Hello everyone.
I work in customer service, and I was thinking about proactively developing an algorithm to assist in the supporting service. In general, my job focuses in a single technology, and our job is to receive “cases” and solve them. The algorithm would receive an input X which would be the case description - “having problems doing abc”, “the connection is very slow”, etc, and would provide an output Y which would be a solution or at least would narrow the possibilities, allowing the supporting team to save time. So, for each problem X, a solution Y would be provided (not to the customer, only to the supporting team), and this would be based on all the historical data the company has, which maps a problem description X to a solution Y.
This is a NLP problem, and it makes sense to have something like this because there’s a finite number of problems a specific technology has, and for a specific problem X there’s a high probability that this problem X has troubled a couple of users in the past; and instead of going through the normal troubleshooting process, it would be nice to have an algorithm that would analyze all the historical data and understand that this problem X someone presented was solved by Y in the past.
I’m still finishing the NLP course so I will probably have it a little more clear on my mind by the end of it, but just wanted to generally discuss it with you guys, a little more experienced in NLP than me, how would you tackle something like this, and if there’s a similar topic from which I can take some inspiration. I see some similarities in machine translation for example, as the input X gets mapped to a solution Y, here the solution being the translated sentenced.
Thanks!