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Use AI for
better customer experience with sentiment analysis
Customer service calls and chatbots are becoming automated and easier to use. The frustration that comes with poor experiences, however, can have a significant impact on customer loyalty. Deep learning models such as natural language processing (NLP) are perfect for providing insights into the user experience during a customer service interaction.
Having the customer service systems automated is great for reducing phone queues and resolving simple issues efficiently, but when the customer is faced with a major problem, most people want to talk to a human operator. The traditional solution to this would be to invest in a more sophisticated automated service or more staff. With Deep Learning, these costs can be avoided by effectively allocating the time of human employees to problems where human interaction is required.
The opportunity for deep learning
State-of-the-art NLP models are able to use the text a customer writes to a chatbot to pick up on specific things the customer mentions and identify emotions such as frustration. Customer service telephone calls may also be analyzed using speech-to-text technology to analyze what the customer is saying, combined with direct analysis of audio to understand how the customer is saying it.
How is the AI model implemented?
Based on a customer service interaction, the model performs an automatic real-time analysis of audio and/or text data. As soon as a certain threshold is reached, the customer is redirected to a human agent. Alternatively, the insights gained about the exact point of interaction can be used to analyze which parts of the interaction lead to frustration in order to improve customer service.
What can be achieved?
Deep Learning can predict a customer's mood during their interaction with customer service, which is critical to improving the customer experience. Automatic detection of when a customer should switch from an automated customer service system to a human agent is an example of what can be done with this information.
Using text or audio examples of successful and unsuccessful automated customer service interactions, the model must be trained.
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