Remove Engineering Remove Metrics Remove SaaS
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20 Call Center Pros Share the Most Undervalued Call Center Metrics and How To Better Leverage Them

Callminer

From essentials like average handle time to broader metrics such as call center service levels , there are dozens of metrics that call center leaders and QA teams must stay on top of, and they all provide visibility into some aspect of performance. Kaye Chapman @kayejchapman. First contact resolution (FCR) measures might be…”.

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How to scale machine learning inference for multi-tenant SaaS use cases

AWS Machine Learning

Staff Machine Learning Engineer at Zendesk. Zendesk is a SaaS company that builds support, sales, and customer engagement software for everyone, with simplicity as the foundation. Customers like Zendesk have built successful, high-scale software as a service (SaaS) businesses on Amazon Web Services (AWS).

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Build a video insights and summarization engine using generative AI with Amazon Bedrock

AWS Machine Learning

This post presents a solution where you can upload a recording of your meeting (a feature available in most modern digital communication services such as Amazon Chime ) to a centralized video insights and summarization engine. All of this data is centralized and can be used to improve metrics in scenarios such as sales or call centers.

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Unleashing the power of generative AI: Verisk’s journey to an Instant Insight Engine for enhanced customer support

AWS Machine Learning

The software as a service (SaaS) platform offers out-of-the-box solutions for life, annuity, employee benefits, and institutional annuity providers. Verisk has embraced this technology and has developed their own Instant Insight Engine, or AI companion, that provides an enhanced self-service capability to their FAST platform.

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Key SaaS Metrics that Matter

CSM Practice

With the value of the software-as-a-service (SaaS) market estimated to hit $225 billion in the US by 2025 , the industry offers lucrative opportunities for tech entrepreneurs. However, launching a SaaS company can be risky. for SaaS companies, compared to over 10% for paid advertising clicks.

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How Vericast optimized feature engineering using Amazon SageMaker Processing

AWS Machine Learning

One aspect of this data preparation is feature engineering. Feature engineering refers to the process where relevant variables are identified, selected, and manipulated to transform the raw data into more useful and usable forms for use with the ML algorithm used to train a model and perform inference against it.

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How will the economic downturn affect Customer Success? Learn from three SaaS CEOs.

ChurnZero

Discover the lessons these three SaaS CEOs have learned from leading customer-centric businesses through an economic slump, and how you can apply them. A SaaS business’s greatest source of intel is direct customer feedback. The SaaS mindset of “top-line growth over everything” is changing. .

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