This Guidance demonstrates a scalable, cost-effective approach to post-call analytics that uses AWS pre-trained artificial intelligence (AI) services. It helps you gather actionable insights by using AI to transcribe and analyze customer conversations. Through advanced natural language processing, this Guidance extracts intent, context, and sentiment cues from these transcripts, allowing you to spot emerging trends and pinpoint areas for improvement. You also gain greater visibility into agent performance, surfacing coaching opportunities to enhance customer experiences.
Architecture Diagram
Step 1
Call audio is delivered from the telephone system to an Amazon Simple Storage Service (Amazon S3) bucket.
Step 2
This event initiates the creation of an AWS Step Functions workflow, which orchestrates the entire analytics process.
Step 3
AWS pre-trained artificial intelligence (AI) services are called by the workflow at the appropriate times for speech-to-text and text analytics functions.
- Amazon Transcribe is called to convert audio call recordings into text transcripts, forming the basis for further analysis.
- Amazon Comprehend is called to perform sentiment analysis, entity recognition, and key phrase extraction on the transcribed text.
- Amazon Bedrock is called to apply advanced natural language processing tasks for deeper insights into the transcribed text.
Step 4
Transcript text and AI insights data are delivered to an Amazon S3 bucket to facilitate further analysis.
Step 5
Supervisors or agents can log in to the Guidance’s user interface to review transcripts and insights for specific.
Step 6
Business analysts can log in to Amazon QuickSight to build dashboards based upon the AI insights data, including sentiment trends, agent performance, hot topic trends, and entity insights.
Step 1
Call audio is delivered from the telephone system to an Amazon Simple Storage Service (Amazon S3) bucket.
Step 2
This event initiates the creation of an AWS Step Functions workflow, which orchestrates the entire analytics process.
Step 3
AWS pre-trained artificial intelligence (AI) services are called by the workflow at the appropriate times for speech-to-text and text analytics functions.
- Amazon Transcribe is called to convert audio call recordings into text transcripts, forming the basis for further analysis.
- Amazon Comprehend is called to perform sentiment analysis, entity recognition, and key phrase extraction on the transcribed text.
- Amazon Bedrock is called to apply advanced natural language processing tasks for deeper insights into the transcribed text.
Step 4
Transcript text and AI insights data are delivered to an Amazon S3 bucket to facilitate further analysis.
Step 5
Supervisors or agents can log in to the Guidance’s user interface to review transcripts and insights for specific.
Step 6
Business analysts can log in to Amazon QuickSight to build dashboards based upon the AI insights data, including sentiment trends, agent performance, hot topic trends, and entity insights.
Well-Architected Pillars
The AWS Well-Architected Framework helps you understand the pros and cons of the decisions you make when building systems in the cloud. The six pillars of the Framework allow you to learn architectural best practices for designing and operating reliable, secure, efficient, cost-effective, and sustainable systems. Using the AWS Well-Architected Tool, available at no charge in the AWS Management Console, you can review your workloads against these best practices by answering a set of questions for each pillar.
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.
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Operational ExcellenceRead the Operational Excellence whitepaper
Data, such as speaker sentiment analysis and how well a customer’s internal compliance rules are met, is used to identify how effective contact center agents are at handling customer calls. The same data identifies the topics and entities discussed in the call. All of this data can be visualised in QuickSight to help business analysts identify trends from a customer’s perspective and potential training needs for agents.
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SecurityRead the Security 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. The solution is entirely serverless, but 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.
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ReliabilityRead the Reliability whitepaper
The solution is entirely serverless, and each of those services (Amazon Transcribe, Amazon S3) operate using multiple Availability Zones in a resilient fashion.
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Performance EfficiencyRead the Performance Efficiency whitepaper
The solution scales 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.
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Cost OptimizationRead the Cost Optimization whitepaper
The solution only uses 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.
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SustainabilityRead the Sustainability whitepaper
By using managed services and dynamic scaling, we minimize the environmental impact of the backend services.
Implementation Resources
The sample code is a starting point. It is industry validated, prescriptive but not definitive, and a peek under the hood to help you begin.
Related Content
Amazon Transcribe Post Call Analytics
This sample code demonstrates how to provide an end-to-end solution that can process call recordings from your existing contact center.
Post call analytics for your contact center with Amazon language AI services
This post demonstrates how to use Amazon Machine Learning (ML) services to transcribe and extract insights from your contact center audio recordings at scale.
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.
References to third-party services or organizations in this Guidance do not imply an endorsement, sponsorship, or affiliation between Amazon or AWS and the third party. Guidance from AWS is a technical starting point, and you can customize your integration with third-party services when you deploy the architecture.