Speech analytics is the process of extracting meaning from audio recordings and analyzing it to find relevant business intelligence. Related to audio mining, speech analytics is often performed using specialized speech analytics software that can understand the spoken word of many dialects and translate it into text.
Why is speech analytics important?
51% of consumers and a full 92% of businesses say that the phone is their preferred channel of customer/business interaction1. Obviously, call recordings are a major source of customer feedback, particularly about areas that are causing dissatisfaction for your customers.
How do you capture and process the data from your customer calls efficiently? Speech analytics software automatically parses call records so that critical business insights are not lost.
Highly regulated industries such as financial services and healthcare can particularly benefit from speech analytics, as they have compliance requirements regarding storing and searching for customer data. They also have a pressing need for the earliest possible indication of compliance violations – and people calling with complaints or questions can definitely be a red flag. Audio mining is also a key factor in the discovery process in the event of litigation.
How does Clarabridge perform speech analytics?
Clarabridge analyzes large volumes of recordings to identify key patterns and trends. First, the software transcribes call recordings. Then, using our industry-leading text and sentiment analytics engine, we uncover the customer’s emotions, feelings and sentiment.
By incorporating deep contextual awareness, emotion, and situational discovery, we allow organizations to maximize every customer interaction and to use the data to drive strategy, improve services, and ultimately deliver a satisfying customer experience.
Clarabridge’s speech analytics solution:
Simultaneously processes up to 200 streams of uncompressed audio data
Automatically transcribes any type of call recording, including service calls, phone-based market research, or after-call surveys, with the highest degree of accuracy
Applies best-in-class, enterprise-grade audio transcription algorithms for consistency and accuracy
Uses Natural Language Processing (NLP) and sentiment scoring to capture the literal voice of the customer considering nuances like emotional data, including tone of voice, phrasing and punctuation
1Bailey, Thomas, and Staples, Joe, Interactive Intelligence. (2014). Customer Service Experience Study (Wave II).