Email Classification Engine

By August 9, 2019 Case Study

Indian financial services company

Problem Statement

An Indian financial services firm gets close to 1.2 million support emails per month.
They outsourced email categorization to a 50-member support team. Each support
member had to read 2.27 emails per minute and classify them in the right bucket.
Customer faced several issues including 24-hour SLA, team attrition and the
classification accuracy was less than 80%.

Proposed Solution

Powerup built an email classification engine using a combination of several
algorithms & techniques including a bag of words, stemming, lemmatization, decision
forest, neural networks and more. The machine learning was able to achieve above
80% accuracy on a consistent basis.


Technologies used

Spark, Python, SQL Server.

Leave a Reply