Speech analytics is a key capability of NICE Interaction Analytics
. It helps identify the reasons why customers call the company and what causes customer dissatisfaction. It also helps contact centers uncover training opportunities to improve agent performance.
NICE speech analytics combines several best-of-breed speech technologies to gain powerful insights into customer interactions:
A cutting edge technology that analyzes spoken interactions in real-time as they occur. It enables real-time agent guidance for next-best-action, as well as supervisor alerts for call intervention.
This powerful speech analytics technology breaks down speech into phonemes, the smallest units of language, and creates an indexed voice database. Since phonetic indexing is a fast and highly scalable technology, it can analyze 100% of call recordings to understand why customers are calling. It also enables free text search for specific words or phrases.
Transcription of calls from spoken to written words is a foundation of speech analytics. It enables text and data mining models to uncover root causes and hidden insights in frequently mentioned topics as well as the context in which these topics were mentioned.
It’s not enough to know what was said during calls, but to know who—customer or agent—said it. Speech analytics leverages speaker separation to provide valuable context for the content of customer interactions.
Certain words spoken during an interaction may indicate high levels of emotion. However, sometimes emotion can only be detected by analyzing the voice and tone of the speaker. Emotion detection is a speech analytics technology that analyzes the voice of the speaker and identifies emotion via slight variations of pitch or tone. High levels of emotion are a reliable gauge of both customer dissatisfaction and agent skills.
This useful speech analytics capability identifies portions of calls in which the customer and agent are talking simultaneously—a common indicator of customer dissatisfaction. In addition, talk-over analysis identifies periods of silence during calls that may be related to agent knowledge gaps.