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Email Classification and Routing Using Machine Learning

EasyChair Preprint no. 12557

4 pagesDate: March 18, 2024


Email has been the primary form of communication for businesses and their customers for a long time now. Be it B2B or B2C, companies rely heavily on email form of communication for internal as well as customer facing areas. A large company might receive thousands of emails per day for a vast range of customer queries such as updates and complaints. Each of these queries needs to be addressed by a specific department like Sales, HR, Marketing of the concerned company. For the ease of customer, companies tend to have a single customer facing email such as This tends to make customer’s. work very easy as they can write up all their enquiries at a single email but managing this email is a cumbersome process for a company as an employee would have to manually look into each and every email and forward it to the relevant department. This part becomes a bottleneck in customer service as the rate of customer queries answered is limited to how many emails can be routed to concerned department. In a world full of Automation, this is still one of the big issues that has to be currently handled manually. In our project, we aim to automate this process by checking the content of the incoming email using a supervised machine learning model and classify the query for which department it belongs such as Sales, HR, Marketing etc. and then automatically routes the incoming emails to their concerned department. All without any human intervention. Such a system could easily handle the load of a large organization which might receive thousands of emails per day.

Keyphrases: Automation, Email Classification, Routing, supervised learning

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Yavnik Sharma and Darshan Thakar and Dipesh Majithia and Chaithanya Kolan},
  title = {Email Classification and Routing Using Machine Learning},
  howpublished = {EasyChair Preprint no. 12557},

  year = {EasyChair, 2024}}
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