The Team at CallMiner
March 14, 2017
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Voice analytics are the use of a voice recognition tool to analyze and record a spoken conversation. Not only does voice analytics software translate speech to text, it can also identify speaker emotion and intent by analyzing audio patterns. This software was first leveraged for commercial purposes in the enterprise in the early 2000s. Since then, it has grown in importance with more and more companies investing in voice analytics technology. In fact, industry analysts predict that the speech and voice analytics market will be worth some $1.33 billion by 2019.
The first solutions in this space concentrated on converting speech to text which certainly had its uses. For one, it was quick to produce a transcript or a report on an agent’s call. However, analysis of the report was still a manual and time-consuming task left to team-members. Leading voice analytics solutions today go one step further and leverage speech to text or transcription technology which applies a language model to automatically piece together a full conversation and identify common, trending, and hot topics.
Voice analytics software brings with it enormous benefit. Companies in a range of industries including insurance, technology, financial services, and healthcare are leveraging this technology to generate insights into customer needs.
One business area that can really benefit from voice analytics is customer service. By using these analytics to analyze huge volumes of customer conversation data, your company can identify vital and previously overlooked company information.
Voice analytics can boost customer service and call center performance levels by automatically identifying the following insights:
There are two different approaches to voice analytics – phonetics and transcription. They both begin the same way by identifying the sounds and audio and converting them to phonemes – the basic units of communication. However a phonetics-based conversation is limited hereafter. A very long list of phonemes is created and the solution scans this extensive list for phonetic patterns for words and phrases. This technique is inefficient, can be slow, and, because there is an average of 4 phonemes per word, there is a high chance of errors.
Transcription or speech to text technology goes one step further than phonetics and applies a language model of hundreds of thousands of words to the phonetic index enabling the analytics software to automatically piece together accurate conversations using the same logic and context found in the human brain.
As you weigh up which voice analytics solution is most suitable to your business needs, you should prioritize transcription-based technology.
Voice Analytics Best Practices: There are a number of best practices you should bear in mind as you figure out the solution most suited to your company.
Further Reading:
Voice analytics is an exciting and rapidly growing area in business today. They can bring enormous benefits to your organization by improving agent performance and boosting customer satisfaction. To learn more about voice analytics click on the links below.
