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HomeArtificial Intelligence and Machine LearningEnhance customer service efficiency with AI-powered summarization using Amazon Transcribe Call Analytics

Enhance customer service efficiency with AI-powered summarization using Amazon Transcribe Call Analytics

In the fast-paced world of customer service, efficiency and accuracy are paramount. After each call, contact center agents often spend up to a third of the total call time summarizing the customer conversation. Additionally, manual summarization can lead to inconsistencies in the style and level of detail due to varying interpretations of note-taking guidelines. This post-contact work can not only add to customer wait times, but also can put pressure on some agents to avoid taking notes altogether. Supervisors also spend a considerable amount of time listening to call recordings or reading transcripts to understand the gist of a customer conversation when investigating customer issues or evaluating an agent’s performance. This can make it challenging to scale quality management within the contact center.

To address these issues, we launched a generative artificial intelligence (AI) call summarization feature in Amazon Transcribe Call Analytics. Transcribe Call Analytics is a generative AI-powered API for generating highly accurate call transcripts and extracting conversation insights to improve customer experience, agent productivity, and supervisor productivity. Powered by Amazon Bedrock, a fully managed service that offers a choice of high-performing foundation models (FMs) through a single API, generative call summarization in Transcribe Call Analytics produces call summaries that reduce the time agents spend capturing and summarizing notes after each conversation. This reduces customer wait times and improves agent productivity. Generative call summarization also provides supervisors with quick insight into a conversation without the need to listen to the entire call recording or read the entire transcript.

As Praphul Kumar, Chief Product Officer at SuccessKPI, noted,

“Generative call summarization in the Amazon Transcribe Call Analytics API has enabled us to add generative AI capabilities to our platform faster. With this feature, we are able to improve productivity in our customer’s contact center by automatically summarizing calls and removing the need for agents to write after call notes. We are looking forward to bringing this valuable capability into the hands of many more large enterprises.”

We previously published Use generative AI to increase agent productivity through automated call summarization. This new generative call summarization feature automatically integrates with multiple services and handles necessary configurations, making it simple and seamless to start using and realizing the benefits. You don’t need to manually integrate with services or perform additional configurations. Simply turn the feature on from the Amazon Transcribe console or using the start_call_analytics_job API. You can also use generative call summarization through Amazon Transcribe Post Call Analytics Solution for post-call summaries.

In this post, we show you how to use the new generative call summarization feature.

Solution overview

The following diagram illustrates the solution architecture.

You can upload a call recording in Amazon S3 and start a Transcribe Call Analytics job. The summary is generated and uploaded back to S3 along with the transcript and analytics as a single JSON.

We show you how to use the generative call summarization feature with a call sample inquiring about a used car through the following high-level steps:

Create a new Post Call Analytics job and turn on the generative call summarization feature.
Review the generative call summarization results.

Prerequisites

To get started, upload your recorded file or the sample file provided to an Amazon Simple Storage Service (Amazon S3) bucket.

Create a new Post call analytics job

Complete the following steps to create a new Post call analytics job:

On the Amazon Transcribe console, choose Post-call Analytics in the navigation pane under Amazon Transcribe Call Analytics.
Choose Create job.

For Name, enter summarysample.
In the Language settings and Model type sections, leave the default settings.

For Input file location on S3, browse to the S3 bucket containing the uploaded audio file and choose Choose.

In the Output data section, leave as default.

Create a new AWS Identity and Access Management (IAM) role named summarysamplerole that provides Amazon Transcribe service permissions to read the audio files from the S3 bucket.

In the Role permissions details section, leave as default and choose Next.

Toggle Generative call summarization on and choose Create job.

Review the transcription and summary

When the status of the job is Complete, you can review the transcription and summary by choosing the job name summarysample. The Text tab shows the Agent and Customer sentences clearly separated.

The Generative call summarization tab provides a concise summary of the call.

Choose Download transcript for the JSON output containing the transcript and summary.

Conclusion

The world of customer service is constantly evolving, and organizations must adapt to meet the growing demands of their clients. Amazon Transcribe Call Analytics introduces an innovative solution to streamline the post-call process and enhance productivity. With generative call summarization, contact center agents can devote more time to engage with customers, and supervisors can gain insights quickly without extensive call reviews. This feature improves efficiency and empowers enterprises to scale their quality management efforts, enabling them to deliver exceptional customer experiences.

Generative call summarization in Amazon Transcribe Call Analytics is generally available today in English in US East (N. Virginia) and US West (Oregon). We invite you to share your thoughts and questions in the comments section.

Learn more:

Amazon Transcribe Call Analytics product page
Amazon Transcribe Call Analytics pricing page
Amazon Transcribe Call Analytics developer guide

About the Authors

Ami Dani is a Senior Technical Program Manager at AWS focusing on AI/ML services. During her career, she has focused on delivering transformative software development projects for the federal government and large companies in industries as diverse as advertising, entertainment, and finance. Ami has experience driving business growth, implementing innovative training programs and successfully managing complex, high-impact projects. She is a strategic problem-solver and collaborative partner, consistently delivering results that exceed expectations.

Gopikrishnan Anilkumar is a Senior Technical Product Manager on the Amazon Transcribe team. He has 10 years of product management experience across a variety of domains and is passionate about AI/ML. Outside of work, Gopikrishnan loves to travel and enjoys playing cricket.

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