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Organizing Data with AI for Better Speech Analytics Insight

Every company understands that you need to listen to customer feedback for insight into your success and failure points within your contact center. This feedback is most commonly acquired from survey response. Customer feedback from other sources such as conversations between your customers and agents can offer an even richer repository for capturing intent, action, and emotion as unsolicited feedback. Artificial intelligence (AI)-fueled speech analytics can be applied to capture dialog from the voice of the customer (VoC) and the voice of the employee (VoE) to provide real insights in real time.
 
So, you say you have captured thousands of hours of speech conversations, transcribed, and analyzed them. Now what? How can you viably organize all these words and put them into actual use?
 
Much of the value delivered by AI within a speech analytics solution comes from its ability to perform categorization with machine learning. In categorization, the words, acoustics and sentiments from an interaction are automatically tagged and analyzed to identify topics and patterns. This is the process of assigning meaning to unstructured voice communication. Categorization is critical to speech analytics because without it you have a monumental volume of transcribed words with no reasonable way to derive insight or any value from that ”big data.”
 
An AI-fueled speech analytics solution will find patterns (based on individual words, strings of words and language patterns, tone of voice, metadata, and other variables) that can be identified as categories within a voice conversation. Common category examples include “dissatisfaction,” “churn language,” or “technical issue.” Any given customer contact can be tagged as belonging to several categories.
 
Categories allow organizations to more quickly and accurately find, count, trend action, and discover intent and emotion as expressed by unstructured voice interactions. In many cases rather ambiguous statements are fortified with acoustic measures such a pace and prosody, contributing to sentiment measures. Categories deliver immediate insight that’s much stronger than the predictive value of analyzing only individual words or phrases.
 
Categorization is most effectively accomplished by reviewing interactions and mapping semantics and acoustics to the appropriate category. AI makes it practical and convenient to essentially do the same thing, but with greater speed and automation on large data sets.
 
Organizations have several options for obtaining relevant categorizations. They can develop their own by applying data science to call recordings, other interaction transcripts, and metadata. Sometimes the process is outsourced to service providers. Some customer engagement analytics solutions include pre-built categorizations and may include tools so organizations can easily customize them and create new ones.
 
The bottom line here is that the contact center calls that you’re already recording for other purposes can also be used to tune your agent performance and optimize your customer experience with data-driven evidence from speech analytics. The influence of AI within speech analytics solutions is injecting speed and efficiency, gaining insight from every call that drives action in and beyond your contact center.
 
Learn more about how artificial intelligence improves the customer experience including use cases for your contact center in our latest whitepaper here.

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