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Guidance for Live Call Analytics with Agent Assist on AWS

Deploy dashboards and boost key performance indicator (KPI) visibility for call center agents

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Overview

This Guidance uses Artificial Intelligence (AI) to analyze speech and conversations in near real-time to improve agent key performance indicators (KPIs) and the customer experience. Data can be visualized through dashboards on Amazon QuickSight, increasing the visibility into agent effectiveness and improving the customer experience. Machine learning is used to capture intent and context from conversations and offer intelligent search features.

How it works

These technical details feature an architecture diagram to illustrate how to effectively use this solution. The architecture diagram shows the key components and their interactions, providing an overview of the architecture's structure and functionality step-by-step.

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Well-Architected Pillars

The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.

Data, such as sentiment analysis of speakers and how well contact center agents meet a customer’s internal compliance rules, is used to identify how effective contact center agents are at handling customer calls. The same data also identifies the topics and entities discussed in the call. All of this data can be visualized in Amazon QuickSight to help business analysts identify trends from a customer’s perspective and potential training needs for agents. 

Read the Operational Excellence whitepaper 

All data is encrypted both in motion and at rest, and can use customer-controlled AWS Key Management Service (AWS KMS) keys for this encryption. Although the solution is entirely serverless, the AWS Lambda components can optionally run within a customer’s VPC, accessing external services such as Amazon Transcribe and Amazon S3 only through a customer’s approved endpoints.

Read the Security whitepaper 

The solution is entirely serverless, and each of those services (for example, Amazon Transcribe, Amazon S3) operate using multiple Availability Zones in a resilient fashion.

Read the Reliability whitepaper 

The solution scales its usage of its serverless components as it needs to, both up and down, in order to handle the concurrent processing of potentially thousands of calls or those times when there are no pending calls to process.

Read the Performance Efficiency whitepaper 

As in the Performance Efficiency pillar, the solution will only use serverless components when there is an active call audio file to process, minimizing the incurred costs as much as possible. If required, the original audio files can be archived to lower-cost long-term storage on a customer-specified schedule in order to minimize storage costs. 

Read the Cost Optimization whitepaper 

By extensively using managed services and dynamic scaling, we minimize the environmental impact of the backend services.

Read the Sustainability whitepaper 

Related Content

Live call analytics and agent assist for your contact center with Amazon language AI services

Your contact center connects your business to your community, enabling customers to order products, callers to request support, clients to make appointments, and much more. When calls go well, callers retain a positive image of your brand, and are likely to return and recommend you to others.  This post demonstrates how to use Amazon Machine Learning (ML) services to transcribe and extract insights from your contact center calls at scale. 

Learn more

Disclaimer

The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.