While it might seem mundane, digitizing voice data is essential for downstream advantage. There are significant benefits in gathering, aggregating, analyzing, and leveraging appropriate actions from voice data. There are obvious benefits in customer service, sales management, and compliance. Voice data is an equal tributary in the growing data mesh that organizations are dealing with for now and the future. We are only at the beginning of leveraging voice data, so expect a big push in brand monitoring next.
With NLP, NER trains AI models to automate their understanding in the same way — in other words, it helps them think more like us.
Today, call centers using human scoring score less than 5% of volume
due to cost and technological limitations. With automated call scoring,
machine learning algorithms train using results defined by humans
(hot lead, rude agent, upset customer, etc.) to score calls instantly.
Enterprise organizations are beginning to understand the tangible value that contact center operations bring into the fold. And a majority of that value is found buried deep inside the hundreds of thousands of calls that go in and out of the call center on a daily basis. However, manually sorting through countless customer conversations for information is a costly and time consuming undertaking.
Call centers are a rich source of sensitive personal information, particularly financial information such as credit card details. They are the main channel where customer problems are addressed, and sensitive data are shared. Personal data such as credit card numbers and social security numbers hold great financial value and are often bought and sold on the dark web by cyber criminals.
There is so much data embedded in our voice such as pitch, tone, speed, volume, etc, that is more critical to understanding what is being said, than the words themselves.