks in the aforementioned reports. Bias with Bias Benchmark for Question Answering (BBQ).

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Evaluate the text summarization capabilities of LLMs for enhanced decision-making on AWS

AWS Machine Learning

In this post, we explore leading approaches for evaluating summarization accuracy objectively, including ROUGE metrics, METEOR, and BERTScore. The overall goal of this post is to demystify summarization evaluation to help teams better benchmark performance on this critical capability as they seek to maximize value.

Metrics 135
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Accelerate Amazon SageMaker inference with C6i Intel-based Amazon EC2 instances

AWS Machine Learning

Refer to the appendix for instance details and benchmark data. Import intel extensions for PyTorch to help with quantization and optimization and import torch for array manipulations: import intel_extension_for_pytorch as ipex import torch Apply model calibration for 100 iterations. Refer to invoke-INT8.py py and invoke-FP32.py