Voice Intelligence

Gain actionable insight into agent performance and CSAT by analyzing verbal and non-verbal content.

  • Capture verbal and non-verbal content of every interaction to provide data for analytics and guidance
  • Gain powerful and actionable insights into agent performance and customer satisfaction
  • Leverage a data-driven model to build highly satisfying and highly profitable call paths and conversations

The leading voice and speech researchers at Spoken Labs apply powerful deep learning and big data analysis techniques to voice, speech, transcripts, and screen interactions. The advances yielded by their research provide powerful analytics and automation to make customer engagement more successful, more satisfying, and more profitable.


“We chose Spoken for its cost efficiency, but the main benefit is the analytics:
we get instant visibility into agent productivity and call dispositions, enabling us to be more efficient and to increase productivity.”

– Brian F., Director, Contact Center Operations Verengo Solar


Transcription:

Convert dual-channel voice to text in real time with extremely high accuracy, providing a basis for interaction review as well as powering leading-edge AI solutions.

Speaker Verification:

Identify speakers automatically in a fraction of the time it takes a human to recognize a voice, and validate work-from-home agent identity.

Real-Time Agent Analytics:

Gather meaningful data on interaction success and customer satisfaction from voice patterns such as silences, talk-over, and interruptions.

Caller Segmentation:

Leverage customer demographics, experience, and sentiment for optimal channel selection and call routing.

Call Summarization:

Extract key content from call transcripts to guarantee that agents are better informed and more prepared to support customers and their issues.

Path Analysis:

Identify the agent on-screen workflows that support the most productive and efficient interactions.

Universal Search:

Use keywords and patterns to find relevant information across the big data domain of customer interaction, whether in audio, images, or call metadata.

Sentiment Analysis:

Assess voice and language cues in caller interactions to help guide decisions to improve or save problem calls as they happen.