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In business, what is said can speak volumes.

Not just the words that are uttered: when, how and by whom are also insights of high value.

When analyzed and understood, they have the power to drive customer satisfaction levels, aid staff training and ensure legal compliance.

Indeed, the capture and retrospective curation of voice data has long been a thing, but software implementation has, to date, taken months and delivered only the most rudimentary intelligence.

However, todays smarter enterprises are now benefitting from altogether more sophisticated solutions that are fast and feature-rich.

Amazon Chime the all-in-one-place meet, chat and call platform for business has just transformed voice communications with its Amazon Chime SDK Call Analytics feature that makes it simpler for communication builders and developers to add that functionality into their app and website workloads.

Users benefit from real-time transcription, call categorization, post-call summary, speaker search, and tone-based sentiment via pre-built integrations with Amazon Transcribe and Amazon Transcribe Call Analytics, and natively through the Amazon Chime SDK voice analytics capability.

Insights can be consumed in both real-time and following completion of a call by accessing a data lake. Users can then use pre-built dashboards in Amazon QuickSight or the data visualization tool of their choice to help interpret information and implement learnings.

Voice remains a hugely important part of any organizations suite of communication channels and is capable of so much more than simply facilitating a conversation, says Sid Rao, GM of Amazon Chime SDK.

It generates valuable data which, when processed by call analytics, can contribute greatly to the effectiveness and efficiency of enterprises processes and workflows.

Machine Learning-based call analytics are particularly helpful for companies processing large volumes of call data to monitor customer satisfaction, improve staff training or stay compliant, but implementing such solutions can often take months.

The new call analytics features from Amazon Chime SDK reduces deployment time to a few days.

The insights and call recordings can be used across a variety of use cases such as financial services, insurance, mortgage advisory, expert consultation, and remote troubleshooting for products.

Customers can use the launched feature to improve customer experience, increase efficiency of experts such as wealth management advisors, and reduce compliance costs.

For example, banks can use Amazon Chime SDK call analytics to record and transcribe trader conversations for compliance purposes, generate real-time transcription, and perform speaker attribution using the speaker search feature.

Amazon Chime SDK customer IPC is a leading provider of secure, compliant communications and multi-cloud connectivity solutions for the global financial markets.

Tim Carmody, IPC CTO, said: In our industry, transcribing and recording trader calls is required for regulatory compliance. With all that recorded call data, machine learning is ideal to monitor calls for compliance and acquire better insights about the trades that are occurring.

Optional integration of Amazon Chime SDKs call analytics feature into call flows helps our customers compliance teams to securely monitor and automatically flag trades for non-compliance in real-time, as well as gather new trader insights from call data. Working with AWS, IPC was able to execute this quickly: where 12 months prior it would have taken over a week to implement a machine-learning-powered solution like this, Amazon Chime SDKs call analytics was deployed in just a couple days.

Businesses can also apply voice tone analysis to customer conversations to assess sentiment around products, services, or experiences.

The Chime SDK Insights console can manage integrations with AWS Machine Learning services such as Amazon Transcribe, Amazon Transcribe Call Analytics and Chime SDK voice insights, including speaker search and voice tone analysis.

Speaker search uses machine learning to take a 10 second voice sample from call audio and returns a set of closest matches from a database of voiceprints.

Voice tone analysis uses Machine Learning to extract sentiment from a speech signal based on a joint analysis of linguistic information (what was said) as well as tonal information (how it was said).

Real-time alerts can be triggered by events such as poor caller sentiment, or key words spoken during a call.

All in all, its a powerful tool capable of raising the value of voice data to great new heights.

Now THATS what were talking about..!

To learn more about how Amazon Chime SDK can help your business digitize and thrive, visit Amazon Chime SDK.

Read more here:
Voice Data: New Machine Learning Smarts are Powering Fast and Feature-Rich Analysis - UC Today