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Live Chat Scripts for Sales and Customer Service

ProProfs Blog

What makes live chat scripts so important for sales and customer service? To realize all the benefits of live chat scripts, you need to understand the importance of chat etiquette for your customers’ experience and satisfaction. Useful Customer Service Scripts Templates And Examples. Customer Service Greetings Scripts.

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Here’s How to Write Effective Call Center Scripts

Quality Contact Solutions

After writing over one thousand call center scripts, we know that there isn’t a single stand-alone ingredient we’d consider the ‘secret sauce’ for creating the perfect script. Instead, scripts are purposeful and serve as a guide to accomplish the objective of the call. No, it doesn’t.

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Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access

AWS Machine Learning

SageMaker Feature Store now makes it effortless to share, discover, and access feature groups across AWS accounts. With this launch, account owners can grant access to select feature groups by other accounts using AWS Resource Access Manager (AWS RAM).

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25 Conflict Resolution Strategies for Customer Service

Callminer

Set time limit and end the interaction, when the customer refuses to act constructively. ” – Gregory Ciotti, Go-To Scripts for Handling 10 Tricky Customer Service Scenarios , Help Scout; Twitter: @helpscout. Account for customers’ biases and try to adapt to their communication style.

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International Contact Centre Operations Tips & Best Practices

Callminer

Encourage agents to cheer up callers with more flexible scripting. “A 2014 survey suggested that 69% of customers feel that their call center experience improves when the customer service agent doesn’t sound as though they are reading from a script. Minimise language barriers with better hires.

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Bring legacy machine learning code into Amazon SageMaker using AWS Step Functions

AWS Machine Learning

SageMaker runs the legacy script inside a processing container. SageMaker takes your script, copies your data from Amazon Simple Storage Service (Amazon S3), and then pulls a processing container. The SageMaker Processing job sets up your processing image using a Docker container entrypoint script. Create an S3 bucket.

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Use the AWS CDK to deploy Amazon SageMaker Studio lifecycle configurations

AWS Machine Learning

Lifecycle configurations are shell scripts triggered by Studio lifecycle events, such as starting a new Studio notebook. AWS CDK constructs are the building blocks of AWS CDK applications, representing the blueprint to define cloud architectures. AWS CDK constructs The file we want to inspect is aws_sagemaker_lifecycle.py.