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Proper training : Giving proper training to agents in the beginning is very important as they will learn how to resolve certain issues and have the proper knowledge of the campaign. Interactive agent scripts from Zingtree solve this problem. Agents can also send feedback directly to script authors to further improve processes.
Writing a call script is a must for contact centers that want to excel in their prospecting effort. If you write it according to the rules of the game, the script is an observable, cost-effective, and efficient method of attracting and maintaining prospects and clients. What exactly is call scripting? Why do scripts exist?
Similarly, training telco employees to handle all service requests with standard scripts and procedures is a recipe for customer and employee frustration. A means of calibrating and measuring how good – or bad – is the service you provide. Ultimately, telcos need to increase their customers’ loyalty and deepen their trust.
In this post, we show you how to build and deploy INT8 inference with your own processing container for PyTorch. Use the supplied Python scripts for quantization. Run the provided Python test scripts to invoke the SageMaker endpoint for both INT8 and FP32 versions. py scripts for testing. Refer to invoke-INT8.py
Calibrations Aren’t Just a Chore. Calibrations Aren’t Just a Chore. One of the ways in which managers can support their team is to ensure that supervisors have the time they need to undertake routine calibrations. Calibrations can be tedious, so they sometimes seem like a chore or task to be checked off.
3 Calibrate Quality Evaluations and Metrics. Central to the QM function is the evaluation of contacts for regulatory compliance, adherence to scripting and qualitative features like professionalism, product knowledge and empathy. And when the QM team “owns” quality, others across the organization know who to turn to for insights. #3
This post illustrates how to use common architecture principles to transition from a manual monitoring process to one that is automated. In a subsequent post, we walk you through how to deploy a model and implement sample compliance checks. We now explore this script in more detail.
When your focus is on how to hold people accountable, it takes your focus off an important question: “Why do we need to hold people accountable in the first place?”. Think about the evaluation, the calibration, and the coaching. And you can’t script Empathy and have it felt as genuine. Ownership over Accountability.
Clarify their roles, goals, and performance expectations so they know exactly what’s expected of them and how they can contribute during CMS test season. Train teams to recognize the signs that a call is actually a test call—for example, overly scripted language or formulaic questions. Furnish agents with informational scripts.
In this post, we demonstrate how to create this counterfactual analysis using Amazon SageMaker JumpStart solutions. Causal inference Causality is all about understanding change, but how to formalize this in statistics and machine learning (ML) is not a trivial exercise. For further details, refer to the feature extraction script.
We show you how to train, deploy and use a churn prediction model that has processed numerical, categorical, and textual features to make its prediction. For more information on specific solutions under each use case and how to launch a JumpStart solution, see Solution Templates. Solution overview.
AV/ADAS teams need to label several thousand frames from scratch, and rely on techniques like label consolidation, automatic calibration, frame selection, frame sequence interpolation, and active learning to get a single labeled dataset. This includes scripts for model loading, inference handling etc.
Take the time to write out some guidelines to get them thinking about how to communicate acknowledgement, ownership, and empathy and then practice putting these skills into action. When going through this exercise, make sure that these don’t become scripts. This will go a long way toward building and sometimes restoring customer trust.
Such pipelines support structured and systematic processes for building, calibrating, assessing, and implementing ML models, and the models themselves generate predictions and inferences. As such, an ML model is the product of an MLOps pipeline, and a pipeline is a workflow for creating one or more ML models.
It’s designed for professional use, and calibrated for high-resolution photorealistic images. In this post, we walk through how to use SDXL 1.0 Solution overview In this post, we demonstrate how to deploy SDXL 1.0 For more details on how to get started and set up SageMaker Studio, refer to Amazon SageMaker Studio.
We’ll examine how to develop clear evaluation criteria, select the right tools, and establish a regular review cycle that keeps your QA program dynamic and effective. The next section will explore the practical steps for implementing a robust QA process that delivers tangible results. Consider both objective and subjective metrics.
Schedule monthly calibrations with representatives from all of our different departments to review calls. The resulting information not only creates training opportunities but also enables refinement of scripts, improving the overall performance of the contact centre. Operations should make this a priority. Business knowledge is power.
But, how do you, manager, with heaps of other work to do, stay on top of these daily supplements for your agents? With so much to do, it’s tough to find the headspace, let alone the time, to think through how to coach your agents regularly. DO calibrate often. How many times did the agent say your customer’s name?
In this post, we show you how to build a dynamic, self-improving, and maintainable credit card fraud detection system with machine learning (ML) using Amazon SageMaker. To demonstrate how to use our models in a production system, we built a REST API with Amazon API Gateway and a Lambda function. file in the solution’s source code.
In this blog post, we’ll delve into the world of call center dialer optimization, discussing various dialing strategies, common pitfalls, and how to effectively optimize your call center operations for maximum efficiency. Gamification is one of the most effective training methods that fosters both loyalty and efficiency.
Process Automation – Intelligent call routing, intelligent scripting and unification of desktop across applications to improve agent efficiency. Improve AX - Agent-Oriented Elements. Goal: Leverage AI, smart workflow management tools and analytics to unburden agents. 6 Things Contact Centers Should do.
In this post, we explain how to run PySpark processing jobs within a pipeline. In addition, we showcase how to optimize your PySpark steps using configurations and Spark UI logs. The code samples show you how to set up and configure the ProcessingStep. The example code and deployment guide is also available on GitHub.
Good parameters are measurable and clearly defined (something you can test through calibration sessions with management, supervisors, and reps – post on this coming soon). You’ll learn how to choose the right parameters and you can snag a free call monitoring template at the bottom of that post. Script Adherence.
Right about now your head may be filled with all of those stories you’ve been told about how to do this and do that with post-call IVR surveys. The project was designed to use the same data collection method with the same survey script for both contact centers. Are you believing this? Learn more at: www.SurveyCalibration.com.
In this post, we show you how to integrate Amazon SageMaker Clarify LLM evaluation with Amazon SageMaker Pipelines to enable LLM evaluation at scale. Evaluating these models allows continuous model improvement, calibration and debugging. name: "llama2-7b-finetuned". client("s3") bucket, object_key = parse_s3_url(data_s3_path) s3.download_file(bucket,
However, enterprises looking to use AI face a major roadblock: how to safely use sensitive data. It works by injecting calibrated noise into the data generation process, making it virtually impossible to infer anything about a single data point or confidential information in the source dataset.
The steering geometry, the differentials, the lack of engineering precision of the A979, and the corresponding difficulty in calibrating it, causes gap #4. This turned into several scripts comprising all the changes to the different nodes and creating an upgraded software package that could be installed on an original AWS DeepRacer car.
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