Tackling customer support through NLP

There is a high boom in consulting companies handling worldwide clients and customers with different services. Hence, there is a dire need of understanding the customer’s problems to make improvements in the company’s product or services. The company’s customer support hires many people in this regard, but customer complaints are delayed. Therefore, tackling customer support by using an automation model with the help of NLP would be a suitable idea. In this paper, we proposed an efficient methodology of an ML model trained by various algorithms using NLP support with TF-IDF vectorizer and using word2vec. We got high accuracy of 90.5% with the Multiclass Logistic Regression model. Hence, this model can run in the backend of bank applications of customer support and help in classifying the context and right category of complaint such that it can be connected to the right customer agent who can tackle the problem.