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How To Set Up Excellent Call Center Calibration sessions?

NobelBiz

Calibration sessions serve this purpose for call centers. This article decodes the function and best practices for call calibration. Key Points: Call Center Calibration measures how well the call center works as a whole. You must assist the call center in ensuring the accuracy of its quality measurement procedures.

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Predict football punt and kickoff return yards with fat-tailed distribution using GluonTS

AWS Machine Learning

Data preprocessing and feature engineering First, the tracking data was filtered for just the data related to punts and kickoff returns. The data preprocessing and feature engineering was adapted from the winner of the NFL Big Data Bowl competition on Kaggle. The data distribution for punt and kickoff are different.

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

AWS Machine Learning

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. Aniruddha Kappagantu is a Software Development Engineer in the AI Platforms team at AWS. Refer to invoke-INT8.py

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Churn prediction using multimodality of text and tabular features with Amazon SageMaker Jumpstart

AWS Machine Learning

We now carry out feature engineering steps and then fit the model. The model training consists of two components: a feature engineering step that processes numerical, categorical, and text features, and a model fitting step that fits the transformed features into a Scikit-learn random forest classifier. BERT + Random Forest.

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Face-off Probability, part of NHL Edge IQ: Predicting face-off winners in real time during televised games

AWS Machine Learning

Based on 10 years of historical data, hundreds of thousands of face-offs were used to engineer over 70 features fed into the model to provide real-time probabilities. By continuously listening to NHL’s expertise and testing hypotheses, AWS’s scientists engineered over 100 features that correlate to the face-off event.

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What to do with a ‘Watermelon Customer’?

CustomerSuccessBox

To identify a watermelon customer, the metric that would help you the most is instead the Customer Intent Score. The Customer Intent Score is a metric that measures a visitor’s willingness to accomplish a conversion goal, for example- a request for further information. Based on rule engines. But what does it tell?

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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.