This article is related to our recent webinar on the same topic, where we also demonstrated the automated processing of an audio recording from a call centre using AI between two callers.
Every day, hundreds to thousands of messages land in company emails, chats, social media, or voicemails. They contain frustrations, wishes, suggestions and warning signs that a customer is considering leaving for a competitor. Without systematic analysis, this information often fades into obscurity and companies miss the opportunity to respond in a timely manner.
Without a structured approach it is difficult:
The solution is automated analysis of these communications, ideally in real time, which can turn disparate text and audio into actionable data and actionable recommendations.
Text communication is stagnating, while audio channels are growing at a double-digit rate:
People simply like to talk and expect companies to not only listen to their voice, but also to understand it.
So there is a clear direction -> those who rely on CX (customer experience) grow faster and lose fewer customers.
Modern tools based on generative and large-scale language models (LLMs) provide a triple benefit:
Of course, it all starts with the audio recording. Then the recording must be processed, with a so-called Speech-to-text process, using Large Language Models (LLM). First we recognize the language(s) and then we create the transcript. In the next step, for example, sentiment and emotion analysis takes place (again using LLM and specialized models for this, such as Hume AI). In the penultimate step, we classify the topics from the conversation, extracting intentions or keywords or information that was heard during the conversation. Finally, everything is clearly visualized and integrated into business intelligence (BI) or custom reporting tools.
Above we mention a relatively basic case of call analysis. However, it is of course possible to experiment with call analysis in various ways. For example, if you process calls using real-time AI, real-time data enrichment for a specific call from your own knowledge base (order statuses, previous communications, etc.) can also enter the analysis. In the last step, you can then update your knowledge base with the newly acquired information from the call. All these steps can save your operators or HR managers additional time.
Always choose tools, models and procedures with regard to the nature of the information to be processed and the sensitivity of the data.
Automated analysis of customer communications, especially voice, is changing the game. Companies that can listen and respond in real time increase client satisfaction, reduce costs and gain an edge over the competition. Now is the time to give your customer voice an AI boost.
Need help implementing AI into your customer communications? Let us know, we'd love to help.
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Jakub Bílý
Head of Business Development
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