Tuesday, June 18, 2019

Case Study: Machine Learning Drives Support Ticket Prioritization



Key Challenges

   Respond to 30 complex support tickets daily.
   Prioritize urgent tickets from key stakeholders automatically.
   Automatically forward support tickets to the correct team member.

Timely Support Requires Prioritization

User support is a key DataOps function. Large data systems generate hundreds of support requests per month. Teams typically respond to support requests in the order received. But adhering to the order received risks losing urgent requests in the ticket backlog. To ensure that teams address urgent requests quickly, they must implement prioritization systems.

15,000 users depended on our client’s reporting infrastructure for business metrics every day. To support the users, our nine-member team tracked up to 30 complex support tickets per day. The support tickets ranged from feature requests to urgent data needs. Answering the tickets in the order received wouldn’t work. The team couldn’t risk ignoring urgent requests or overlooking tickets from key stakeholders. The DataOps team needed a system to automatically prioritize the tickets. The system also needed to analyze content, locate similar but previously resolved tickets, and categorize tickets.

Instant Analysis with Azure Databricks

The first version of the support ticket prioritization system used on-premise servers. The system improved prioritization but required manual intervention for processing. To improve efficiency, the team began developing a cloud-based solution.

Azure Databricks was the key component of the cloud-based prioritization solution. Azure Databricks allowed the team to run machine learning models directly on cloud-based data. The system starts with emails that users send to a support alias. Every five minutes, the system collates and processes the emails through Azure Data Factory. Azure Databricks notebooks then run an AI model that analyzes the support tickets. The team trained the AI model for one month using responses from a 9,000-ticket database. The AI model compares the content of each ticket to previous tickets, identifying high priority topics and users. The model recommends the best support team to answer each ticket, categorizing the ticket into an issue and sub-issue. The system also pairs each support request with previously resolved reference tickets. Finally, a Power BI report displays the ticket priorities, categories, sub-categories, and reference tickets.

Fast Responses, Increased Satisfaction

The cloud-based ticket prioritization system is extremely efficient. Previously, the DataOps team required two and a half days to resolve a ticket. Now, tickets are resolved within one and a half days, despite the user base increasing by 50%. The prioritization system also improved training efficiency. Newly onboarded DataOps team members refer to the reference tickets to create accurate responses.

The quick ticket response time increased user satisfaction. Before the team implemented the system, there were significant backlogs. Now, tickets are categorized and prioritized within five minutes of creation. There is a minimal backlog with only 10 to 15 open tickets at a time. The DataOps team plans to incorporate the ticket prioritization system into future DataOps projects. As the support team lead states, “The ticket prioritization system gets tickets to the right people, with the right priority, in the right order. It will greatly benefit our clients for a long time.”