How Machine Learning Is Improving U.S. Navy Customer Support

Abstract

The U.S. Navy is successfully using natural language processing (NLP) and common machine-learning (ML) algorithms to categorize and automatically route plain text support requests at a Navy fleet support center. The algorithms enhance routine IT support tasks with automation and reduce the workload of service desk agents. The ML pipeline works in a five-step process. First, an archive of documents is created from various sources, including standard operating procedure (SOP) memos, frequently asked questions (FAQs), knowledge articles, Wikipedia articles, encyclopedia articles, previously closed support requests, and other relevant documents. Next, a library of words and phrases is generated from the archive. Then, this library is used to vectorize an incoming support request, producing a term frequency inverse document frequency (TF-IDF) vector. Following, the TF-IDF vector is used to compute similarity scores between the support request and the documents in the previously-created archive. Finally, the similarity scores are processed by support vector machine (SVM) classifiers to categorize and route the incoming support request to the correct support provider. This algorithm was deployed at a U.S. Navy customer support center as part of a pilot study, where it decreased the amount of time agents spend on tickets by 35%; the amount of time required to assign tickets by 74%; and the amount of time to close tickets by 60%. Our internal tests show that, with an error rate of 2%, a 35% reduction in ticket volume could be achieved by fully deploying these algorithms.