Customer service automation is based on automatic tagging as a crucial step. Tagging based on identifying keywords in the text of the conversation has been the norm for a long time. To address the limitations of the keyword-based tagging of conversations, many NLP-backed solutions offer a more robust approach based on the meaning of the ticket content rather than the actual words that compose it. Such solutions use high dimensional vectors to represent words and sentences and can easily identify similarity and proximity of meaning. In more practical terms, two cases containing the utterances “I want my money back” and “I want an immediate refund” will both get tagged as a [refund request].
This kind of processing can grasp the meaning of a ticket and do tag it according. It is naturally more reliable than the automatic tagging offered by CRMs such as Zendesk.