Why would automatic text analysis help Telenor?
PO: It would give us a live dashboard to understand whether customers are angry, happy, have shared issues, and more. This could help with product development, for example, to get a live read on customers to help us market and develop the products and to react even faster and on a more personalised level. It would also help train new customer agents, giving them examples of good and bad conversations from which to learn.
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Doesn’t this technology exist already?
PO: Performance is acceptable for the English language but not for Norwegian. Partly because we have a massive amount of data in English, whereas, in Norwegian, it’s scarce. We need a great deal more if we’re going to capture the differences within the language, as people from Trondheim and Oslo have a completely different way of pronouncing some words. Nevertheless, data is not enough when you deal with spontaneous, dialectical speech with multiple participants. This research area is evolving rapidly, and we need new methods to handle real-life situations.
Are there applications for this technology beyond Telenor customer service?
PO: This can have so much impact on a societal level, which is why we applied for a grant with the Research Council of Norway (RCN) and have partnered with NTNU, NRK, and the National Library. Taking this to a broader level helped secure the funding, as the RCN doesn’t fund Telenor customer service projects.