A transformative solution We worked with the client to categorize the various intents behind the customer calls, identifying sixteen categories that would help model for automated responses. The next stage of the process involved using a large dataset of nearly 12,200 recorded conversations to train and test the model that we developed.
After taking these steps, the model was designed using machine learning and NLP neural network-based techniques, before undergoing several iterations to achieve greater accuracy.
We boiled down customer intents down to 9 categories to model for automated responses
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