Published on 7|7|2020 by Lucie Kolářová & David Radosta
Peter Kesch, head of business development at SentiSquare, gave an interview to the financial magazine Fintag. He described the benefits of using machine learning-driven text analysis to obtain detailed information about customers - and what they say about their concerns, wishes, and objections.
Peter answered question that has been on the mind of many a financial advisory manager: Can a machine get more information from call records and e-mails than a financial advisor can? How does it work in practice? What are the results? How will the analysis specifically help me? Can it predict future behaviour?
SentiSquare uses machine learning to analyze customer-generated text. That way, companies unearth valuable data that they would not be able to obtain manually.
“We can help them get added value from the recordings and e-mails. Understanding the customer provides a massive competitive advantage, so why not use it? The additional investment is relatively low but the added value is huge,” says Peter Kesch.
You can read the whole interview here (in Czech): fintag.cz
SentiSquare is a technology company that deals with customer-generated text analysis. As one of the few companies uses artificial intelligence based on principles of distribution semantics, which provides many competitive advantages, such as language independence. The company was founded in 2014 as a spin-off by team of researchers at the Faculty of Applied Sciences of the University of West Bohemia in Pilsen. SentiSquare currently supplies technology to the contact centers of large companies such as T-Mobile, E.ON or Albert.
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