Aevitae’s customer service receives hundreds, if not thousands of e-mails per day. Up until recently, every new e-mail was read by an employee and manually forwarded to the relevant department. Not only was this process time-consuming, but it was also costly. The question was: can we tackle this problem smarter and more efficiently? Within six weeks, our Data Science team developed a machine learning model that automatically forwards 90% of the incoming e-mails to the right department. The AI tool can spare up to 2 FTEs per year. During this project, we assisted Aevitae with:
Developing an AI tool that automatically forwards e-mails to the right department
- Creating a Power BI dashboard that displays the numbers of forwarded e-mails and how often this process is performed correctly
Every day, up to thousands of incoming e-mails were manually forwarded to the right department. An AI tool can automate this repetitive task by learning from the historical data. For example, by looking at the e-mails that Aevitae has filtered and categorized over the past year, the AI tool can use machine learning techniques to predict which department should pick up the e-mails.
Promptly answering all of the customers’ questions was challenging, particularly during busy periods. Because all e-mails arrive in one main folder, it requires the customer service employee to drag each e-mail to the correct folder manually. This way-of-working was time-consuming and created delays in the process.
MEDIAAN IN ACTION
With our expertise in Natural Language Processing (NLP), our team developed an AI tool. The following techniques and technologies were used:
Support Vector Machine
- Microsoft PSQL Server
Microsoft Power BI
Our team also designed a Power BI dashboard that showcases the numbers of forwarded e-mails. It also displays how often this process is performed correctly and when an employee had to correct it manually. The new ability to track the number of e-mails each department receives and how it evolves over time provides Aevitae with brand new insights into their customers.
Within six weeks, our Data Science team designed, built, and trained a machine learning model that automatically forwards 90% of the incoming e-mails to the right department. The AI tool can spare up to 2 FTEs per year. Two weeks after the implementation, 77% of the incoming e-mails were forwarded to the correct folder. The customer service employees manually corrected the remaining. Due to the self-learning component of the model, the percentage will only keep getting better over time. One year after going live, the tool forwarded 100% of the e-mails, of which 88% was forwarded correctly.